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<!DOCTYPE html>
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<title>VITA</title>
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  <b style="color: rgb(71, 71, 71)">V</b>isual <b style="color: rgb(71, 71, 71)">I</b>nformatics Group @ University of <b style="color: rgb(71, 71, 71)">T</b>exas at <b style="color: rgb(71, 71, 71)">A</b>ustin
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<p>Our group actively publishes in the fields of machine learning, computer vision, and interdisciplinary data science. Below are a list of recent and selected papers. A mark * denotes the author to be a VITA student or Dr. Wang's mentee. An up-to-date full paper list can be found <a href="https://scholar.google.com/citations?user=pxFyKAIAAAAJ&hl=en">here</a>.</p>
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<ul>
<li>A. Velasquez, N. Bhatt*, U. Topcu, Z. Wang, K. Sycara, S. Stepputtis, S. Neemad, and G. Vallabhae<br> <b style="color:rgb(71, 71, 71)">“Neuro-symbolic AI as an Antithesis to Scaling Laws”</b><br>Proceedings of the National Academy of Sciences Nexus (PNAS Nexus), 2025. <a href="">[Paper]</a> <a href="">[Code]</a></li>
<li>Z. Yang*, X. Chen*, B. Zhu, T. Chen*, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“Deep Learning for Accurate Diagnosis of Viral Infections through scRNA-seq Analysis: A Comprehensive Benchmark Study”</b><br>Journal of Data-centric Machine Learning Research (DMLR), 2025. <a href="https://openreview.net/forum?id=wlH6Np0Um0">[Paper]</a> <a href="">[Code]</a></li>
<li>E. Oikonomou, A. Vaid, G. Holste*, A. Coppi, R. McNamara, C. Baloescu, H. Krumholz, Z. Wang, D. Apakama, G. Nadkarni, and R. Khera<br> <b style="color:rgb(71, 71, 71)">“Artificial intelligence-guided detection of under-recognized cardiomyopathies on point-of-care cardiac ultrasound: a multi-center study”</b><br>Lancet Digital Health, 2024. <a href="https://www.thelancet.com/journals/landig/article/PIIS2589-7500(24)00249-8/fulltext">[Paper]</a> <a href="">[Code]</a></li>
<li>W. Zheng*, S. Sharan*, Z. Fan*, K. Wang*, Y. Xi*, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“Symbolic Visual Reinforcement Learning: A Scalable Framework with Object-Level Abstraction and Differentiable Expression Search”</b><br>IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2024. <a href="https://ieeexplore.ieee.org/abstract/document/10694733">[Paper]</a> <a href="https://github.com/VITA-Group/DiffSES">[Code]</a></li>
<li> H. Yang*, Y. Liang, X. Guo, L. Wu, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“Pruning Before Training May Improve Generalization, Provably”</b><br> Journal of Machine Learning Research (JMLR), 2024. <a href="https://arxiv.org/abs/2301.00335">[Paper]</a> <a href="">[Code]</a></li>
<li> H. Yang*, Z. Jiang*, R. Zhang, Y. Liang, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“Neural Networks with Sparse Activation Induced by Large Bias: Tighter Analysis with Bias-Generalized NTK”</b><br> Journal of Machine Learning Research (JMLR), 2024. <a href="https://www.jmlr.org/papers/v25/23-0831.html">[Paper]</a> <a href="">[Code]</a></li>
<li> D. Xu*, Y. Yuan, M. Mardani, S. Liu, J. Song, Z. Wang, and A. Vahdat<br> <b style="color:rgb(71, 71, 71)">“AGG: Amortized Generative 3D Gaussians for Single Image to 3D”</b><br>Transactions on Machine Learning Research (TMLR), 2024. <a href="https://arxiv.org/abs/2401.04099">[Paper]</a> <a href="https://ir1d.github.io/AGG/">[Code]</a></li>
<li> G. Holste*, M. Lin, R. Zhou, F. Wang, L. Liu, Q. Yan, S. Tassel, K. Kovacs, E. Chew, Z. Lu, Z. Wang, and Y. Peng<br> <b style="color:rgb(71, 71, 71)">“Harnessing the power of longitudinal medical imaging for eye disease prognosis using Transformer-based sequence modeling”</b><br>npj Digital Medicine, 2024. <a href="https://www.nature.com/articles/s41746-024-01207-4">[Paper]</a> <a href="">[Code]</a></li>
<li> G. Holste*, Y. Zhou, S. Wang, A. Jaiswal, M. Lin, S. Zhuge, Y. Yang, D. Kim, T. Nguyen-Mau, M. Tran, J. Jeong, W. Park, J. Ryu, F. Hong, A. Verma, Y. Yamagishi, C. Kim, H. Seo, M. Kang, L. Celi, Z. Lu, R. Summers, G. Shih, Z. Wang, and Y. Peng<br> <b style="color:rgb(71, 71, 71)">“Towards Long-tailed, Multi-label Disease Classification from Chest X-ray”</b><br>Medical Image Analysis, 2024. <a href="https://www.sciencedirect.com/science/article/abs/pii/S136184152400149X?CMX_ID=&SIS_ID=&dgcid=STMJ_219742_AUTH_SERV_PA&utm_acid=216299604&utm_campaign=STMJ_219742_AUTH_SERV_PA&utm_in=DM481041&utm_medium=email&utm_source=AC_">[Paper]</a> <a href="https://bionlplab.github.io/2024_MICCAI_CXRLT/">[Code]</a></li>
<li> G. Li, D. Hoang*, K. Bhardwaj, M. Lin, Z. Wang, and R. Marculescu<br> <b style="color:rgb(71, 71, 71)">“Zero-Shot Neural Architecture Search: Challenges, Solutions, and Opportunities”</b><br> IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2024. <a href="https://arxiv.org/abs/2307.01998">[Paper]</a> <a href="https://github.com/SLDGroup/survey-zero-shot-nas">[Code]</a></li>
<li> E. Oikonomou, G. Holste*, N. Yuan, A. Coppi, R. McNamara, N. Haynes, A. Vora, E. Velazquez, F. Li, V. Menon, S. Kapadia, T. Gill, G. Nadkarni, H. Krumholz, Z. Wang, D. Ouyang, and R. Khera<br> <b style="color:rgb(71, 71, 71)">“A Multimodality Video-Based AI Biomarker for Aortic Stenosis Development and Progression”</b><br> JAMA Cardiology, 2024. <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10557799/">[Paper]</a> [Code]</li>
<li> W. Chen*, X. Gong*, J. Wu*, Y. Wei, H. Shi, Z. Yan, Y. Yang, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“Understanding and Accelerating Neural Architecture Search with Training-Free and Theory-Grounded Metrics”</b><br> IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2023. <a href="https://arxiv.org/abs/2108.11939">[Paper]</a> <a href="https://github.com/VITA-Group/TEGNAS">[Code]</a></li>
<li> Z. Jiang*, G. Zheng, Y. Cheng, A. Awadallah, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“CR-MoE: Consistent Routed Mixture-of-Experts for Scaling Contrastive Learning”</b><br>Transactions on Machine Learning Research (TMLR), 2023. <a href="https://openreview.net/forum?id=qKIvn9xL1R">[Paper]</a> <a href="https://github.com/VITA-Group/CRMoE">[Code]</a></li>
<li> M. Lin, T. Li, Y. Yang, G. Holste*, Y. Ding, S. Tassel, K. Kovacs, G. Shih, Z. Wang, Z. Lu, F. Wang, and Y. Peng<br> <b style="color:rgb(71, 71, 71)">“Improving Model Fairness in Image-based Computer-Aided Diagnosis”</b><br> Nature Communications, 2023. <a href="https://www.nature.com/articles/s41467-023-41974-4">[Paper]</a> <a href="https://zenodo.org/record/8226443">[Code]</a></li>
<li> Q. Wu*, X. Chen*, Y. Jiang*, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“Chasing Better Deep Image Priors between Over- and Under-Parameterization”</b><br>Transactions on Machine Learning Research (TMLR), 2023. <a href="https://openreview.net/forum?id=EwJJks2cSa">[Paper]</a> <a href="https://github.com/VITA-Group/Chasing-Better-DIPs">[Code]</a></li>
<li> G. Holste*, E. Oikonomou, B. Mortazavi, A. Coppi, K. Faridi, E. Miller, J. Forrest, R. McNamara, L. Ohno-Machado, N. Yuan, A. Gupta, D. Ouyang, H. Krumholz, Z. Wang, and R. Khera<br> <b style="color:rgb(71, 71, 71)">“Severe Aortic Stenosis Detection by Deep Learning Applied to Echocardiography”</b><br>European Heart Journal (EHJ), 2023. <a href="https://academic.oup.com/eurheartj/advance-article/doi/10.1093/eurheartj/ehad456/7248551">[Paper]</a> <a href="https://github.com/CarDS-Yale/echo-severe-AS">[Code]</a></li>
<li> W. Zheng*, H. Yang, J. Cai, P. Wang*, X. Jiang, S. Du, Y. Wang, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“Integrating the Traffic Science with Representation Learning for City-Wide Network Congestion Prediction”</b><br>Elsevier Information Fusion, 2023. <a href="https://www.sciencedirect.com/science/article/abs/pii/S1566253523001537">[Paper]</a> <a href="https://github.com/VITA-Group/TinT">[Code]</a></li>
<li> W. Zheng*, E. Huang, N. Rao, S. Katariya, Z. Wang, and K. Subbian<br> <b style="color:rgb(71, 71, 71)">“You Only Transfer What You Share: Intersection-Induced Graph Transfer Learning for Link Prediction”</b><br>Transactions on Machine Learning Research (TMLR), 2023. <a href="https://arxiv.org/abs/2302.14189">[Paper]</a> <a href="https://github.com/amazon-science/gnn-tail-generalization">[Code]</a></li>
<li> X. Yang, Z. Wang, S. Hu, C. Kim, S. Yu, M. Pajic, R. Manohar, Y. Chen, and H. Li<br> <b style="color:rgb(71, 71, 71)">“Neuro-Symbolic Computing: Advancements and Challenges in Hardware-Software Co-Design”</b><br>IEEE Transactions on Circuits and Systems II (TCAS-II), 2023. <a href="https://ieeexplore.ieee.org/document/10327770">[Paper]</a> <a href="">[Code]</a></li>
<li> Z. Li*, T. Chen*, L. Li, B. Li, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“Can Pruning Improve Certified Robustness of Neural Networks?”</b><br>Transactions on Machine Learning Research (TMLR), 2023. <a href="https://openreview.net/forum?id=6IFi2soduD">[Paper]</a> <a href="https://github.com/VITA-Group/CertifiedPruning">[Code]</a></li>
<li>H. Wang*, J. Hong, J. Zhou, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“How Robust is Your Fairness? Evaluating and Sustaining Fairness under Unseen Distribution Shifts”</b><br>Transactions on Machine Learning Research (TMLR), 2023. <a href="https://openreview.net/forum?id=11pGlecTz2">[Paper]</a> <a href="">[Code]</a></li>
<li>P. Narayanan, X. Hu, Z. Wu*, M. Thielke, J. Rogers, A. Harrison, J. D’Agostino, J. Brown, L. Quang, J. Uplinger, H. Kwon, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“A Multi-Purpose Real Haze Benchmark with Quantifiable Haze Levels and Ground Truth”</b><br>IEEE Transactions on Image Processing (TIP), 2023. <a href="https://arxiv.org/abs/2206.06427">[Paper]</a> <a href="https://a2i2-archangel.vision/">[Code]</a></li>
<li>T. Chen*, Z. Zhang*, J. Wu, R. Huang, S. Liu, S. Chang, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“Can You Win Everything with A Lottery Ticket?”</b><br> Transactions on Machine Learning Research (TMLR), 2022. <a href="https://openreview.net/forum?id=JL6MU9XFzW">[Paper]</a> <a href="https://github.com/VITA-Group/LTH-Pass">[Code]</a></li>
<li>Y. Han*, G. Holste*, Y. Ding, A. Tewfik, Y. Peng, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“Radiomics-Guided Global-Local Transformer for Weakly Supervised Pathology Localization in Chest X-Rays”</b><br> IEEE Transactions on Medical Imaging (TMI), 2022. <a href="https://ieeexplore.ieee.org/document/9930800">[Paper]</a> <a href="https://github.com/VITA-Group/CheXT">[Code]</a></li>
<li>T. Chen*, Y. Cheng, Z. Gan, J. Wang, L. Wang, J. Liu, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“Adversarial Feature Augmentation and Normalization for Visual Recognition”</b><br> Transactions on Machine Learning Research (TMLR), 2022. <a href="https://openreview.net/forum?id=2VEUIq9Yff">[Paper]</a> <a href="https://github.com/VITA-Group/CV_A-FAN">[Code]</a></li>
<li>S. Mohseni, H. Wang*, Z. Yu, C. Xiao, Z. Wang, J. Yadawa<br> <b style="color:rgb(71, 71, 71)">“Taxonomy of Machine Learning Safety: A Survey and Primer”</b><br> ACM Computing Surveys (CSUR), 2022. <a href="https://dl.acm.org/doi/10.1145/3551385">[Paper]</a> </li>
<li>T. Chen*, S. Liu, S. Chang, L. Amini, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“Queried Unlabeled Data Improves and Robustifies Class-Incremental Learning”</b><br> Transactions on Machine Learning Research (TMLR), 2022. (Featured Certification) <a href="https://openreview.net/forum?id=oLvlPJheCD">[Paper]</a> <a href="https://github.com/VITA-Group/CIL-QUD">[Code]</a></li>
<li> (α-β) T. Chen*, X. Chen*, W. Chen*, H. Heaton, J. Liu, Z. Wang, and W. Yin<br> <b style="color:rgb(71, 71, 71)">“Learning to Optimize: A Primer and A Benchmark”</b><br> Journal of Machine Learning Research (JMLR), 2022. <a href="https://jmlr.org/papers/v23/21-0308.html">[Paper]</a> <a href="https://github.com/VITA-Group/Open-L2O">[Code]</a></li>
<li>T. Chen*, K. Zhou, K. Duan, W. Zheng*, P. Wang*, X. Hu, and Z. Wang<br> <b style="color:rgb(71, 71, 71)"> “Bag of Tricks for Training Deeper Graph Neural Networks: A Comprehensive Benchmark Study”</b><br> IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2022. <a href="https://arxiv.org/abs/2108.10521">[Paper]</a> <a href="https://github.com/VITA-Group/Deep_GCN_Benchmarking">[Code]</a></li>
<li>X. Chen*, Y. Zhao, Y. Wang, P. Xu, H. You, C. Li, Y. Fu, Y. Lin, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“SmartDeal: Re-Modeling Deep Network Weights for Efficient Inference and Training”</b><br>IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2021. <a href="https://arxiv.org/pdf/2101.01163.pdf">[Paper]</a> <a href="https://github.com/VITA-Group/SmartDeal">[Code]</a></li>
<li>T. Hu*, F. Gama, T. Chen*, W. Zheng*, Z. Wang, A. Ribeiro, and B. Sadler<br> <b style="color:rgb(71, 71, 71)">“Scalable Perception-Action-Communication Loops with Convolutional and Graph Neural Networks”</b><br>IEEE Transactions on Signal and Information Processing over Networks (TSIPN), 2021. <a href="https://arxiv.org/abs/2106.13358">[Paper]</a> <a href="https://github.com/VITA-Group/VGAI">[Code]</a></li>
<li>S. Yang*, Z. Wang, J. Jiu, and Z. Guo<br> <b style="color:rgb(71, 71, 71)">“Controllable Sketch-to-Image Translation for Robust Face Synthesis”</b><br> IEEE Transactions on Image Processing (TIP), 2021. <a href="https://ieeexplore.ieee.org/document/9583954">[Paper]</a> <a href="https://github.com/VITA-Group/DeepPS">[Code]</a></li>
<li>J. Yan, Y. Zhong, Y. Fang, Z. Wang, and K. Ma<br> <b style="color:rgb(71, 71, 71)">“Exposing Semantic Segmentation Failures via Maximum Discrepancy Competition”</b><br> International Journal of Computer Vision (IJCV), 2021. <a href="https://arxiv.org/abs/2103.00259">[Paper]</a> <a href="https://github.com/QTJiebin/MAD_Segmentation">[Code]</a></li>
<li>S. Yang*, Z. Wang, and J. Liu<br> <b style="color:rgb(71, 71, 71)">“Shape-Matching GAN++: Scale Controllable Dynamic Artistic Text Style Transfer”</b><br> IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2021. <a href="https://ieeexplore.ieee.org/document/9339900">[Paper]</a></li>
<li>Y. Jiang*, X. Gong*, D. Liu, Y. Cheng, C. Fang, X. Shen, J. Yang, P. Zhou, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“EnlightenGAN: Deep Light Enhancement without Paired Supervision”</b><br> IEEE Transactions on Image Processing (TIP), 2021. (IEEE SPS Young Author Best Paper Award, 2024) <a href="https://arxiv.org/abs/1906.06972">[Paper]</a> <a href="https://github.com/VITA-Group/EnlightenGAN">[Code]</a></li>
<li>Z. Wu*, H. Wang*, Z. Wang, H. Jin, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“Privacy-Preserving Deep Action Recognition: An Adversarial Learning Framework and A New Dataset”</b><br> IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2020. <a href="https://ieeexplore.ieee.org/abstract/document/9207852">[Paper]</a> <a href="https://github.com/VITA-Group/PA-HMDB51">[Code]</a></li>
<li> M. Karimi, D. Wu, Z. Wang, and Y. Shen<br> <b style="color:rgb(71, 71, 71)">“Explainable Deep Relational Networks for Predicting Compound-Protein Affinities and Contacts”</b><br> Journal of Chemical Information and Modeling (JCIM), 2020. <a href="https://pubs.acs.org/doi/10.1021/acs.jcim.0c00866">[Paper]</a> [Code] </li>
<li>S. Li, W. Ren, F. Wang, I. Araujo*, E. K. Tokuda*, R. Hirata, R. Cesar, Z. Wang, and X. Cao<br> <b style="color:rgb(71, 71, 71)">“A Comprehensive Benchmark Analysis of Single Image Deraining: Current Challenges and Future Perspectives”</b><br> International Journal of Computer Vision (IJCV), 2020. <a href="https://link.springer.com/article/10.1007/s11263-020-01416-w">[Paper]</a> </li>
<li>Y. Yuan*, W. Yang, W. Ren, J Liu, W. J. Scheirer, and Z. Wang, et. al.<br> <b style="color:rgb(71, 71, 71)">“Advancing Image Understanding in Poor Visibility Environments: A Collective Benchmark Study”</b><br> IEEE Transactions on Image Processing (TIP), 2020. <a href="https://ieeexplore.ieee.org/abstract/document/9049390">[Paper]</a></li>
<li>M. Karimi, D. Wu, Z. Wang and Y. Shen<br> <b style="color:rgb(71, 71, 71)">“DeepAffinity: Interpretable Deep Learning of Compound-Protein Affinity through Unified Recurrent and Convolutional Neural Networks”</b><br> Oxford Bioinformatics, 2019. <a href="https://academic.oup.com/bioinformatics/article/35/18/3329/5320555">[Paper]</a> <a href="https://github.com/Shen-Lab/DeepAffinity">[Code]</a></li>
<li>R. G. VidalMata, ... Y. Yuan*, J. Wu*, Z. Wang, ... et. al. <br> <b style="color:rgb(71, 71, 71)">“Bridging the Gap Between Computational Photography and Visual Recognition”</b><br> IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2020. <a href="https://ieeexplore.ieee.org/abstract/document/9097964">[Paper]</a><a href="https://github.com/VITA-Group/TAMU-PKU-UG2">[Code]</a></li>
<li>B. Li*, W. Ren, D. Fu, D. Tao, D. Feng, W. Zeng, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“Benchmarking Single Image Dehazing and Beyond”</b><br> IEEE Transactions on Image Processing (TIP), vol. 28, no. 1, pp. 492-505, 2019. <a href="https://ieeexplore.ieee.org/abstract/document/8451944">[Paper]</a> <a href="https://sites.google.com/site/boyilics/website-builder/reside">[Project Page]</a></li>
</ul>
</div>
</div>
<div class="section-title" style="margin-bottom: 30px">
<h2>Conference Paper</h2>
</div>
<div class="trend-entry d-flex">
<div class="trend-contents">
<ul>
<li>H. Zhu, Z. Zhang*, W. Cong*, X. Liu, S. Park, V. Chandra, B. Long, D. Pan, Z. Wang, and J. Lee<br> <b style="color:rgb(71, 71, 71)">"APOLLO: SGD-like Memory, AdamW-level Performance"</b><br>Conference on Machine Learning and Systems (MLSys), 2025. <a href="https://arxiv.org/abs/2412.05270">[Paper]</a> <a href="https://github.com/zhuhanqing/APOLLO">[Code]</a> </li>
<li>N. Bhatt*, Y. Yang, R. Siva, D. Milan, U. Topcu, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">"Know Where You’re Uncertain When Planning with Multimodal Foundation Models: A Formal Framework"</b><br>Conference on Machine Learning and Systems (MLSys), 2025. <a href="https://arxiv.org/abs/2411.01639">[Paper]</a> <a href="https://anonymous.4open.science/r/Disentangle-Uncertainty-F476/README.md">[Code]</a> </li>
<li>W. Redman, Z. Wang, A. Ingrosso, and S. Goldt<br> <b style="color:rgb(71, 71, 71)">"On How Iterative Magnitude Pruning Discovers Local Receptive Fields in Fully Connected Neural Networks”</b><br>Conference on Parsimony and Learning (CPAL), 2025. <a href="https://arxiv.org/abs/2412.06545">[Paper]</a> <a href="">[Code]</a> </li>
<li>Z. Zhang*, A. Jaiswal*, L. Yin*, S. Liu*, J. Zhao, Y. Tian, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">"Q-GaLore: Quantized GaLore with INT4 Projection and Layer-Adaptive Low-Rank Gradients”</b><br>Conference on Parsimony and Learning (CPAL), 2025. <a href="https://arxiv.org/abs/2407.08296">[Paper]</a> <a href="https://github.com/VITA-Group/Q-GaLore">[Code]</a> </li>
<li>P. Wang*, R. Cai*, Y. Wang*, J. Zhu*, P. Srivastava, Z. Wang, and P. Li<br> <b style="color:rgb(71, 71, 71)">"Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing”</b><br>International Conference on Learning Representations (ICLR), 2025. <a href="https://arxiv.org/abs/2501.00658">[Paper]</a> <a href="https://github.com/VITA-Group/SSM-Bottleneck">[Code] </a> </li>
<li>H. Yang*, Z. Wang, J. Lee, and Y. Liang<br> <b style="color:rgb(71, 71, 71)">"Transformers Provably Learn Two-Mixture of Linear Classification via Gradient Flow”</b><br>International Conference on Learning Representations (ICLR), 2025. <a href="https://openreview.net/forum?id=AuAj4vRPkv">[Paper]</a> <a href="">[Code] </a> </li>
<li>R. Cai*, S. Muralidharan, H. Yin, Z. Wang, J. Kautz, and P. Molchanov<br> <b style="color:rgb(71, 71, 71)">"LLaMaFlex: Many-in-one LLMs via Generalized Pruning and Weight Sharing”</b><br>International Conference on Learning Representations (ICLR), 2025. <a href="https://openreview.net/forum?id=AyC4uxx2HW">[Paper]</a> <a href="">[Code] </a> </li>
<li>Z. Zhang*, Z. Liu, Y. Tian, H. Khaitan, Z. Wang, and S. Li<br> <b style="color:rgb(71, 71, 71)">"R-Sparse: Rank-Aware Activation Sparsity for Efficient LLM Inference”</b><br>International Conference on Learning Representations (ICLR), 2025. <a href="https://openreview.net/forum?id=9VMW4iXfKt">[Paper]</a> <a href="">[Code] </a> </li>
<li>T. Huang, Z. Zhu, G. Jin, L. Liu, Z. Wang, and S. Liu*<br> <b style="color:rgb(71, 71, 71)">"SPAM: Spike-Aware Adam with Momentum Reset for Stable LLM Training”</b><br>International Conference on Learning Representations (ICLR), 2025. <a href="https://openreview.net/forum?id=L9eBxTCpQG">[Paper]</a> <a href="https://github.com/TianjinYellow/SPAM-Optimizer">[Code] </a> </li>
<li>R. Li, P. Pan, B. Yang, D. Xu*, S. Zhou, X. Zhang, Z. Li, A. Kadambi, Z. Wang, Z. Tu, Z. Fan*<br> <b style="color:rgb(71, 71, 71)">"4K4DGen: Panoramic 4D Generation at 4K Resolution”</b><br>International Conference on Learning Representations (ICLR), 2025. (Spotlight) <a href="https://openreview.net/forum?id=qxRoo7ULCo">[Paper]</a> <a href="https://4k4dgen.github.io/">[Code] </a> </li>
<li>H. Manukyan, A. Sargsyan, B. Atanyan, Z. Wang, S. Navasardyan, and H. Shi<br> <b style="color:rgb(71, 71, 71)">"ProFI-Painter: Text-Guided Prompt-Faithful Image Inpainting with Diffusion Models”</b><br>International Conference on Learning Representations (ICLR), 2025. <a href="https://openreview.net/forum?id=6lB5qtdYAg">[Paper]</a> <a href="">[Code] </a> </li>
<li>P. Wang*, Z. Fan*, D. Xu*, D. Wang, S. Mohan, F. Iandola, R. Ranjan, Y. Li, Q. liu, Z. Wang, and V. Chandra<br> <b style="color:rgb(71, 71, 71)">"SteinDreamer: Variance Reduction for Text-to-3D Score Distillation via Stein Identity”</b><br>International Conference on Artificial Intelligence and Statistics (AISTATS), 2025. <a href="https://arxiv.org/abs/2401.00604">[Paper]</a> [<a href="https://github.com/VITA-Group/SteinDreamer">[Code]</a></li>
<li>Z. Li*, T. Chen*, L. Li, B. Li, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">"Sparse Transfer Learning Accelerates and Enhances Certified Robustness”</b><br>AAAI Conference on Artificial Intelligence (AAAI), 2025. <a href="">[Paper]</a> <a href="">[Code]</a> </li>
<li>Z. Fan*, K. Wang*, K. Wen, Z. Zhu*, D. Xu*, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">"LightGaussian: Unbounded 3D Gaussian Compression with 15x Reduction and 200+ FPS"</b><br>Advances in Neural Information Processing Systems (NeurIPS), 2024. (Spotlight) <a href="https://arxiv.org/abs/2311.17245">[Paper]</a> <a href="https://lightgaussian.github.io/">[Code]</a>
<li>H. Hu*, Z. Fan*, T. Wu, Y. Xi*, S. Lee*, G. Pavlakos, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">"Expressive Gaussian Human Avatars from Monocular RGB Video"</b><br>Advances in Neural Information Processing Systems (NeurIPS), 2024. <a href="https://arxiv.org/abs/2407.03204">[Paper]</a> <a href="https://evahuman.github.io/">[Code]</a>
<li>R. Cai*, Y. Ro, G. Kim, P. Wang*, B. Bejnordi, A. Akella, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">"Read-ME: Refactorizing LLMs as Router-Decoupled Mixture of Experts with System Co-Design"</b><br>Advances in Neural Information Processing Systems (NeurIPS), 2024. <a href="https://utns.cs.utexas.edu/assets/papers/neurips24-readme.pdf">[Paper]</a> <a href="https://github.com/VITA-Group/READ-ME">[Code]</a>
<li>Z. Zhang*, R. Chen*, S. Liu*, Z. Yao, O. Ruwase, B. Chen, X. Wu, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">"Found in the Middle: How Language Models Use Long Contexts Better via Plug-and-Play Positional Encoding"</b><br>Advances in Neural Information Processing Systems (NeurIPS), 2024. <a href="https://arxiv.org/abs/2403.04797">[Paper]</a> <a href="https://github.com/VITA-Group/Ms-PoE">[Code]</a>
<li>Z. Fan*, J. Zhang, W. Cong*, P. Wang*, R. Li, K. Wen, S. Zhou, A Kadambi, Z. Wang, D. Xu, B. Ivanovic, M. Pavone, and Y. Wang<br> <b style="color:rgb(71, 71, 71)">“Large Spatial Model: End-to-end Unposed Images to Semantic 3D”</b><br>Advances in Neural Information Processing Systems (NeurIPS), 2024. <a href="https://arxiv.org/abs/2410.18956">[Paper]</a> <a href="https://largespatialmodel.github.io/">[Code] </a>
<li>H. Yang*, B. Kailkhura, Z. Wang, and Y. Liang<br> <b style="color:rgb(71, 71, 71)">“Training Dynamics of Transformers to Recognize Word Co-occurrence via Gradient Flow Analysis"</b><br>Advances in Neural Information Processing Systems (NeurIPS), 2024. <a href="https://arxiv.org/abs/2410.09605">[Paper]</a> <a href="">[Code] </a>
<li>H. Liang, Y. Yin, D. Xu*, H. Liang*, Z. Wang, K. Plataniotis, Y. Zhao, and Y. Wei<br> <b style="color:rgb(71, 71, 71)">“Diffusion4D: Fast Spatial-temporal Consistent 4D generation via Video Diffusion Models"</b><br>Advances in Neural Information Processing Systems (NeurIPS), 2024. <a href="https://arxiv.org/abs/2405.16645">[Paper]</a> <a href="https://github.com/VITA-Group/Diffusion4D">[Code] </a>
<li>H. Lu, Y. Zhou, S. Liu*, Z. Wang, M. Mahoney, and Y. Yang<br> <b style="color:rgb(71, 71, 71)">“AlphaPruning: Using Heavy-Tailed Self Regularization Theory for Improved Layer-wise Pruning of Large Language Models"</b><br>Advances in Neural Information Processing Systems (NeurIPS), 2024. <a href="https://arxiv.org/abs/2410.10912">[Paper]</a> <a href="https://github.com/haiquanlu/AlphaPruning">[Code] </a>
<li>X. Zhao, G. Sun, R. Cai*, Y. Zhou, P. Li, P. Wang*, B. Tan, Y. He, L. Chen, Y. Liang, B. Chen, B. Yuan, H. Wang, A. Li, Z. Wang, and T. Chen*<br> <b style="color:rgb(71, 71, 71)">“Model-GLUE: Democratized LLM Scaling for A Large Model Zoo in the Wild”</b><br>Advances in Neural Information Processing Systems, Track on Datasets and Benchmarks (NeurIPS D & B), 2024. <a href="https://arxiv.org/pdf/2410.05357">[Paper]</a> <a href="https://github.com/Model-GLUE/Model-GLUE">[Code] </a> </li>
<li>Z. Zhu*, Z. Fan*, Y. Jiang*, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“FSGS: Real-Time Few-shot View Synthesis using Gaussian Splatting”</b><br>European Conference on Computer Vision (ECCV), 2024. <a href="https://www.ecva.net/papers/eccv_2024/papers_ECCV/papers/05583.pdf">[Paper]</a> <a href="https://github.com/VITA-Group/FSGS">[Code] </a> </li>
<li>S. Zhou, Z. Fan*, D. Xu*, H. Chang, P. Chari, T. Bharadwaj, S. You, Z. Wang, and A. Kadambi<br> <b style="color:rgb(71, 71, 71)">“DreamScene360: Unconstrained Text-to-3D Scene Generation with Panoramic Gaussian Splatting”</b><br>European Conference on Computer Vision (ECCV), 2024. <a href="https://www.ecva.net/papers/eccv_2024/papers_ECCV/papers/00996.pdf">[Paper]</a> <a href="https://dreamscene360.github.io/">[Code] </a> </li>
<li>R. Li, Z. Fan*, B. Wang, P. Wang*, Z. Wang, and X. Wu<br> <b style="color:rgb(71, 71, 71)">“VersatileGaussian: Real-time Neural Rendering for Versatile Tasks using Gaussian Splatting”</b><br>European Conference on Computer Vision (ECCV), 2024. <a href="https://www.ecva.net/papers/eccv_2024/papers_ECCV/papers/03032.pdf">[Paper]</a> <a href="https://versatilegaussian.github.io/">[Code] </a> </li>
<li>Q. Li, J. Hong*, C. Xie, J. Tan, R. Xin, J. Hou, X. Yin, Z. Wang, D. Hendrycks, Z. Wang, B. Li, B. He, and D. Song<br> <b style="color:rgb(71, 71, 71)">“LLM-PBE: Assessing Data Privacy in Large Language Models”</b><br>International Conference on Very Large Data Bases (VLDB), 2024. (Best Paper Finalist) <a href="https://www.vldb.org/pvldb/vol17/p3201-li.pdf">[Paper]</a> <a href="https://llm-pbe.github.io/home">[Code] </a> </li>
<li>L. Sun*, N. Bhatt*, J. Liu*, Z. Fan*, Z. Wang, T. Humphreys, and U. Topcu<br> <b style="color:rgb(71, 71, 71)">“MM3DGS SLAM: Multi-modal 3D Gaussian Splatting for SLAM Using Vision, Depth, and Inertial Measurements”</b><br>IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2024. (Oral Pitch Finalist) <a href="https://arxiv.org/abs/2404.00923">[Paper]</a> <a href="https://github.com/VITA-Group/MM3DGS-SLAM">[Code] </a> </li>
<li>R. Cai*, S. Muralidharan, G. Heinrich, H. Yin, Z. Wang, J. Kautz, and P. Molchanov<br> <b style="color:rgb(71, 71, 71)">“Flextron: Many-in-One Flexible Large Language Model”</b><br>International Conference on Machine Learning (ICML), 2024. (Oral) <a href="https://openreview.net/pdf?id=9vKRhnflAs">[Paper]</a> <a href="">[Code] </a> </li>
<li>R. Cai*, Y. Tian, Z. Wang, and B. Chen<br> <b style="color:rgb(71, 71, 71)">“LoCoCo: Dropping In Convolutions for Long Context Compression”</b><br>International Conference on Machine Learning (ICML), 2024. <a href="https://arxiv.org/abs/2406.05317">[Paper]</a> <a href="https://github.com/VITA-Group/LoCoCo">[Code] </a> </li>
<li>L. Yin*, A. Jaiswal*, S. Liu*, S. Kundu, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“Junk DNA Hypothesis: Pruning Small Pre-Trained Weights Irreversibly and Monotonically Impairs Difficult Downstream Tasks in LLMs”</b><br>International Conference on Machine Learning (ICML), 2024. <a href="https://arxiv.org/abs/2310.02277">[Paper]</a> <a href="https://github.com/VITA-Group/Junk_DNA_Hypothesis">[Code] </a> </li>
<li>L. Yin*, Y. Wu, Z. Zhang*, C. Hsieh, Y. Wang, Y. Jia, G. Li, A. Jaiswal*, M. Pechenizkiy, Y. Liang, M. Bendersky, Z. Wang, and S. Liu*<br> <b style="color:rgb(71, 71, 71)">“Outlier Weighed Layerwise Sparsity (OWL): A Missing Secret Sauce for Pruning LLMs to High Sparsity”</b><br>International Conference on Machine Learning (ICML), 2024. <a href="https://arxiv.org/abs/2310.05175">[Paper]</a> <a href="https://github.com/luuyin/OWL">[Code] </a> </li>
<li>R. Chen*, T. Zhao, A. Jaiswal*, N. Shah, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“LLaGA: Large Language and Graph Assistant”</b><br>International Conference on Machine Learning (ICML), 2024. <a href="https://arxiv.org/abs/2402.08170">[Paper]</a> <a href="https://github.com/VITA-Group/LLaGA">[Code] </a> </li>
<li>J. Hong*, J. Duan, C. Zhang, Z. Li*, C. Xie, K. Lieberman, J. Diffenderfer, B. Bartoldson, A. Jaiswal*, K. Xu, B. Kailkhura, D. Hendrycks, D. Song, Z. Wang, and B. Li<br> <b style="color:rgb(71, 71, 71)">“Decoding Compressed Trust: Scrutinizing the Trustworthiness of Efficient LLMs Under Compression”</b><br>International Conference on Machine Learning (ICML), 2024. <a href="https://arxiv.org/abs/2403.15447">[Paper]</a> <a href="https://decoding-comp-trust.github.io/">[Code] </a> </li>
<li>Z. Li*, S. Liu*, T. Chen*, A. Jaiswal*, Z. Zhang*, D. Wang, R. Krishnamoorthi, S. Chang, Z. Wang<br> <b style="color:rgb(71, 71, 71)">“Sparse Cocktail: Every Sparse Pattern Every Sparse Ratio All At Once”</b><br>International Conference on Machine Learning (ICML), 2024. <a href="https://openreview.net/pdf/e34b99064ed4210ff231d4616590494ef817370b.pdf">[Paper]</a> <a href="https://github.com/VITA-Group/SparseCocktail">[Code] </a> </li>
<li>J. Zhao, Z. Zhang*, B. Chen, Z. Wang, A. Anandkumar, and Y. Tian<br> <b style="color:rgb(71, 71, 71)">“GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection”</b><br>International Conference on Machine Learning (ICML), 2024. (Oral) <a href="https://arxiv.org/abs/2403.03507">[Paper]</a> <a href="https://github.com/jiaweizzhao/GaLore">[Code] </a> </li>
<li>H. Dong, X. Yang, Z. Zhang*, Z. Wang, Y. Chi, and B. Chen<br> <b style="color:rgb(71, 71, 71)">“Get More with LESS: Synthesizing Recurrence with KV Cache Compression for Efficient LLM Inference”</b><br>International Conference on Machine Learning (ICML), 2024. <a href="https://arxiv.org/abs/2402.09398">[Paper]</a> <a href="https://github.com/hdong920/LESS">[Code] </a> </li>
<li>Y. Zhang, P. Li, J. Hong*, J. Li, Y. Zhang, W. Zheng*, P. Chen, J. Lee, W. Yin, M. Hong, Z. Wang, S. Liu, and T. Chen*<br> <b style="color:rgb(71, 71, 71)">“Revisiting Zeroth-Order Optimization for Memory-Efficient LLM Fine-Tuning: A Benchmark”</b><br>International Conference on Machine Learning (ICML), 2024. <a href="https://arxiv.org/abs/2402.11592">[Paper]</a> <a href="https://github.com/ZO-Bench/ZO-LLM">[Code] </a> </li>
<li>P. Wang*, D. Xu*, Z. Fan*, D. Wang, S. Mohan, F. Iandola, R. Ranjan, Y. Li, Q. liu, Z. Wang, and V. Chandra<br> <b style="color:rgb(71, 71, 71)">"Taming Mode Collapse in Score Distillation for Text-to-3D Generation”</b><br>IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024. <a href="https://arxiv.org/abs/2401.00909">[Paper]</a> <a href="https://vita-group.github.io/3D-Mode-Collapse/">[Code] </a> </li>
<li>M. Varma, P. Wang*, Z. Fan*, Z. Wang, H. Su, and R. Ramamoorthi<br> <b style="color:rgb(71, 71, 71)">"Lift3D: Zero-Shot Lifting of Any 2D Vision Model to 3D”</b><br>IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024. <a href="https://arxiv.org/abs/2403.18922">[Paper]</a> <a href="https://mukundvarmat.github.io/Lift3D/">[Code] </a> </li>
<li>S. Zhou, H. Chang, S. Jiang, Z. Fan*, Z. Zhu*, D. Xu*, P. Chari, S. You, Z. Wang, and A. Kadambi<br> <b style="color:rgb(71, 71, 71)">"Feature 3DGS: Supercharging 3D Gaussian Splatting to Enable Distilled Feature Fields”</b><br>IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024. (Highlight) <a href="https://arxiv.org/abs/2312.03203">[Paper]</a> <a href="https://feature-3dgs.github.io/">[Code] </a> </li>
<li>V. Goel, E. Peruzzo, Y. Jiang*, D. Xu*, X. Xu, N. Sebe, T. Darrell, Z. Wang, H. Shi<br> <b style="color:rgb(71, 71, 71)">"PAIR Diffusion: A Comprehensive Multimodal Object-Level Image Editor”</b><br>IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024. <a href="https://arxiv.org/abs/2303.17546">[Paper]</a> <a href="https://vidit98.github.io/publication/conference-paper/pair_diff.html">[Code] </a> </li>
<li>M. Ohanyan, H. Manukyan, Z. Wang, S. Navasardyan, and H. Shi<br> <b style="color:rgb(71, 71, 71)">"Zero-Painter: Training-Free Layout Control for Text-to-Image Synthesis”</b><br>IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024. <a href="https://openaccess.thecvf.com/content/CVPR2024/papers/Ohanyan_Zero-Painter_Training-Free_Layout_Control_for_Text-to-Image_Synthesis_CVPR_2024_paper.pdf">[Paper]</a> <a href="https://github.com/Picsart-AI-Research/Zero-Painter">[Code] </a> </li>
<li>X. Xu, J. Guo, Z. Wang, G. Huang, I. Essa, and H. Shi<br> <b style="color:rgb(71, 71, 71)">"Prompt-Free Diffusion: Taking 'Text' out of Text-to-Image Diffusion Models”</b><br>IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024. <a href="https://arxiv.org/abs/2305.16223">[Paper]</a> <a href="https://github.com/SHI-Labs/Prompt-Free-Diffusion">[Code] </a> </li>
<li>M. D'Incà, E. Peruzzo, M. Mancini, D. Xu*, V. Goel, X. Xu, Z. Wang, H. Shi, and N. Sebe<br> <b style="color:rgb(71, 71, 71)">"OpenBias: Open-set Bias Detection in Generative Models”</b><br>IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024. (Highlight) <a href="https://arxiv.org/abs/2404.07990">[Paper]</a> <a href="https://github.com/Picsart-AI-Research/OpenBias">[Code] </a> </li>
<li>Z. Zhang*, S. Liu*, R. Chen*, B. Kailkhura, B. Chen, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">"Q-Hitter: A Better Token Oracle for Efficient LLM Inference via Sparse-Quantized KV Cache”</b><br>Conference on Machine Learning and Systems (MLSys), 2024. <a href="https://proceedings.mlsys.org/paper_files/paper/2024/file/bbb7506579431a85861a05fff048d3e1-Paper-Conference.pdf">[Paper]</a> <a href="https://github.com/VITA-Group/Q-Hitter">[Code] </a> </li>
<li>Y. Yang, N. Bhatt*, T. Ingebrand, W. Ward, S. Carr, Z. Wang, and U. Topcu<br> <b style="color:rgb(71, 71, 71)">"Fine-Tuning Language Models Using Formal Methods Feedback”</b><br>Conference on Machine Learning and Systems (MLSys), 2024. <a href="https://proceedings.mlsys.org/paper_files/paper/2024/file/b0131b6ee02a00b03fc3320176fec8f5-Paper-Conference.pdf">[Paper]</a> <a href="">[Code] </a> </li>
<li>A. Jaiswal*, Z. Gan, X. Du, B. Zhang, Z. Wang, and Y. Yang<br> <b style="color:rgb(71, 71, 71)">"Compressing LLMs: The Truth is Rarely Pure and Never Simple”</b><br>International Conference on Learning Representations (ICLR), 2024. <a href="https://openreview.net/forum?id=B9klVS7Ddk">[Paper]</a> <a href="https://github.com/VITA-Group/llm-kick">[Code] </a> </li>
<li>J. Hong*, J. Wang, C. Zhang, Z. LI*, B. Li, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">"DP-OPT: Make Large Language Model Your Differentially-Private Prompt Engineer”</b><br>International Conference on Learning Representations (ICLR), 2024. (Spotlight) <a href="https://openreview.net/forum?id=Ifz3IgsEPX">[Paper]</a> <a href="https://github.com/VITA-Group/DP-OPT">[Code] </a> </li>
<li>Y. Jiang*, H. Tang, J. Chang, L. Song, Z. Wang, and L. Cao<br> <b style="color:rgb(71, 71, 71)">"Efficient-3DiM: Learning a Generalizable Single-image Novel-view Synthesizer in One Day”</b><br>International Conference on Learning Representations (ICLR), 2024. <a href="https://openreview.net/forum?id=3eFMnZ3N4J">[Paper]</a> <a href="">[Code] </a> </li>
<li>W. Chen*, J. Wu*, Z. Wang, and B. Hanin<br> <b style="color:rgb(71, 71, 71)">"Principled Architecture-aware Scaling of Hyperparameters”</b><br>International Conference on Learning Representations (ICLR), 2024. <a href="https://openreview.net/forum?id=HZndRcfyNI">[Paper]</a> <a href="https://github.com/VITA-Group/principled_scaling_lr_init">[Code] </a> </li>
<li>P. Wang*, S. Yang, S. Li, Z. Wang, and P. Li<br> <b style="color:rgb(71, 71, 71)">"Polynomial Width is Sufficient for Set Representation with High-dimensional Features”</b><br>International Conference on Learning Representations (ICLR), 2024. <a href="https://openreview.net/forum?id=34STseLBrQ">[Paper]</a> <a href="">[Code] </a> </li>
<li>X. Chen*, Y. Yang, Z. Wang, and B. Mirzasoleiman<br> <b style="color:rgb(71, 71, 71)">"Data Distillation Can Be Like Vodka: Distilling More Times For Better Quality”</b><br>International Conference on Learning Representations (ICLR), 2024. <a href="https://openreview.net/forum?id=1NHgmKqOzZ">[Paper]</a> <a href="https://github.com/VITA-Group/ProgressiveDD">[Code] </a> </li>
<li>Y. You*, R. Zhou, J. Park, H. Xu, C. Tian, Z. Wang, and Y. Shen<br> <b style="color:rgb(71, 71, 71)">"Latent 3D Graph Diffusion”</b><br>International Conference on Learning Representations (ICLR), 2024. <a href="https://openreview.net/forum?id=cXbnGtO0NZ">[Paper]</a> <a href="https://github.com/Shen-Lab/LDM-3DG">[Code] </a> </li>
<li>A. Isajanyan, A. Shatveryan, D. Kocharian, Z. Wang, and H. Shi<br> <b style="color:rgb(71, 71, 71)">"Social Reward: Evaluating and Enhancing Generative AI through Million-User Feedback from an Online Creative Community”</b><br>International Conference on Learning Representations (ICLR), 2024. (Spotlight) <a href="https://openreview.net/forum?id=tjn2YZSHUv">[Paper]</a> <a href="https://github.com/Picsart-AI-Research/Social-Reward">[Code] </a> </li>
<li>S. Yu, J. Hong*, H. Zhang, H. Wang*, Z. Wang, and J. Zhou<br> <b style="color:rgb(71, 71, 71)">"Safe and Robust Watermark Injection with a Single OoD Image”</b><br>International Conference on Learning Representations (ICLR), 2024. <a href="https://openreview.net/forum?id=PCm1oT8pZI">[Paper]</a> <a href="https://github.com/illidanlab/Single_oodwatermark">[Code] </a> </li>
<li>D. Sow, S. Lin, Z. Wang, and Y. Liang<br> <b style="color:rgb(71, 71, 71)">"Doubly Robust Instance-Reweighted Adversarial Training”</b><br>International Conference on Learning Representations (ICLR), 2024. <a href="https://openreview.net/forum?id=OF5x1dzWSS">[Paper]</a> <a href="">[Code] </a> </li>
<li>A. Jaiswal*, S. Liu*, T. Chen*, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">"The Emergence of Essential Sparsity in Large Pre-trained Models: The Weights that Matter”</b><br>Advances in Neural Information Processing Systems (NeurIPS), 2023. <a href="https://arxiv.org/abs/2306.03805">[Paper]</a> <a href="https://github.com/VITA-Group/essential_sparsity">[Code] </a> </li>
<li>Z. Zhang*, Y. Sheng, T. Zhou, T. Chen*, L. Zheng, R. Cai*, Z. Song, Y. Tian, C. Ré, C. Barrett, Z. Wang, and B. Chen<br> <b style="color:rgb(71, 71, 71)">"H<sub>2</sub>O: Heavy-Hitter Oracle for Efficient Generative Inference of Large Language Models”</b><br>Advances in Neural Information Processing Systems (NeurIPS), 2023. <a href="https://arxiv.org/abs//2306.14048">[Paper]</a> <a href="https://github.com/FMInference/H2O">[Code] </a> </li>
<li>D. Hoang*, S. Kundu, S. Liu*, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">"Don’t Just Prune by Magnitude! Your Mask Topology is A Secret Weapon”</b><br>Advances in Neural Information Processing Systems (NeurIPS), 2023. <a href="https://openreview.net/forum?id=DIBcdjWV7k">[Paper]</a> <a href="https://github.com/VITA-Group/FullSpectrum-PAI">[Code] </a> </li>
<li>H. Wang*, Z. Jiang*, Y. You*, Y. Han*, G. Liu, J. Srinivasa, R. Kompella, Z. Wang<br> <b style="color:rgb(71, 71, 71)">"Graph Mixture of Experts: Learning on Large-Scale Graphs with Explicit Diversity Modeling”</b><br>Advances in Neural Information Processing Systems (NeurIPS), 2023. <a href="https://arxiv.org/abs/2304.02806">[Paper]</a> <a href="https://github.com/VITA-Group/Graph-Mixture-of-Experts">[Code] </a> </li>
<li>Z. Wang, Y. Jiang*, Y. Lu, Y. Shen, P. He, W. Chen, Z. Wang, M. Zhou<br> <b style="color:rgb(71, 71, 71)">"In-Context Learning Unlocked for Diffusion Models”</b><br>Advances in Neural Information Processing Systems (NeurIPS), 2023. (Spotlight) <a href="https://arxiv.org/abs/2305.01115">[Paper]</a> <a href="https://github.com/Zhendong-Wang/Prompt-Diffusion">[Code] </a> </li>
<li>Z. Wang, Y. Jiang*, H. Zheng, P. Wang*, P. He, Z. Wang, W. Chen, M. Zhou<br> <b style="color:rgb(71, 71, 71)">"Patch Diffusion: Faster and More Data-Efficient Training of Diffusion Models”</b><br>Advances in Neural Information Processing Systems (NeurIPS), 2023. <a href="https://arxiv.org/abs/2304.12526">[Paper]</a> <a href="https://github.com/Zhendong-Wang/Patch-Diffusion">[Code] </a> </li>
<li>L. Yin*, G. Li, M. Fang, L. Shen, T. Huang, Z. Wang, V. Menkovski, X. Ma, M. Pechenizkiy, and S. Liu*<br> <b style="color:rgb(71, 71, 71)">"Dynamic Sparsity Is Channel-Level Sparsity Learner”</b><br>Advances in Neural Information Processing Systems (NeurIPS), 2023. <a href="https://arxiv.org/abs/2305.19454">[Paper]</a> <a href="https://github.com/luuyin/chase">[Code] </a> </li>
<li>W. Cong*, H. Liang*, P. Wang*, Z. Fan*, T. Chen*, M. Varma*, Y. Wang*, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">"Enhancing NeRF akin to Enhancing LLMs: Generalizable NeRF Transformer with Mixture-of-View-Experts”</b><br> IEEE International Conference on Computer Vision (ICCV), 2023. <a href="https://openaccess.thecvf.com/content/ICCV2023/papers/Cong_Enhancing_NeRF_akin_to_Enhancing_LLMs_Generalizable_NeRF_Transformer_with_ICCV_2023_paper.pdf">[Paper]</a> <a href="https://github.com/VITA-Group/GNT-MOVE">[Code] </a> </li>
<li>A. Jaiswal*, X. Zhang, S. Chan, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">"Physics-Driven Turbulence Image Restoration with Stochastic Refinement”</b><br> IEEE International Conference on Computer Vision (ICCV), 2023. <a href="https://openaccess.thecvf.com/content/ICCV2023/papers/Jaiswal_Physics-Driven_Turbulence_Image_Restoration_with_Stochastic_Refinement_ICCV_2023_paper.pdf">[Paper]</a> <a href="https://github.com/VITA-Group/PiRN">[Code] </a> </li>
<li>Y. Han*, P. Wang*, S. Kundu, Y. Ding, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">"Vision HGNN: An Image is More than a Graph of Nodes”</b><br> IEEE International Conference on Computer Vision (ICCV), 2023. (Oral) <a href="https://openaccess.thecvf.com/content/ICCV2023/papers/Han_Vision_HGNN_An_Image_is_More_than_a_Graph_of_ICCV_2023_paper.pdf">[Paper]</a> <a href="https://github.com/VITA-Group/ViHGNN">[Code] </a> </li>
<li>T. Chen*, X. Chen*, X. Du, A. Rashwan, F. Yang, H. Chen, Z. Wang, and Y. Li<br> <b style="color:rgb(71, 71, 71)">"AdaMV-MoE: Adaptive Multi-Task Vision Mixture-of-Experts”</b><br> IEEE International Conference on Computer Vision (ICCV), 2023. <a href="https://openaccess.thecvf.com/content/ICCV2023/papers/Chen_AdaMV-MoE_Adaptive_Multi-Task_Vision_Mixture-of-Experts_ICCV_2023_paper.pdf">[Paper]</a> <a href="https://github.com/google-research/google-research/tree/master/moe_mtl">[Code] </a> </li>
<li>C. Li, B. Feng, Z. Fan*, P. Pan, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">"StegaNeRF: Embedding Invisible Information within Neural Radiance Fields”</b><br> IEEE International Conference on Computer Vision (ICCV), 2023. <a href="https://openaccess.thecvf.com/content/ICCV2023/papers/Li_StegaNeRF_Embedding_Invisible_Information_within_Neural_Radiance_Fields_ICCV_2023_paper.pdf">[Paper]</a> <a href="https://github.com/XGGNet/StegaNeRF">[Code] </a> </li>
<li>X. Xu, Z. Wang, G. Zhang, K. Wang, and H. Shi<br> <b style="color:rgb(71, 71, 71)">"Versatile Diffusion: Text, Images and Variations All in One Diffusion Model”</b><br> IEEE International Conference on Computer Vision (ICCV), 2023. <a href="https://openaccess.thecvf.com/content/ICCV2023/papers/Xu_Versatile_Diffusion_Text_Images_and_Variations_All_in_One_Diffusion_ICCV_2023_paper.pdf">[Paper]</a> <a href="https://github.com/SHI-Labs/Versatile-Diffusion">[Code] </a> </li>
<li>Y. Zhang, R. Cai*, T. Chen*, G. Zhang, H. Zhang, P. Chen, S. Chang, Z. Wang, and S. Liu<br> <b style="color:rgb(71, 71, 71)">"Robust Mixture-of-Expert Training for Convolutional Neural Networks”</b><br> IEEE International Conference on Computer Vision (ICCV), 2023. (Oral) <a href="https://openaccess.thecvf.com/content/ICCV2023/html/Zhang_Robust_Mixture-of-Expert_Training_for_Convolutional_Neural_Networks_ICCV_2023_paper.html">[Paper]</a> <a href="https://github.com/OPTML-Group/Robust-MoE-CNN">[Code] </a> </li>
<li>L. Khachatryan, A. Movsisyan, V. Tadevosyan, R. Henschel, Z. Wang, S. Navasardyan, and H. Shi<br> <b style="color:rgb(71, 71, 71)">"Text2Video-Zero: Text-to-Image Diffusion Models are Zero-Shot Video Generators”</b><br> IEEE International Conference on Computer Vision (ICCV), 2023. (Oral) <a href="https://openaccess.thecvf.com/content/ICCV2023/papers/Khachatryan_Text2Video-Zero_Text-to-Image_Diffusion_Models_are_Zero-Shot_Video_Generators_ICCV_2023_paper.pdf">[Paper]</a> <a href="https://github.com/Picsart-AI-Research/Text2Video-Zero">[Code] </a> </li>
<li>G. Holste*, Z. Jiang*, A. Jaiswal*, M. Hanna, S. Minkowitz, A. Legasto, J. Escalon, S. Steinberger, M. Bittman, T. Shen, Y. Ding, R. Summers, G. Shih, Y. Peng, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“How Does Pruning Impact Long-Tailed Multi-Label Medical Image Classifiers?”</b><br>Medical Image Computing and Computer Assisted Interventions (MICCAI), 2023. <a href="https://arxiv.org/abs/2308.09180">[Paper]</a> <a href="https://github.com/VITA-Group/PruneCXR">[Code] </a></li>
<li>W. Chen*, W. Huang, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“No Free Lunch in Neural Architectures? A Joint Analysis of Expressivity, Convergence, and Generalization”</b><br>International Conference on Automated Machine Learning (AutoML-Conf), 2023. <a href="https://openreview.net/pdf?id=EMys3eIDJ2">[Paper]</a> <a href="https://github.com/chenwydj/no_free_lunch_architectures"> [Code]</a></li>
<li>X. Chen*, T. Chen*, W. Chen, A. Awadallah, Z. Wang, and Y. Cheng<br> <b style="color:rgb(71, 71, 71)">“DSEE: Dually Sparsity-embedded Efficient Tuning of Pre-trained Language Models”</b><br> Annual Meeting of the Association for Computational Linguistics (ACL), 2023. (Long) <a href="https://arxiv.org/abs/2111.00160">[Paper]</a><a href="https://github.com/VITA-Group/DSEE">[Code]</a> </li>
<li>A. Jaiswal*, S. Liu*, T. Chen*, Y. Ding, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“Instant Soup: Cheap Pruning Ensembles in A Single Pass Can Draw Lottery Tickets from Large Models”</b><br>International Conference on Machine Learning (ICML), 2023. (Oral) <a href="https://arxiv.org/abs/2306.10460">[Paper]</a> <a href="https://github.com/VITA-Group/instant_soup">[Code] </a> </li>
<li>A. Jaiswal*, S. Liu*, T. Chen*, Y. Ding, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“Graph Ladling: Shockingly Simple Parallel GNN Training without Intermediate Communication”</b><br>International Conference on Machine Learning (ICML), 2023. <a href="https://arxiv.org/abs/2306.10466">[Paper]</a> <a href="https://github.com/VITA-Group/graph_ladling">[Code] </a> </li>
<li>R. Cai*, Z. Zhang*, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“Robust Weight Signatures: Gaining Robustness as Easy as Patching Weights?”</b><br>International Conference on Machine Learning (ICML), 2023. <a href="https://proceedings.mlr.press/v202/cai23f.html">[Paper]</a> <a href="https://github.com/VITA-Group/Robust_Weight_Signatures">[Code] </a> </li>
<li>P. Wang*, R. Panda, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“Data Efficient Neural Scaling Law via Model Reusing”</b><br>International Conference on Machine Learning (ICML), 2023. <a href="https://proceedings.mlr.press/v202/wang23aa.html">[Paper]</a> <a href="https://github.com/VITA-Group/Data-Efficient-Scaling">[Code] </a> </li>
<li>W. Zheng*, S. Sharan*, A. Jaiswal*, K. Wang*, Y. Xi*, D. Xu*, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“Outline, Then Details: Syntactically Guided Coarse-To-Fine Code Generation”</b><br>International Conference on Machine Learning (ICML), 2023. <a href="https://arxiv.org/abs/2305.00909">[Paper]</a> <a href="https://github.com/VITA-Group/ChainCoder">[Code] </a> </li>
<li>X. Chen*, N. Vadori, T. Chen*, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“Learning to Optimize Differential Games”</b><br>International Conference on Machine Learning (ICML), 2023. <a href="https://proceedings.mlr.press/v202/chen23ab.html">[Paper]</a> <a href="https://github.com/VITA-Group/L2PG">[Code] </a> </li>
<li>T. Huang, L. Yin*, Z. Zhang*, L. Shen, M. Fang, M. Pechenizkiy, Z. Wang, and S. Liu*<br> <b style="color:rgb(71, 71, 71)">“Are Large Kernels Better Teachers than Transformers for ConvNets?”</b><br>International Conference on Machine Learning (ICML), 2023. <a href="https://arxiv.org/abs/2305.19412">[Paper]</a> <a href="https://github.com/VITA-Group/SLaK">[Code] </a> </li>
<li>Y. Ro, Z. Wang, V. Chidambaram, and A. Akella<br> <b style="color:rgb(71, 71, 71)">“Lowering the Pre-training Tax for Gradient-based Subset Training: A Lightweight Distributed Pre-Training Toolkit”</b><br>International Conference on Machine Learning (ICML), 2023. <a href="https://proceedings.mlr.press/v202/ro23a.html">[Paper]</a> <a href="https://github.com/moonbucks/LiPT">[Code] </a> </li>
<li>J. Liu, X. Chen*, Z. Wang, W. Yin, and H. Cai<br> <b style="color:rgb(71, 71, 71)">“Towards Constituting Mathematical Structures for Learning to Optimize”</b><br>International Conference on Machine Learning (ICML), 2023. <a href="https://arxiv.org/abs/2305.18577">[Paper]</a> <a href="https://github.com/xhchrn/MS4L2O">[Code] </a> </li>
<li>D. Xu*, Y. Jiang*, P. Wang*, Z. Fan*, Y. Wang*, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">"NeuralLift-360: Lifting An In-the-wild 2D Photo to A 3D Object with 360◦ Views”</b><br>IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2023. (Highlight) <a href="https://arxiv.org/abs/2211.16431">[Paper]</a> <a href="https://vita-group.github.io/NeuralLift-360/">[Code] </a> </li>
<li>Y. Jiang*, P. Hedman, B. Mildenhall, D. Xu*, J. Barron, Z. Wang, and T. Xue<br> <b style="color:rgb(71, 71, 71)">"AligNeRF: High-Fidelity Neural Radiance Fields via Alignment-Aware Training”</b><br>IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2023. <a href="https://arxiv.org/abs/2211.09682">[Paper]</a> <a href="https://yifanjiang19.github.io/alignerf">[Code] </a> </li>
<li>X. Gong*, S. Mohan, N. Dhingra, J. Bazin, Y. Li, Z. Wang, and R. Ranjan<br> <b style="color:rgb(71, 71, 71)">"MMG-Ego4D: Multimodal Generalization in Egocentric Action Recognition”</b><br>IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2023. <a href="https://arxiv.org/abs/2305.07214">[Paper]</a> <a href="https://github.com/facebookresearch/MMG_Ego4D">[Code] </a></li>
<li> H. Lu*, H. Tunanyan, K. Wang, S. Navasardyan, Z. Wang, and H. Shi<br> <b style="color:rgb(71, 71, 71)">"Specialist Diffusion: Plug-and-Play Sample-Efficient Fine-Tuning of Text-to-Image Diffusion Models to Learn Any Unseen Style”</b><br>IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2023. <a href="https://openaccess.thecvf.com/content/CVPR2023/papers/Lu_Specialist_Diffusion_Plug-and-Play_Sample-Efficient_Fine-Tuning_of_Text-to-Image_Diffusion_Models_To_CVPR_2023_paper.pdf">[Paper]</a> <a href="https://github.com/Picsart-AI-Research/Specialist-Diffusion">[Code] </a></li>
<li>D. Hoang*, S. Liu*, R. Marculescu, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">"Revisiting Pruning at Initialization Through the Lens of Ramanujan Graph”</b><br>International Conference on Learning Representations (ICLR), 2023. (Oral) <a href="https://openreview.net/forum?id=uVcDssQff_">[Paper]</a> <a href="https://github.com/VITA-Group/ramanujan-on-pai">[Code] </a> </li>
<li>S. Liu*, T. Chen*, Z. Zhang*, X. Chen*, T. Huang, A. Jaiswal*, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">"Sparsity May Cry: Let Us Fail (Current) Sparse Neural Networks Together!”</b><br>International Conference on Learning Representations (ICLR), 2023. (Spotlight) <a href="https://openreview.net/forum?id=J6F3lLg4Kdp">[Paper]</a> <a href="https://github.com/VITA-Group/SMC-Bench">[Code] </a> </li>
<li>T. Chen*, Z. Zhang*, A. Jaiswal*, S. Liu*, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">"Sparse MoE as the New Dropout: Scaling Dense and Self-Slimmable Transformers”</b><br>International Conference on Learning Representations (ICLR), 2023. (Spotlight) <a href="https://openreview.net/forum?id=w1hwFUb_81">[Paper]</a> <a href="https://github.com/VITA-Group/Random-MoE-as-Dropout">[Code] </a> </li>
<li>P. Wang*, R. Panda, L. Hennigen, P. Greengard, L. Karlinsky, R. Feris, D. Cox, Z. Wang, and Y. Kim<br> <b style="color:rgb(71, 71, 71)">"Learning to Grow Pretrained Models for Efficient Transformer Training”</b><br>International Conference on Learning Representations (ICLR), 2023. (Spotlight) <a href="https://openreview.net/forum?id=cDYRS5iZ16f">[Paper]</a> <a href="https://github.com/VITA-Group/LiGO">[Code] </a> </li>
<li>S. Yu, J. Hong, H. Wang*, Z. Wang, and J. Zhou<br> <b style="color:rgb(71, 71, 71)">"Turning the Curse of Heterogeneity in Federated Learning into a Blessing for Out-of-Distribution Detection”</b><br>International Conference on Learning Representations (ICLR), 2023. (Spotlight) <a href="https://openreview.net/forum?id=mMNimwRb7Gr">[Paper]</a> <a href="https://github.com/illidanlab/FOSTER">[Code] </a> </li>
<li>S. Liu*, T. Chen*, X. Chen*, X. Chen*, Q. Xiao, B. Wu, T. Karkkainen, M. Pechenizkiy, D. Mocanu, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">"More ConvNets in the 2020s: Scaling up Kernels Beyond 51x51 using Sparsity”</b><br>International Conference on Learning Representations (ICLR), 2023. <a href="https://openreview.net/forum?id=bXNl-myZkJl">[Paper]</a> <a href="https://github.com/VITA-Group/SLaK">[Code] </a> </li>
<li>M. Varma*, P. Wang*, X. Chen*, T. Chen*, S. Venugopalan, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">"Is Attention All That NeRF Needs?”</b><br>International Conference on Learning Representations (ICLR), 2023. <a href="https://openreview.net/forum?id=xE-LtsE-xx">[Paper]</a> <a href="https://github.com/VITA-Group/GNT">[Code]</a> </li>
<li>Z. Fan*, P. Wang*, Y. Jiang*, X. Gong*, D. Xu*, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">"NeRF-SOS: Any-View Self-supervised Object Segmentation on Complex Scenes”</b><br>International Conference on Learning Representations (ICLR), 2023. <a href="https://openreview.net/forum?id=kfOtMqYJlUU">[Paper]</a> <a href="https://github.com/VITA-Group/NeRF-SOS">[Code] </a> </li>
<li>Z. Jiang*, Y. Chen, M. Liu, D. Chen, X. Dai, L. Yuan, Z. Liu, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">"Layer Grafted Pre-training: Bridging Contrastive Learning and Masked Image Modeling For Label-Efficient Representations”</b><br>International Conference on Learning Representations (ICLR), 2023. <a href="https://openreview.net/forum?id=jwdqNwyREyh">[Paper]</a> <a href="https://github.com/VITA-Group/layerGraftedPretraining_ICLR23">[Code]</a> </li>
<li>T. Chen*, C. Gong, D. Diaz, X. Chen*, J. Wells, Q. Liu, Z. Wang, A. Ellington, A. Dimakis, and A. Klivans<br> <b style="color:rgb(71, 71, 71)">"HotProtein: A Novel Framework for Protein Thermostability Prediction and Editing”</b><br>International Conference on Learning Representations (ICLR), 2023. <a href="https://openreview.net/forum?id=YDJRFWBMNby">[Paper]</a> <a href="https://github.com/VITA-Group/HotProtein">[Code]</a> </li>
<li>P. Wang*, S. Yang, Y. Liu, Z. Wang, and P. Li<br> <b style="color:rgb(71, 71, 71)">"Equivariant Hypergraph Diffusion Neural Operators”</b><br>International Conference on Learning Representations (ICLR), 2023. <a href="https://openreview.net/forum?id=RiTjKoscnNd">[Paper]</a> <a href="https://github.com/Graph-COM/ED-HNN">[Code]</a></li>
<li>Y. You*, T. Chen*, Z. Wang, and Y. Shen<br> <b style="color:rgb(71, 71, 71)">"Graph Domain Adaptation via Theory-Grounded Spectral Regularization”</b><br>International Conference on Learning Representations (ICLR), 2023. <a href="https://openreview.net/forum?id=OysfLgrk8mk">[Paper]</a> <a href="https://github.com/Shen-Lab/GDA-SpecReg">[Code]</a></li>
<li>J. Yang, X. Chen*, T. Chen*, Z. Wang, and Y. Liang<br> <b style="color:rgb(71, 71, 71)">"M-L2O: Towards Generalizable Learning-to-Optimize by Test-Time Fast Self-Adaptation”</b><br>International Conference on Learning Representations (ICLR), 2023. <a href="https://openreview.net/forum?id=s7oOe6cNRT8">[Paper]</a> <a href="https://github.com/VITA-Group/M-L2O">[Code]</a></li>
<li>H. Fan, Z. Wang, Y. Yang, and M. Kankanhalli<br> <b style="color:rgb(71, 71, 71)">"Continuous-Discrete Convolution for (3+1)D Geometry-Sequence Modeling in Proteins”</b><br>International Conference on Learning Representations (ICLR), 2023. <a href="https://openreview.net/forum?id=P5Z-Zl9XJ7">[Paper]</a> <a href="https://github.com/hehefan/Continuous-Discrete-Convolution">[Code] </a></li>
<li>H. Yang*, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">"On the Neural Tangent Kernel Analysis of Randomly Pruned Neural Networks”</b><br>International Conference on Artificial Intelligence and Statistics (AISTATS), 2023. <a href="https://arxiv.org/abs/2203.14328">[Paper]</a> <a href="https://github.com/VITA-Group/Random-Pruning-NTK">[Code]</a> </li>
<li>J. Yang, T. Chen*, M. Zhu*, F. He, D. Tao, Y. Liang, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">"Learning to Generalize Provably in Learning to Optimize”</b><br>International Conference on Artificial Intelligence and Statistics (AISTATS), 2023. <a href="https://arxiv.org/abs/2302.11085">[Paper]</a> <a href="https://github.com/VITA-Group/Open-L2O/tree/main/Model_Free_L2O/L2O-Entropy">[Code] </a> </li>
<li>H. Heaton, X. Chen*, Z. Wang, and W. Yin<br> <b style="color:rgb(71, 71, 71)">"Safeguarded Learned Convex Optimization”</b><br>AAAI Conference on Artificial Intelligence (AAAI), 2023. <a href="https://arxiv.org/abs/2003.01880">[Paper]</a> [Code] </li>
<li>J. Hong, H. Wang*, Z. Wang, and J. Zhou<br> <b style="color:rgb(71, 71, 71)">"Federated Robustness Propagation: Sharing Adversarial Robustness in Heterogeneous Federated Learning”</b><br>AAAI Conference on Artificial Intelligence (AAAI), 2023. <a href="https://arxiv.org/abs/2106.10196">[Paper]</a> [Code] </li>
<li>Z. Kong, H. Ma, G. Yuan, M. Sun, Y. Xie, P. Dong, X. Meng, X. Shen, H. Tang, M. Qin, T. Chen*, X. Ma, X. Xie, Z. Wang, and Y. Wang<br> <b style="color:rgb(71, 71, 71)">"Peeling the Onion: Hierarchical Reduction of Data Redundancy for Efficient Vision Transformer Training”</b><br>AAAI Conference on Artificial Intelligence (AAAI), 2023. <a href="https://arxiv.org/abs/2211.10801">[Paper]</a> <a href="https://github.com/ZLKong/Tri-Level-ViT">[Code] </a> </li>
<li>T. Huang, T. Chen*, M. Fang, V. Menkovski, J. Zhao, L. Yin, Y. Pei, D. Mocanu, Z. Wang, M. Pechenizkiy, and S. Liu*<br> <b style="color:rgb(71, 71, 71)">"You Can Have Better Graph Neural Networks by Not Training Weights at All: Finding Untrained Graph Tickets”</b><br>Learning on Graphs Conference (LoG), 2022. (Oral & Best Paper Award) <a href="https://openreview.net/forum?id=dF6aEW3_62O">[Paper]</a> <a href="https://github.com/TienjinHuang/UGTs-LoG">[Code] </a> </li>
<li>Y. Han*, E. Huang, W. Zheng*, N. Rao, Z. Wang, and K. Subbian<br> <b style="color:rgb(71, 71, 71)">“Search Behavior Prediction: A Hypergraph Perspective”</b><br>ACM International Conference on Web Search and Data Mining (WSDM), 2023. <a href="https://arxiv.org/abs/2211.13328">[Paper]</a> <a href="https://github.com/amazon-science/dual-channel-hypergraph-neural-network">[Code] </a> </li>
<li>W. Chen*, W. Huang, X. Gong*, B. Hanin, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“Deep Architecture Connectivity Matters for Its Convergence: A Fine-Grained Analysis”</b><br>Advances in Neural Information Processing Systems (NeurIPS), 2022. <a href="https://arxiv.org/abs/2205.05662">[Paper]</a> <a href="https://github.com/VITA-Group/architecture_convergence">[Code] </a> </li>
<li>D. Xu*, P. Wang*, Y. Jiang*, Z. Fan*, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“Signal Processing for Implicit Neural Representations”</b><br>Advances in Neural Information Processing Systems (NeurIPS), 2022. <a href="https://arxiv.org/abs/2210.08772">[Paper]</a> <a href="https://vita-group.github.io/INSP/">[Code] </a> </li>
<li>H. Liang*, Z. Fan*, R. Sarkar, Z. Jiang*, T. Chen*, K. Zou, Y. Cheng, C. Hao, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“M<sup>3</sup>ViT: Mixture-of-Experts Vision Transformer for Efficient Multi-task Learning with Model-Accelerator Co-design”</b><br>Advances in Neural Information Processing Systems (NeurIPS), 2022. <a href="https://arxiv.org/abs/2210.14793">[Paper]</a> <a href="https://github.com/VITA-Group/M3ViT">[Code] </a> </li>
<li>Z. Jiang*, X. Chen*, X. Huang, X. Du, D. Zhou, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“Back Razor: Memory-Efficient Transfer Learning by Self-Sparsified Backpropogation”</b><br>Advances in Neural Information Processing Systems (NeurIPS), 2022. <a href="https://openreview.net/forum?id=mTXQIpXPDbh">[Paper]</a> <a href="https://github.com/VITA-Group/BackRazor_Neurips22">[Code] </a> </li>
<li>R. Cai*, Z. Zhang*, T. Chen*, X. Chen*, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“Randomized Channel Shuffling: Minimal-Overhead Backdoor Attack Detection without Clean Datasets”</b><br>Advances in Neural Information Processing Systems (NeurIPS), 2022. <a href="https://openreview.net/forum?id=TItRK4VP9X2">[Paper]</a> <a href="https://github.com/VITA-Group/Random-Shuffling-BackdoorDetect">[Code] </a> </li>
<li>S. Sharan*, W. Zheng*, K. Hsu, J. Xiong, A. Chen, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“Symbolic Distillation for Learned TCP Congestion Control”</b><br>Advances in Neural Information Processing Systems (NeurIPS), 2022. <a href="https://arxiv.org/abs/2210.16987">[Paper]</a> <a href="https://github.com/VITA-Group/SymbolicPCC">[Code] </a> </li>
<li>A. Jaiswal*, P. Wang*, T. Chen*, J. Rousseau, Y. Ding, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“Old can be Gold: Better Gradient Flow can make Vanilla-GCNs Great Again”</b><br>Advances in Neural Information Processing Systems (NeurIPS), 2022. <a href="https://arxiv.org/abs/2210.08122">[Paper]</a> <a href="https://github.com/VITA-Group/GradientGCN">[Code]</a></li>
<li>H. Wang*, J. Hong, A. Zhang, J. Zhou, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“Trap and Replace: Defending Backdoor Attacks by Trapping Them into an Easy-to-Replace Subnetwork”</b><br>Advances in Neural Information Processing Systems (NeurIPS), 2022. <a href="https://arxiv.org/abs/2210.06428">[Paper]</a> <a href="https://github.com/VITA-Group/Trap-and-Replace-Backdoor-Defense">[Code]</a> </li>
<li>J. Wu*, Y. Liang, F. Han, H. Akbari, Z. Wang, and C. Yu<br> <b style="color:rgb(71, 71, 71)">“Scaling Multimodal Pre-Training via Cross-Modality Gradient Harmonization”</b><br>Advances in Neural Information Processing Systems (NeurIPS), 2022. <a href="https://openreview.net/forum?id=7WuCttgNQ79">[Paper]</a> [Code] </li>
<li>M. Varma*, X. Chen*, Z. Zhang*, T. Chen*, S. Venugopalan, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“Sparse Winning Tickets are Data-Efficient Image Recognizers”</b><br>Advances in Neural Information Processing Systems (NeurIPS), 2022. <a href="https://openreview.net/forum?id=wfKbtSjHA6F">[Paper]</a> <a href="https://github.com/VITA-Group/DataEfficientLTH">[Code]</a> </li>
<li>T. Wei, Y. You*, T. Chen*, Y. Shen, J. He, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“Augmentations in Hypergraph Contrastive Learning: Fabricated and Generative”</b><br>Advances in Neural Information Processing Systems (NeurIPS), 2022. <a href="https://arxiv.org/abs/2210.03801">[Paper]</a> <a href="https://github.com/weitianxin/HyperGCL">[Code]</a> </li>
<li>K. Duan, Z. Liu, P. Wang*, W. Zheng*, K. Zhou, T. Chen*, X. Hu, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“A Comprehensive Study on Large-Scale Graph Training: Benchmarking and Rethinking”</b><br>Advances in Neural Information Processing Systems, Track on Datasets and Benchmarks (NeurIPS D & B), 2022. <a href="https://openreview.net/forum?id=2QrFr_U782Z">[Paper]</a> <a href="https://github.com/VITA-Group/Large_Scale_GCN_Benchmarking">[Code] </a> </li>
<li>D. Xu*, Y. Jiang*, P. Wang*, Z. Fan*, H. Shi, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“SinNeRF: Training Neural Radiance Field on Complex Scenes from a Single Image”</b><br>European Conference on Computer Vision (ECCV), 2022. <a href="https://arxiv.org/abs/2204.00928">[Paper]</a> <a href="https://vita-group.github.io/SinNeRF/">[Code] </a> </li>
<li>Z. Fan*, Y. Jiang*, P. Wang*, X. Gong*, D. Xu*, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“Unified Implicit Neural Stylization”</b><br>European Conference on Computer Vision (ECCV), 2022. <a href="https://arxiv.org/abs/2204.01943">[Paper]</a> <a href="https://zhiwenfan.github.io/INS/">[Code] </a> </li>
<li>X. Chen*, T. Chen*, Y. Cheng, W. Chen, A. Awadallah, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“Scalable Learning to Optimize: A Learned Optimizer Can Train Big Models”</b><br>European Conference on Computer Vision (ECCV), 2022. <a href="https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136830376.pdf">[Paper]</a> <a href="https://github.com/VITA-Group/Scalable-L2O">[Code] </a> </li>
<li>H. Liang*, H. Fan*, Z. Fan*, Y. Wang*, T. Chen*, Y. Cheng, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“Point Cloud Domain Adaptation via Masked Local 3D Structure Prediction”</b><br>European Conference on Computer Vision (ECCV), 2022. <a href="https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136630159.pdf">[Paper]</a> <a href="https://github.com/VITA-Group/MLSP">[Code] </a> </li>
<li> Z. Jiang*, T. Chen*, X. Chen*, Y. Cheng, L. Zhou, L. Yuan, A. Awadallah, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“DnA: Improving Few-shot Transfer Learning with Low-Rank Decomposition and Alignment”</b><br>European Conference on Computer Vision (ECCV), 2022. <a href="https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136800229.pdf">[Paper]</a> <a href="https://github.com/VITA-Group/DnA">[Code] </a> </li>
<li> Y. Jiang*, B. Wronski, B. Mildenhall, J. Barron, Z. Wang, and T. Xue<br> <b style="color:rgb(71, 71, 71)">“Fast and High Quality Image Denoising via Malleable Convolution”</b><br>European Conference on Computer Vision (ECCV), 2022. <a href="https://arxiv.org/abs/2201.00392">[Paper]</a> <a href="https://yifanjiang.net/MalleConv.html">[Code] </a> </li>
<li> W. Chen*, X. Du, F. Yang, L. Beyer, X. Zhai, T. Lin, H. Chen, J. Li, X. Song, Z. Wang, and D. Zhou<br> <b style="color:rgb(71, 71, 71)">“A Simple Single-Scale Vision Transformer for Object Detection and Instance Segmentation”</b><br>European Conference on Computer Vision (ECCV), 2022. <a href="https://arxiv.org/abs/2112.09747">[Paper]</a> [Code] </li>
<li> Z. Mao, A. Jaiswal*, Z. Wang, and S. Chan<br> <b style="color:rgb(71, 71, 71)">“Single Frame Atmospheric Turbulence Mitigation: A Benchmark Study and A New Physics-Inspired Transformer Model”</b><br>European Conference on Computer Vision (ECCV), 2022. <a href="https://arxiv.org/abs/2207.10040">[Paper]</a> <a href="https://github.com/VITA-Group/TurbNet">[Code] </a> </li>
<li>H. Wang*, A. Zhang, Y. Zhu, S. Zheng, M. Li, A. Smola, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“Partial and Asymmetric Contrastive Learning for Out-of-Distribution Detection in Long-Tailed Recognition”</b><br>International Conference on Machine Learning (ICML), 2022. (Long Talk) <a href="https://proceedings.mlr.press/v162/wang22aq/wang22aq.pdf">[Paper]</a> <a href="https://github.com/amazon-research/long-tailed-ood-detection">[Code]</a> </li>
<li>H. Wang*, A. Zhang, S. Zheng, X. Shi, M. Li, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“Removing Batch Normalization Boosts Adversarial Training”</b><br>International Conference on Machine Learning (ICML), 2022. <a href="https://proceedings.mlr.press/v162/wang22ap/wang22ap.pdf">[Paper]</a> <a href="https://github.com/amazon-research/normalizer-free-robust-training">[Code]</a> </li>
<li>P. Wang*, Z. Fan*, T. Chen*, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“Neural Implicit Dictionary Learning via Mixture-of-Expert Training”</b><br>International Conference on Machine Learning (ICML), 2022. <a href="https://proceedings.mlr.press/v162/wang22d/wang22d.pdf">[Paper]</a> <a href="https://github.com/VITA-Group/Neural-Implicit-Dict">[Code]</a> </li>
<li>A. Jaiswal*, H. Ma, T. Chen*, Y. Ding, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“Training Your Sparse Neural Network Better with Any Mask”</b><br>International Conference on Machine Learning (ICML), 2022. <a href="https://proceedings.mlr.press/v162/jaiswal22a/jaiswal22a.pdf">[Paper]</a> <a href="https://github.com/VITA-Group/ToST">[Code]</a> </li>
<li>T. Chen*, H. Zhang, Z. Zhang*, S. Chang, S. Liu, P. Chen, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“Linearity Grafting: How Neuron Pruning Helps Certifiable Robustness”</b><br>International Conference on Machine Learning (ICML), 2022. <a href="https://proceedings.mlr.press/v162/chen22af/chen22af.pdf">[Paper]</a> <a href="https://github.com/VITA-Group/Linearity-Grafting">[Code]</a> </li>
<li>T. Chen*, X. Chen*, X. Ma, Y. Wang, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“Coarsening the Granularity: Towards Structurally Sparse Lottery Tickets”</b><br>International Conference on Machine Learning (ICML), 2022. <a href="https://proceedings.mlr.press/v162/chen22a/chen22a.pdf">[Paper]</a> <a href="https://github.com/VITA-Group/Structure-LTH">[Code]</a></li>
<li>T. Chen*, Z. Zhang*, S. Liu, Y. Zhang, S. Chang, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“Data-Efficient Double-Win Lottery Tickets from Robust Pre-training”</b><br>International Conference on Machine Learning (ICML), 2022. <a href="https://proceedings.mlr.press/v162/chen22ae/chen22ae.pdf">[Paper]</a> <a href="https://github.com/VITA-Group/Double-Win-LTH">[Code]</a> </li>
<li>W. Redman, T Chen*, Z. Wang, and A. Dogra,<br> <b style="color:rgb(71, 71, 71)">“Universality of Winning Tickets: A Renormalization Group Perspective”</b><br>International Conference on Machine Learning (ICML), 2022. <a href="https://proceedings.mlr.press/v162/redman22a/redman22a.pdf">[Paper]</a> [Code]</li>
<li>R. Ardywibowo, Z. Huo, Z. Wang, B. Mortazavi, S. Huang, and X. Qian<br> <b style="color:rgb(71, 71, 71)">“VariGrow: Variational Architecture Growing for Task-Agnostic Continual Learning based on Bayesian Novelty”</b><br>International Conference on Machine Learning (ICML), 2022. <a href="https://proceedings.mlr.press/v162/ardywibowo22a/ardywibowo22a.pdf">[Paper]</a> [Code] </li>
<li>D. Hoang*, K. Zhou, T. Chen*, X. Hu, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“AutoCoG: A Unified Data-Model Co-Search Framework for Graph Neural Networks”</b><br>International Conference on Automated Machine Learning (AutoML-Conf), 2022. <a href="https://openreview.net/forum?id=r0zIWWar8gq">[Paper]</a> <a href="https://github.com/VITA-Group/AutoCoG"> [Code]</a></li>
<li>J. Hong, Z. Wang, and J. Zhou<br> <b style="color:rgb(71, 71, 71)">“Dynamic Privacy Budget Allocation Improves Data Efficiency of Differentially Private Gradient Descent”</b><br>ACM Conference on Fairness, Accountability, and Transparency (FAccT), 2022. <a href="https://arxiv.org/abs/2101.07413">[Paper]</a> [Code]</li>
<li>T. Chen*, Z. Zhang*, Y. Cheng, A. Awadallah, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“The Principle of Diversity: Training Stronger Vision Transformers Calls for Reducing All Levels of Redundancy”</b><br>IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022. <a href="https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_The_Principle_of_Diversity_Training_Stronger_Vision_Transformers_Calls_for_CVPR_2022_paper.pdf">[Paper]</a> <a href="https://github.com/VITA-Group/Diverse-ViT">[Code]</a></li>
<li>T. Chen*, P. Wang*, Z. Fan*, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“Aug-NeRF: Training Stronger Neural Radiance Fields with Triple-Level Physically-Grounded Augmentations”</b><br>IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022. <a href="https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_Aug-NeRF_Training_Stronger_Neural_Radiance_Fields_With_Triple-Level_Physically-Grounded_Augmentations_CVPR_2022_paper.pdf">[Paper]</a> <a href="https://github.com/VITA-Group/Aug-NeRF">[Code]</a></li>
<li>Z. Fan*, T. Chen*, P. Wang*, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“CADTransformer: Panoptic Symbol Spotting Transformer for CAD Drawing”</b><br>IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022. (Oral) <a href="https://openaccess.thecvf.com/content/CVPR2022/papers/Fan_CADTransformer_Panoptic_Symbol_Spotting_Transformer_for_CAD_Drawings_CVPR_2022_paper.pdf">[Paper]</a> <a href="https://github.com/VITA-Group/CADTransformer">[Code]</a></li>
<li>T. Chen*, Z. Zhang*, Y. Zhang, S. Chang, S. Liu, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“Quarantine: Sparsity Can Uncover the Trojan Attack Trigger for Free”</b><br>IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022. <a href="https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_Quarantine_Sparsity_Can_Uncover_the_Trojan_Attack_Trigger_for_Free_CVPR_2022_paper.pdf">[Paper]</a> <a href="https://github.com/VITA-Group/Backdoor-LTH">[Code]</a></li>
<li>X. Sun, A. Hassani, Z. Wang, G. Huang, and H. Shi<br> <b style="color:rgb(71, 71, 71)">“DiSparse: Disentangled Sparsification for Multitask Model Compression”</b><br>IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022. <a href="https://openaccess.thecvf.com/content/CVPR2022/papers/Sun_DiSparse_Disentangled_Sparsification_for_Multitask_Model_Compression_CVPR_2022_paper.pdf">[Paper]</a> <a href="https://github.com/SHI-Labs/DiSparse-Multitask-Model-Compression">[Code]</a></li>
<li>Z. Chen, Y. Chen, J. Liu, X. Xu, V. Goel, Z. Wang, H. Shi, and X. Wang<br> <b style="color:rgb(71, 71, 71)">“VideoINR: Learning Video Implicit Neural Representation for Continuous Space-Time Super-Resolution”</b><br>IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022. <a href="https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_VideoINR_Learning_Video_Implicit_Neural_Representation_for_Continuous_Space-Time_Super-Resolution_CVPR_2022_paper.pdf">[Paper]</a> <a href="https://github.com/Picsart-AI-Research/VideoINR-Continuous-Space-Time-Super-Resolution">[Code]</a></li>
<li>H. Ma, H. Zhao, Z. Lin, A. Kale, Z. Wang, T. Yu, J. Gu, S. Choudhary, and X. Xie<br> <b style="color:rgb(71, 71, 71)">“EI-CLIP: Entity-aware Interventional Contrastive Learning for E-commerce Cross-modal Retrieval”</b><br>IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022. <a href="https://openaccess.thecvf.com/content/CVPR2022/papers/Ma_EI-CLIP_Entity-Aware_Interventional_Contrastive_Learning_for_E-Commerce_Cross-Modal_Retrieval_CVPR_2022_paper.pdf">[Paper]</a> <a href="">[Code]</a></li>
<li>W. Zheng*, T. Chen*, T. Hu*, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“Symbolic Learning to Optimize: Towards Interpretability and Scalability”</b><br>International Conference on Learning Representations (ICLR), 2022. <a href="https://openreview.net/forum?id=ef0nInZHKIC">[Paper]</a> <a href="https://github.com/VITA-Group/Symbolic-Learning-To-Optimize">[Code]</a></li>
<li>X. Chen*, J. Zhang*, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“Peek-a-Boo: What (More) is Disguised in a Randomly Weighted Neural Network, and How to Find It Efficiently”</b><br>International Conference on Learning Representations (ICLR), 2022. <a href="https://openreview.net/forum?id=moHCzz6D5H3">[Paper]</a> <a href="https://github.com/VITA-Group/Peek-a-Boo">[Code]</a></li>
<li>T. Huang*, T. Chen*, S. Liu, S. Chang, L. Amini, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“Optimizer Amalgamation”</b><br>International Conference on Learning Representations (ICLR), 2022. <a href="https://openreview.net/forum?id=VqzXzA9hjaX">[Paper]</a> <a href="https://github.com/VITA-Group/Optimizer_Amalgamation">[Code]</a></li>
<li>T. Chen*, Z. Zhang*, P. Wang, S. Balachandra*, H. Ma, Z. Wang, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“Sparsity Winning Twice: Better Robust Generalization from More Efficient Training”</b><br>International Conference on Learning Representations (ICLR), 2022. <a href="https://openreview.net/forum?id=SYuJXrXq8tw">[Paper]</a> <a href="https://github.com/VITA-Group/Sparsity-Win-Robust-Generalization">[Code]</a></li>
<li>P. Wang*, W. Zheng*, T. Chen*, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“Anti-Oversmoothing in Deep Vision Transformers via the Fourier Domain Analysis: From Theory to Practice”</b><br>International Conference on Learning Representations (ICLR), 2022. <a href="https://openreview.net/forum?id=O476oWmiNNp">[Paper]</a> <a href="https://github.com/VITA-Group/ViT-Anti-Oversmoothing">[Code]</a></li>
<li>W. Chen*, W Huang, X. Du, X. Song, Z. Wang, and D. Zhou<br> <b style="color:rgb(71, 71, 71)">“Auto-Scaling Vision Transformers without Training”</b><br>International Conference on Learning Representations (ICLR), 2022. <a href="https://openreview.net/forum?id=H94a1_Pyr-6">[Paper]</a> <a href="https://github.com/VITA-Group/AsViT">[Code]</a></li>
<li>S. Yu*, T. Chen*, J. Shen*, H. Yuan, J. Tian, S. Yang, J. Liu, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“Unified Visual Transformer Compression”</b><br>International Conference on Learning Representations (ICLR), 2022. <a href="https://openreview.net/forum?id=9jsZiUgkCZP">[Paper]</a> <a href="https://github.com/VITA-Group/UVC">[Code]</a></li>
<li>M. Lu*, X. Luo*, T. Chen*, W. Chen*, D. Liu, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“Learning Pruning-Friendly Networks via Frank-Wolfe: One-Shot, Any-Sparsity, And No Retraining”</b><br>International Conference on Learning Representations (ICLR), 2022. (Spotlight) <a href="https://openreview.net/forum?id=O1DEtITim__">[Paper]</a> <a href="https://github.com/VITA-Group/SFW-Once-for-All-Pruning">[Code]</a></li>
<li>W. Zheng*, E. Huang, N. Rao, S. Katariya, Z. Wang, and K. Subbian<br> <b style="color:rgb(71, 71, 71)">“Cold Brew: Distilling Graph Node Representations with Incomplete or Missing Neighborhoods”</b><br>International Conference on Learning Representations (ICLR), 2022. <a href="https://openreview.net/forum?id=1ugNpm7W6E">[Paper]</a> <a href="https://github.com/amazon-research/gnn-tail-generalization">[Code]</a></li>
<li>S. Ding, T. Chen*, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“Audio Lottery: Speech Recognition Made Ultra-Lightweight, Noise-Robust, and Transferable”</b><br>International Conference on Learning Representations (ICLR), 2022. <a href="https://openreview.net/forum?id=9Nk6AJkVYB">[Paper]</a> <a href="https://github.com/VITA-Group/Audio-Lottery">[Code]</a></li>
<li>J. Hong, H. Wang*, Z. Wang, and J. Zhou<br> <b style="color:rgb(71, 71, 71)">“Efficient Split-Mix Federated Learning for On-Demand and In-Situ Customization”</b><br>International Conference on Learning Representations (ICLR), 2022. <a href="https://openreview.net/forum?id=_QLmakITKg">[Paper]</a> <a href="https://github.com/illidanlab/SplitMix">[Code]</a></li>
<li>S. Liu, T. Chen*, Z. Atashgahi, X. Chen*, G. Sokar, E. Mocanu, M. Pechenizkiy, Z. Wang, and D. Mocanu<br> <b style="color:rgb(71, 71, 71)">“Deep Ensembling with No Overhead for either Training or Testing: The All-Round Blessings of Dynamic Sparsity”</b><br>International Conference on Learning Representations (ICLR), 2022. <a href="https://openreview.net/forum?id=RLtqs6pzj1-">[Paper]</a> <a href="https://github.com/VITA-Group/FreeTickets">[Code]</a></li>
<li>S. Liu, T. Chen*, X. Chen*, L. Shen, D. Mocanu, Z. Wang, and M. Pechenizkiy<br> <b style="color:rgb(71, 71, 71)">“The Unreasonable Effectiveness of Random Pruning: Return of the Most Naive Baseline for Sparse Training”</b><br>International Conference on Learning Representations (ICLR), 2022. <a href="https://openreview.net/forum?id=VBZJ_3tz-t">[Paper]</a> <a href="https://github.com/VITA-Group/Random_Pruning">[Code]</a></li>
<li>Y. You*, Y. Cao, T. Chen*, Z. Wang, and Y. Shen<br> <b style="color:rgb(71, 71, 71)">“Bayesian Modeling and Uncertainty Quantification for Learning to Optimize: What, Why, and How”</b><br>International Conference on Learning Representations (ICLR), 2022. <a href="https://openreview.net/forum?id=EVVadRFRgL7">[Paper]</a> <a href="https://github.com/Shen-Lab/Bayesian-L2O">[Code]</a></li>
<li> R. Ardywibowo, S. Boluki, Z. Wang, B. Mortazavi, S. Huang, and X. Qian<br> <b style="color:rgb(71, 71, 71)">“VFDS: Variational Foresight Dynamic Selection in Bayesian Neural Networks for Efficient Human Activity Recognition”</b><br>International Conference on Artificial Intelligence and Statistics (AISTATS), 2022. <a href="https://arxiv.org/abs/2204.00130">[Paper]</a> <a href="">[Code]</a></li>
<li> S. Bibikar, H. Vikalo, Z. Wang, and X. Chen* (X. C. as corresponding author)<br> <b style="color:rgb(71, 71, 71)">“Federated Dynamic Sparse Training: Computing Less, Communicating Less, Yet Learning Better”</b><br>AAAI Conference on Artificial Intelligence (AAAI), 2022. <a href="https://arxiv.org/abs/2112.09824">[Paper]</a> <a href="https://github.com/bibikar/feddst">[Code]</a></li>
<li>Y. You*, T. Chen*, Z. Wang and Y. Shen<br> <b style="color:rgb(71, 71, 71)">“Bringing Your Own View: Graph Contrastive Learning without Prefabricated Data Augmentations”</b><br>ACM International Conference on Web Search and Data Mining (WSDM), 2022. <a href="https://arxiv.org/abs/2201.01702">[Paper]</a> <a href="https://github.com/Shen-Lab/GraphCL_Automated">[Code]</a></li>
<li>Y. Jiang*, S. Chang, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“TransGAN: Two Pure Transformers Can Make One Strong GAN, and That Can Scale Up”</b><br>Advances in Neural Information Processing Systems (NeurIPS), 2021. <a href="https://arxiv.org/abs/2102.07074">[Paper]</a> <a href="https://github.com/VITA-Group/TransGAN">[Code]</a></li>
<li>H. Wang*, C. Xiao, J. Kossaifi, Z. Yu, A. Anandkumar, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“AugMax: Adversarial Composition of Random Augmentations for Robust Training”</b><br>Advances in Neural Information Processing Systems (NeurIPS), 2021. <a href="https://arxiv.org/abs/2110.13771">[Paper]</a> <a href="https://github.com/VITA-Group/AugMax">[Code]</a></li>
<li>T. Chen*, Y. Cheng, Z. Gan, J. Liu, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“Data-Efficient GAN Training Beyond (Just) Augmentations: A Lottery Ticket Perspective”</b><br>Advances in Neural Information Processing Systems (NeurIPS), 2021. <a href="https://arxiv.org/abs/2103.00397">[Paper]</a> <a href="https://github.com/VITA-Group/Ultra-Data-Efficient-GAN-Training">[Code]</a></li>
<li>X. Chen*, Y. Cheng, S. Wang, Z. Gan, J. Liu, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“The Elastic Lottery Ticket Hypothesis”</b><br>Advances in Neural Information Processing Systems (NeurIPS), 2021. <a href="https://arxiv.org/abs/2103.16547">[Paper]</a> <a href="https://github.com/VITA-Group/ElasticLTH">[Code]</a></li>
<li>T. Chen*, Y. Cheng, Z. Gan, L. Yuan, L. Zhang, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“Chasing Sparsity in Vision Transformers: An End-to-End Exploration”</b><br>Advances in Neural Information Processing Systems (NeurIPS), 2021. <a href="https://arxiv.org/abs/2106.04533">[Paper]</a> <a href="https://github.com/VITA-Group/SViTE">[Code]</a></li>
<li>W. Zheng*, Q. Guo, H. Yang, P. Wang*, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“Delayed Propagation Transformer: A Universal Computation Engine towards Practical Control in Cyber-Physical Systems”</b><br>Advances in Neural Information Processing Systems (NeurIPS), 2021. <a href="https://arxiv.org/abs/2110.15926">[Paper]</a> <a href="https://github.com/VITA-Group/DePT">[Code]</a></li>
<li>X. Chen*, T. Chen*, Z. Zhang*, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“You Are Caught Stealing My Winning Lottery Ticket! Making a Lottery Ticket Claim its Ownership”</b><br>Advances in Neural Information Processing Systems (NeurIPS), 2021. <a href="https://arxiv.org/abs/2111.00162">[Paper]</a> <a href="https://github.com/VITA-Group/NO-stealing-LTH">[Code]</a></li>
<li>Z. Jiang*, T. Chen*, T. Chen, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“Improving Contrastive Learning on Imbalanced Seed Data via Open-World Sampling”</b><br>Advances in Neural Information Processing Systems (NeurIPS), 2021. <a href="https://arxiv.org/abs/2111.01004">[Paper]</a> <a href="https://github.com/VITA-Group/MAK">[Code]</a></li>
<li>X. Chen*, J. Liu, Z. Wang, W. Yin<br> <b style="color:rgb(71, 71, 71)">“Hyperparameter Tuning is All You Need for LISTA”</b><br>Advances in Neural Information Processing Systems (NeurIPS), 2021. <a href="https://arxiv.org/abs/2110.15900">[Paper]</a> <a href="https://github.com/VITA-Group/HyperLISTA">[Code]</a></li>
<li>J. Wu*, X. Dai, D. Chen, Y. Chen, M. Liu, Y. Yu, Z. Wang, Z. Liu, M. Chen, and L. Yuan<br> <b style="color:rgb(71, 71, 71)">“Stronger NAS with Weaker Predictors”</b><br>Advances in Neural Information Processing Systems (NeurIPS), 2021. <a href="https://arxiv.org/abs/2102.10490">[Paper]</a> <a href="https://github.com/VITA-Group/WeakNAS">[Code]</a></li>
<li>B. Pan, R. Panda, Y. Jiang*, Z. Wang, R. Feris, and A. Oliva<br> <b style="color:rgb(71, 71, 71)">“IA-RED<sup>2</sup>: Interpretability-Aware Redundancy Reduction for Vision Transformers”</b><br>Advances in Neural Information Processing Systems (NeurIPS), 2021. <a href="https://arxiv.org/abs/2106.12620">[Paper]</a> <a href="http://people.csail.mit.edu/bpan/ia-red/">[Code]</a></li>
<li>S. Liu, T. Chen*, X. Chen*, Z. Atashgahi, L. Yin, H. Kou, L. Shen, M. Pechenizkiy, Z. Wang, and D. Mocanu<br> <b style="color:rgb(71, 71, 71)">“Sparse Training via Boosting Pruning Plasticity with Neuroregeneration”</b><br>Advances in Neural Information Processing Systems (NeurIPS), 2021. <a href="https://arxiv.org/abs/2106.10404">[Paper]</a> <a href="https://github.com/VITA-Group/GraNet">[Code]</a></li>
<li>X. Ma, G. Yuan, X. Shen, T. Chen*, X. Chen*, X. Chen*, N. Liu, M. Qin, S. Liu, Z. Wang, and Y. Wang<br> <b style="color:rgb(71, 71, 71)">“Sanity Checks for Lottery Tickets: Does Your Winning Ticket Really Win the Jackpot?”</b><br>Advances in Neural Information Processing Systems (NeurIPS), 2021. <a href="https://arxiv.org/abs/2107.00166">[Paper]</a> <a href="https://github.com/boone891214/sanity-check-LTH">[Code]</a></li>
<li> Y. Jiang*, H. Zhang, J. Zhang, Y. Wang, Z. Lin, K. Sunkavalli, S. Chen, S. Amirghods, S. Kong, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“SSH: A Self-Supervised Framework for Image Harmonization”</b><br> IEEE International Conference on Computer Vision (ICCV), 2021. <a href="https://arxiv.org/pdf/2108.06805.pdf">[Paper]</a> <a href="https://github.com/VITA-Group/SSHarmonization">[Code]</a></li>
<li> X. Gong*, H. Wang, M. Shou, M. Feiszli, Z. Wang, and Z. Yan<br> <b style="color:rgb(71, 71, 71)">“Searching for Two-Stream Models in Multivariate Space for Video Recognition”</b><br> IEEE International Conference on Computer Vision (ICCV), 2021. <a href="https://arxiv.org/abs/2108.12957">[Paper]</a> [Code]</li>
<li> Y. Guo, H. Yuan, J. Tan, Z. Wang, S. Yang, and J. Liu<br> <b style="color:rgb(71, 71, 71)">“GDP: Stabilized Neural Network Pruning via Gates with Differentiable Polarization”</b><br> IEEE International Conference on Computer Vision (ICCV), 2021. <a href="https://arxiv.org/abs/2109.02220">[Paper]</a> [Code]</li>
<li>Y. You*, T. Chen*, Y. Shen, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“Graph Contrastive Learning Automated”</b><br> International Conference on Machine Learning (ICML), 2021. (Long Talk) <a href="https://arxiv.org/abs/2106.07594">[Paper]</a><a href="https://github.com/Shen-Lab/GraphCL_Automated">[Code]</a> </li>
<li>M. Zhu*, T. Chen*, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“Sparse and Imperceptible Adversarial Attack via a Homotopy Algorithm”</b><br> International Conference on Machine Learning (ICML), 2021. (Long Talk) <a href="https://arxiv.org/abs/2106.06027">[Paper]</a><a href="https://github.com/VITA-Group/SparseADV_Homotopy">[Code]</a> </li>
<li> T. Chen*, Y. Sui, X. Chen*, A. Zhang, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“A Unified Lottery Ticket Hypothesis for Graph Neural Networks”</b><br> International Conference on Machine Learning (ICML), 2021. <a href="https://arxiv.org/abs/2102.06790">[Paper]</a><a href="https://github.com/VITA-Group/Unified-LTH-GNN">[Code]</a> </li>
<li> Z. Jiang*, T. Chen*, B. Mortazavi, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“Self-Damaging Contrastive Learning”</b><br> International Conference on Machine Learning (ICML), 2021. <a href="https://arxiv.org/abs/2106.02990">[Paper]</a><a href="https://github.com/VITA-Group/SDCLR">[Code]</a> </li>
<li> Z. Zhang*, X. Chen*, T. Chen*, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“Efficient Lottery Ticket Finding: Less Data is More”</b><br> International Conference on Machine Learning (ICML), 2021. <a href="https://arxiv.org/abs/2106.03225">[Paper]</a><a href="https://github.com/VITA-Group/PrAC-LTH">[Code]</a> </li>
<li>X. Chen*, Y. Cheng, S. Wang, Z. Gan, Z. Wang, and J. Liu<br> <b style="color:rgb(71, 71, 71)">“EarlyBERT: Efficient BERT Training via Early-Bird Lottery Tickets”</b><br> Annual Meeting of the Association for Computational Linguistics (ACL), 2021. (Long) <a href="https://arxiv.org/abs/2101.00063">[Paper]</a><a href="https://github.com/VITA-Group/EarlyBERT">[Code]</a> </li>
<li> J. Hong, Z. Zhu, S. Yu, Z. Wang, H. Dodge, and J. Zhou<br> <b style="color:rgb(71, 71, 71)">“Federated Adversarial Debiasing for Fair and Transferable Representations”</b><br> ACM Conference on Knowledge Discovery and Data Mining (KDD), 2021. <a href="https://dl.acm.org/doi/10.1145/3447548.3467281">[Paper]</a> <a href="https://github.com/illidanlab/FADE">[Code]</a> </li>
<li>T. Chen*, J. Frankle, S. Chang, S. Liu, Y. Zhang, M. Carbin, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“The Lottery Tickets Hypothesis for Supervised and Self-supervised Pre-training in Computer Vision Models”</b><br> IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021. <a href="https://arxiv.org/abs/2012.06908">[Paper]</a><a href="https://github.com/VITA-Group/CV_LTH_Pre-training">[Code]</a></li>
<li>Z. Wang, H. Wang*, T. Chen*, Z. Wang, and K. Ma<br> <b style="color:rgb(71, 71, 71)">“Troubleshooting Blind Image Quality Models in the Wild”</b><br> IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021. <a href="https://arxiv.org/abs/2105.06747">[Paper]</a> <a href="https://github.com/wangzhihua520/troubleshooting_BIQA">[Code]</a></li>
<li>P. Cao, Z. Wang, and K. Ma<br> <b style="color:rgb(71, 71, 71)">“Debiased Subjective Assessment of Real-World Image Enhancement”</b><br> IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021. <a href="https://openaccess.thecvf.com/content/CVPR2021/papers/Cao_Debiased_Subjective_Assessment_of_Real-World_Image_Enhancement_CVPR_2021_paper.pdf">[Paper]</a> [Code]
<li>H. Ma, T. Chen*, T. Hu*, C. You, X. Xie, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“Undistillable: Making A Nasty Teacher That CANNOT Teach Students”</b><br> International Conference on Learning Representations (ICLR), 2021. (Spotlight) <a href="https://openreview.net/forum?id=0zvfm-nZqQs">[Paper]</a><a href="https://github.com/VITA-Group/Nasty-Teacher">[Code]</a>
<li>T. Chen*, Z. Zhang*, S. Liu, S. Chang, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“Long Live the Lottery: The Existence of Winning Tickets in Lifelong Learning”</b><br> International Conference on Learning Representations (ICLR), 2021. <a href="https://openreview.net/forum?id=LXMSvPmsm0g">[Paper]</a> <a href="https://github.com/VITA-Group/Lifelong-Learning-LTH">[Code]</a>
<li>T. Chen*, Z. Zhang*, S. Liu, S. Chang, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“Robust Overfitting May be Mitigated by Properly Learned Smoothening”</b><br> International Conference on Learning Representations (ICLR), 2021. <a href="https://openreview.net/forum?id=qZzy5urZw9">[Paper]</a> <a href="https://github.com/VITA-Group/Alleviate-Robust-Overfitting">[Code]</a>
<li>W. Chen*, X. Gong*, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“Neural Architecture Search on ImageNet in Four GPU Hours: A Theoretically Inspired Perspective”</b><br> International Conference on Learning Representations (ICLR), 2021. <a href="https://openreview.net/forum?id=Cnon5ezMHtu">[Paper]</a> <a href="https://github.com/VITA-Group/TENAS">[Code]</a>
<li>W. Chen*, Z. Yu, S. Mello, S. Liu, J. Alvarez, Z. Wang, and A. Anandkumar<br> <b style="color:rgb(71, 71, 71)">“Contrastive Syn-to-Real Generalization”</b><br> International Conference on Learning Representations (ICLR), 2021. <a href="https://openreview.net/forum?id=F8whUO8HNbP">[Paper]</a> <a href=" https://github.com/NVlabs/CSG">[Code]</a>
<li>T. Meng, X. Chen*, Y. Jiang*, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“A Design Space Study for LISTA and Beyond”</b><br> International Conference on Learning Representations (ICLR), 2021. <a href="https://openreview.net/forum?id=GMgHyUPrXa">[Paper]</a> <a href="https://github.com/google-research/google-research/tree/master/lista_design_space">[Code]</a>
<li>J. Shen*, X. Chen*, H. Heaton, T. Chen*, J. Liu, W. Yin, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“Learning A Minimax Optimizer: A Pilot Study”</b><br> International Conference on Learning Representations (ICLR), 2021. <a href="https://openreview.net/forum?id=nkIDwI6oO4_">[Paper]</a> <a href="https://github.com/VITA-Group/L2O-Minimax">[Code]</a>
<li>J. Shen*, H. Wang*, S. Gui, J. Tan, Z. Wang, and J. Liu<br> <b style="color:rgb(71, 71, 71)">“UMEC: Unified Model and Embedding Compression for Efficient Recommendation Systems”</b><br> International Conference on Learning Representations (ICLR), 2021. <a href="https://openreview.net/forum?id=BM---bH_RSh">[Paper]</a> <a href="https://github.com/VITA-Group/UMEC">[Code]</a>
<li>J. Hong, H. Wang*, Z. Wang, and J. Zhou <br> <b style="color:rgb(71, 71, 71)">“Learning Model-Based Privacy Protection under Budget Constraints”</b><br> AAAI Conference on Artificial Intelligence (AAAI), 2021.<a href="https://www.aaai.org/AAAI21Papers/AAAI-7394.HongJ.pdf">[Paper]</a> [Code]
<li>T. Chen*, W. Zhang, J. Zhou, S. Chang, S. Liu, L. Amini, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“Training Stronger Baselines for Learning to Optimize”</b><br> Advances in Neural Information Processing Systems (NeurIPS), 2020. (Spotlight) <a href="https://arxiv.org/abs/2010.09089">[Paper]</a> <a href="https://github.com/VITA-Group/L2O-Training-Techniques">[Code]</a>
<li>H. Wang*, T. Chen*, S. Gui, T. Hu*, J. Liu, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“Once-for-All Adversarial Training: In-Situ Tradeoff between Robustness and Accuracy for Free”</b><br> Advances in Neural Information Processing Systems (NeurIPS), 2020. <a href="https://arxiv.org/abs/2010.11828">[Paper]</a> <a href="https://github.com/VITA-Group/Once-for-All-Adversarial-Training">[Code]</a>
<li>T. Chen*, J. Frankle, S. Chang, S. Liu, Y. Zhang, Z. Wang, and M. Carbin<br> <b style="color:rgb(71, 71, 71)">“The Lottery Ticket Hypothesis for Pre-trained BERT Networks”</b><br> Advances in Neural Information Processing Systems (NeurIPS), 2020. <a href="https://arxiv.org/abs/2007.12223">[Paper]</a> <a href="https://github.com/VITA-Group/BERT-Tickets">[Code]</a>
<li>X. Chen*, Z. Wang, S. Tang, and K. Muandet<br> <b style="color:rgb(71, 71, 71)">“MATE: Plugging in Model Awareness to Task Embedding for Meta Learning”</b><br> Advances in Neural Information Processing Systems (NeurIPS), 2020. <a href="https://proceedings.neurips.cc/paper/2020/file/8989e07fc124e7a9bcbdebcc8ace2bc0-Paper.pdf">[Paper]</a> <a href="https://github.com/VITA-Group/MATE">[Code]</a>
<li>Z. Jiang*, T. Chen*, T. Chen, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“Robust Pre-Training by Adversarial Contrastive Learning”</b><br> Advances in Neural Information Processing Systems (NeurIPS), 2020. <a href="https://arxiv.org/abs/2010.13337">[Paper]</a> <a href="https://github.com/VITA-Group/ACL_Neurips20">[Code]</a>
<li>Y. You*, T. Chen*, Y. Sui, T. Chen, Z. Wang, and Y. Shen<br> <b style="color:rgb(71, 71, 71)">“Graph Contrastive Learning with Augmentations”</b><br> Advances in Neural Information Processing Systems (NeurIPS), 2020. <a href="https://arxiv.org/abs/2010.13902">[Paper]</a> <a href="https://github.com/VITA-Group/GraphCL">[Code]</a>
<li>H. You, X. Chen*, Y. Zhang, C. Li, S. Li, Z. Liu, Z. Wang, and Y. Lin<br> <b style="color:rgb(71, 71, 71)">“ShiftAddNet: A Hardware-Inspired Deep Network”</b><br> Advances in Neural Information Processing Systems (NeurIPS), 2020. <a href="https://arxiv.org/abs/2010.12785">[Paper]</a> <a href="https://github.com/VITA-Group/ShiftAddNet">[Code]</a>
<li>Y. Fu, H. You, Y. Zhao, Y. Wang, C. Li, K. Gopalakrishnan, Z. Wang, and Y. Lin<br> <b style="color:rgb(71, 71, 71)">“FracTrain: Fractionally Squeezing Bit Savings Both Temporally and Spatially for Efficient DNN Training”</b><br> Advances in Neural Information Processing Systems (NeurIPS), 2020. <a href="https://proceedings.neurips.cc/paper/2020/file/8dc5983b8c4ef1d8fcd5f325f9a65511-Paper.pdf">[Paper]</a> <a href="https://github.com/VITA-Group/FracTrain">[Code]</a>
<li>H. Wang*, S. Gui, H. Yang, J. Liu, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“GAN Slimming: All-in-One GAN Compression by A Unified Optimization Framework”</b><br> European Conference on Computer Vision (ECCV), 2020. (Spotlight) <a href="https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490052.pdf">[Paper]</a><a href="https://github.com/VITA-Group/GAN-Slimming">[Code]</a></li>
<li>S. Yang*, Z. Wang, J. Liu, and Z. Guo<br> <b style="color:rgb(71, 71, 71)">“Deep Plastic Surgery: Robust and Controllable Image Editing with Human-Drawn Sketches”</b><br> European Conference on Computer Vision (ECCV), 2020. <a href="https://arxiv.org/abs/2001.02890">[Paper]</a> <a href="https://github.com/VITA-Group/DeepPS">[Code]</a></li>
<li>C. Li, T. Chen*, H. You, Z. Wang, and Y Lin<br> <b style="color:rgb(71, 71, 71)">“HALO: Hardware-Aware Learning to Optimize”</b><br> European Conference on Computer Vision (ECCV), 2020. <a href="http://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123540477.pdf">[Paper]</a> <a href="https://github.com/RICE-EIC/HALO">[Code]</a></li>
<li>Z. Huo, A. PakBin, X. Chen*, N. Hurley, Y. Yuan*, X. Qian, Z. Wang, S. Huang, and B. Mortazavi<br> <b style="color:rgb(71, 71, 71)">“Uncertainty Quantification for Deep Context-Aware Mobile Activity Recognition and Unknown Context Discovery”</b><br>International Conference on Artificial Intelligence and Statistics (AISTATS), 2020. <a href="https://arxiv.org/abs/2003.01753">[Paper]</a> [Code]</li>
<li>W. Chen*, Z. Yu, Z. Wang, and A. Anandkumar<br> <b style="color:rgb(71, 71, 71)">“Automated Synthetic-to-Real Generalization”</b><br> International Conference on Machine Learning (ICML), 2020. <a href="http://proceedings.mlr.press/v119/chen20x/chen20x.pdf">[Paper]</a> <a href="https://github.com/VITA-Group/ASG">[Code]</a></li>
<li>X. Chen*, W. Chen*, T. Chen*, Y. Yuan*, C. Gong, K. Chen, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“Self-PU: Self Boosted and Calibrated Positive-Unlabeled Training”</b><br> International Conference on Machine Learning (ICML), 2020. <a href="https://arxiv.org/abs/2006.11280">[Paper]</a> <a href="https://github.com/TAMU-VITA/Self-PU">[Code]</a></li>
<li>Y. You*, T. Chen*, Z. Wang, and Y. Shen <br> <b style="color:rgb(71, 71, 71)">“When Does Self-Supervision Help Graph Convolutional Networks?”</b><br> International Conference on Machine Learning (ICML), 2020. <a href="https://arxiv.org/abs/2006.09136">[Paper]</a> <a href="https://github.com/Shen-Lab/SS-GCNs">[Code]</a></li>
<li>R. Oftadeh, J. Shen*, Z. Wang, and D. Shell<br> <b style="color:rgb(71, 71, 71)">“Eliminating the Invariance on the Loss Landscape of Linear Autoencoders”</b><br> International Conference on Machine Learning (ICML), 2020. <a href="http://proceedings.mlr.press/v119/oftadeh20a/oftadeh20a.pdf">[Paper]</a> [Code]</li>
<li>Y. Fu, W. Chen*, H. Wang*, H. Li, Y. Lin, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“AutoGAN-Distiller: Searching to Compress Generative Adversarial Networks”</b><br> International Conference on Machine Learning (ICML), 2020. <a href="https://arxiv.org/abs/2006.08198">[Paper]</a> <a href="https://github.com/TAMU-VITA/AGD">[Code]</a></li>
<li>R. Ardywibowo, S. Boluki, X. Gong*, Z. Wang, and X. Qian<br> <b style="color:rgb(71, 71, 71)">“NADS: Neural Architecture Distribution Search for Uncertainty Awareness”</b><br> International Conference on Machine Learning (ICML), 2020. <a href="https://arxiv.org/abs/2006.06646">[Paper]</a> <a href="https://github.com/ardywibowo/NADS">[Code]</a></li>
<li>Y. Zhao, X. Chen*, Y. Wang, C. Li, Y. Xie, Z. Wang, and Y. Lin<br> <b style="color:rgb(71, 71, 71)">“SmartExchange: Trading Higher-cost Memory Storage/Access for Lower-cost Computation”</b><br> IEEE/ACM International Symposium on Computer Architecture (ISCA), 2020. <a href="https://arxiv.org/abs/2005.03403">[Paper]</a> [Code]</li>
<li>T. Chen*, S. Liu, S. Chang, Y. Cheng, L. Amini, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“Adversarial Robustness: From Self-Supervised Pretraining to Fine-Tuning”</b><br> IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020. <a href="https://openaccess.thecvf.com/content_CVPR_2020/papers/Chen_Adversarial_Robustness_From_Self-Supervised_Pre-Training_to_Fine-Tuning_CVPR_2020_paper.pdf">[Paper]</a> <a href="https://github.com/TAMU-VITA/Adv-SS-Pretraining">[Code]</a></li>
<li>Z. Jiang*, B. Liu, S. Schulter, Z. Wang, and M. Chandraker<br> <b style="color:rgb(71, 71, 71)">“Peek-a-boo: Occlusion Reasoning in Indoor Scenes with Plane Representations”</b><br> IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020. (Oral) <a href="https://openaccess.thecvf.com/content_CVPR_2020/papers/Jiang_Peek-a-Boo_Occlusion_Reasoning_in_Indoor_Scenes_With_Plane_Representations_CVPR_2020_paper.pdf">[Paper]</a> [Code]</li>
<li>Y. You*, T. Chen*, Z. Wang, and Y. Shen<br> <b style="color:rgb(71, 71, 71)">“L<sup>2</sup>-GCN: Layer-Wise and Learned Efficient Training of Graph Convolutional Networks”</b><br> IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020. <a href="https://arxiv.org/abs/2003.13606">[Paper]</a> <a href="https://github.com/TAMU-VITA/L2-GCN">[Code]</a></li>
<li>T. Hu*, T. Chen*, H. Wang*, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">"Triple Wins: Boosting Accuracy, Robustness and Efficiency Together by Enabling Input-Adaptive Inference"</b><br> International Conference on Learning Representations (ICLR), 2020. <a href="https://openreview.net/forum?id=rJgzzJHtDB">[Paper]</a> <a href="https://github.com/TAMU-VITA/triple-wins">[Code]</a></li>
<li>W. Chen*, X. Gong*, X. Liu, Q. Zhang, Y. Li and Z. Wang<br> <b style="color:rgb(71, 71, 71)">"FasterSeg: Searching for Faster Real-time Semantic Segmentation"</b><br> International Conference on Learning Representations (ICLR), 2020. <a href="https://openreview.net/forum?id=BJgqQ6NYvB">[Paper]</a> <a href="https://github.com/TAMU-VITA/FasterSeg">[Code]</a></li>
<li>H. Wang*, T. Chen*, Z. Wang, and K. Ma<br> <b style="color:rgb(71, 71, 71)">"I am Going MAD: Maximum Discrepancy Competition for Comparing Classifiers Adaptively"</b><br> International Conference on Learning Representations (ICLR), 2020. <a href="https://openreview.net/forum?id=rJehNT4YPr">[Paper]</a> <a href="https://github.com/TAMU-VITA/MAD">[Code]</a></li>
<li>H. You, C. Li, P. Xu, Y. Fu, Y. Wang, X. Chen*, R. Baraniuk, Z. Wang, and Y. Lin<br> <b style="color:rgb(71, 71, 71)">“Drawing Early-Bird Tickets: Toward More Efficient Training of Deep Networks"</b><br> International Conference on Learning Representations (ICLR), 2020. (Spotlight) <a href="https://openreview.net/forum?id=BJxsrgStvr">[Paper]</a> <a href="https://github.com/RICE-EIC/Early-Bird-Tickets">[Code]</a></li>
<li>J. Shen*, Y. Wang*, P. Xu, Y. Fu, Z. Wang, and Y. Lin<br> <b style="color:rgb(71, 71, 71)">“Fractional Skipping: Toward Finer-Grained Dynamic Inference”</b><br> AAAI Conference on Artificial Intelligence (AAAI), 2020. <a href="https://arxiv.org/abs/2001.00705">[Paper]</a> <a href="https://github.com/VITA-Group/DFS">[Code]</a></li>
<li>S. Mohseni*, M. Pitale, J. Yadawa, and Z. Wang<br> <b style="color:rgb(71, 71, 71)"> “Self-Supervised Learning for Generalizable Out-of-Distribution Detection”</b><br> AAAI Conference on Artificial Intelligence (AAAI), 2020. <a href="http://people.tamu.edu/~sina.mohseni/papers/Self_Supervised_Learning_for_Generalizable_Out_of_Distribution_Detection.pdf">[Paper]</a> [Code]</li>
<li>Z. Jiang*, Y. Wang*, X. Chen*, P. Xu, Y. Zhao, Y. Lin, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“E<sup>2</sup>-Train: Training State-of-the-art CNNs with Over 80% Energy Savings”</b><br> Advances in Neural Information Processing Systems (NeurIPS), 2019. <a href="https://arxiv.org/abs/1910.13349">[Paper]</a> <a href="https://github.com/RICE-EIC/E2Train">[Code]</a></li>
<li>S. Gui, H. Wang*, H. Yang, C. Yu, Z. Wang, and J. Liu<br> <b style="color:rgb(71, 71, 71)">“Model Compression with Adversarial Robustness: A Unified Optimization Framework”</b><br> Advances in Neural Information Processing Systems (NeurIPS), 2019. <a href="https://arxiv.org/abs/1902.03538">[Paper]</a> <a href="https://github.com/TAMU-VITA/ATMC">[Code]</a></li>
<li>Y. Cao, T. Chen*, Z. Wang, and Y. Shen<br> <b style="color:rgb(71, 71, 71)">“Learning to Optimize in Swarms”</b><br> Advances in Neural Information Processing Systems (NeurIPS), 2019. <a href="https://arxiv.org/abs/1911.03787">[Paper]</a> <a href="https://github.com/Shen-Lab/LOIS">[Code]</a></li>
<li>X. Jia, S. Wang*, X. Liang, A. Balagopal, D. Nguyen, M. Yang, Z. Wang, X. Qian, X. Ji, and S. Jiang<br> <b style="color:rgb(71, 71, 71)">“Cone-Beam Computed Tomography (CBCT) Segmentation by Adversarial Learning Domain Adaptation”</b><br> Medical Image Computing and Computer Assisted Interventions (MICCAI), 2019 <a href="https://link.springer.com/chapter/10.1007/978-3-030-32226-7_63">[Paper]</a> [Code]</li>
<li>R. Ardywibowo, G. Zhao, Z. Wang, B. Mortazavi, S. Huang, and X. Qian, <br> <b style="color:rgb(71, 71, 71)">“Activity Monitoring with Uncertainty Quantification in Switching Gaussian Process Models”</b><br> International Conference on Artificial Intelligence and Statistics (AISTATS), 2019 <a href="http://proceedings.mlr.press/v89/ardywibowo19a.html">[Paper]</a> [Code]</li>
<li>S. Yang*, Z. Wang, Z Wang, N. Xu, J. Liu, and Z. Guo<br> <b style="color:rgb(71, 71, 71)">“Controllable Artistic Text Style Transfer via Shape-Matching GAN”</b><br> IEEE International Conference on Computer Vision (ICCV), 2019. (Oral) <a href="https://arxiv.org/abs/1905.01354">[Paper]</a> <a href="https://github.com/TAMU-VITA/ShapeMatchingGAN">[Code]</a></li>
<li>Z. Wu*, K. Suresh, P. Narayanan, H. Xu, H. Kwon, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“Delving into Robust Object Detection from Unmanned Aerial Vehicles: A Deep Nuisance Disentanglement Approach”</b><br> IEEE International Conference on Computer Vision (ICCV), 2019. <a href="https://arxiv.org/abs/1908.03856">[Paper]</a> <a href="https://github.com/TAMU-VITA/UAV-NDFT">[Code]</a></li>
<li>X. Gong*, S. Chang, Y. Jiang*, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“AutoGAN: Neural Architecture Search for Generative Adversarial Networks”</b><br> IEEE International Conference on Computer Vision (ICCV), 2019. <a href="https://arxiv.org/abs/1908.03835">[Paper]</a> <a href="https://github.com/TAMU-VITA/AutoGAN">[Code]</a></li>
<li>T. Chen*, S. Ding, J. Xie, Y. Yuan*, W. Chen*, Y. Yang, Z. Ren, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“ABD-Net: Attentive but Diverse Person Re-Identification”</b><br> IEEE International Conference on Computer Vision (ICCV), 2019. <a href="https://arxiv.org/abs/1908.01114">[Paper]</a> <a href="https://github.com/TAMU-VITA/ABD-Net">[Code]</a></li>
<li>O. Kupyn, T. Martyniuk, J. Wu*, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“DeblurGAN-v2: Deblurring (Orders-of-Magnitude) Faster and Better”</b><br> IEEE International Conference on Computer Vision (ICCV), 2019. <a href="https://arxiv.org/abs/1908.03826">[Paper]</a> <a href="https://github.com/TAMU-VITA/DeblurGANv2">[Code]</a></li>
<li>E. Ryu, J. Liu, S. Wang*, X. Chen*, Z. Wang, and W. Yin<br> <b style="color:rgb(71, 71, 71)">“Plug-and-Play Methods Provably Converge with Properly Trained Denoisers”</b><br> International Conference on Machine Learning (ICML), 2019. <a href="https://arxiv.org/abs/1905.05406">[Paper]</a> <a href="https://github.com/TAMU-VITA/Provable_Plug_and_Play">[Code]</a></li>
<li>W. Chen*, Z. Jiang*, Z. Wang, K. Cui, and X. Qian<br> <b style="color:rgb(71, 71, 71)">“Collaborative Global-Local Networks for Memory-Efficient Segmentation of Ultra-high Resolution Images”</b><br> IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019. (Oral) <a href="https://arxiv.org/abs/1905.06368">[Paper]</a> <a href="https://github.com/TAMU-VITA/GLNet">[Code]</a></li>
<li>S. Li, I. B. Araujo*, W. Ren, Z. Wang, E. K. Tokuda*, R. Hirata, R. Cesar, J. Zhang, X. Guo, and X. Cao<br> <b style="color:rgb(71, 71, 71)">“Single Image Deraining: A Comprehensive Benchmark Analysis”</b><br> IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019. <a href="https://arxiv.org/abs/1903.08558">[Paper]</a> <a href="https://github.com/lsy17096535/Single-Image-Deraining">[Code]</a></li>
<li>J. Liu, X. Chen*, Z. Wang, and W. Yin<br> <b style="color:rgb(71, 71, 71)">“ALISTA: Analytic Weights Are As Good As Learned Weights in LISTA”</b><br> International Conference on Learning Representations (ICLR), 2019. <a href="https://openreview.net/forum?id=B1lnzn0ctQ">[Paper]</a> <a href="https://github.com/TAMU-VITA/ALISTA">[Code]</a></li>
<li>X. Chen*, J. Liu, Z. Wang, and W. Yin<br> <b style="color:rgb(71, 71, 71)">“Theoretical Linear Convergence of Unfolded ISTA and Its Practical Weights and Thresholds”</b><br> Advances in Neural Information Processing Systems (NeurIPS), 2018. (Spotlight) <a href="http://papers.nips.cc/paper/8120-theoretical-linear-convergence-of-unfolded-ista-and-its-practical-weights-and-thresholds">[Paper]</a> <a href="https://github.com/TAMU-VITA/LISTA-CPSS">[Code]</a></li>
<li>N. Bansal*, X. Chen*, and Z. Wang<br> <b style="color:rgb(71, 71, 71)">“Can We Gain More from Orthogonality Regularizations in Training Deep CNNs?”</b><br> Advances in Neural Information Processing Systems (NeurIPS), 2018. <a href="http://papers.nips.cc/paper/7680-can-we-gain-more-from-orthogonality-regularizations-in-training-deep-networks">[Paper]</a> <a href="https://github.com/TAMU-VITA/Orthogonality-in-CNNs">[Code]</a></li>
<li>Z. Wu*, Z. Wang, Z. Wang, and H. Jin<br> <b style="color:rgb(71, 71, 71)">“Towards Privacy-Preserving Visual Recognition via Adversarial Training: A Pilot Study”</b><br> European Conference on Computer Vision (ECCV), 2018. <a href="https://openaccess.thecvf.com/content_ECCV_2018/html/Zhenyu_Wu_Towards_Privacy-Preserving_Visual_ECCV_2018_paper.html">[Paper]</a> <a href="https://github.com/TAMU-VITA/Privacy-AdversarialLearning">[Code]</a></li>
<li>M. Sun, I. Baytas, L. Zhan, Z. Wang, and J. Zhou<br> <b style="color:rgb(71, 71, 71)">“Subspace Network: Deep Multi-Task Censored Regression for Modeling Neurodegenerative Diseases”</b><br> ACM Conference on Knowledge Discovery and Data Mining (KDD), 2018. <a href="https://dl.acm.org/doi/abs/10.1145/3219819.3219966">[Paper]</a> <a href="https://github.com/illidanlab/subspace-net">[Code]</a></li>
<li>J. Wu*, Y. Wang*, Z. Wu*, Z. Wang, A. Veeraraghavan, and Y. Lin<br> <b style="color:rgb(71, 71, 71)">“Deep k-Means: Re-Training and Parameter Sharing with Harder Cluster Assignments for Compressing Deep Convolutions”</b><br> International Conference on Machine Learning (ICML), 2018. <a href="http://proceedings.mlr.press/v80/wu18h.html">[Paper]</a> <a href="https://github.com/TAMU-VITA/Deep-K-Means-pytorch">[Code]</a></li>
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