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Welcome to our reading group!
This reading group was inspired by the SE reading group at CMU led by Christian Kaestner and Gabriel Ferreira and ML reading group at Imperial, and we just want to replicate a similar experience at USC.
The paper reading group meets weekly during the semester to discuss papers in the areas of artificial intelligence, machine learning, systems, and software engineering. Participation is open to all, guests are always welcome; if you are interested in receiving invitations contact the organizer.
Each week we will discuss a different paper. The paper to discuss is announced about one week in advance by the organizer. All participants are expected to read the paper before the meeting. It is recommended to take notes about insights, questions, and other points potentially worth discussing.
The goals of the reading group are:
- Critical reflection on scientific work
- Practice of reading and argumentation strategies
- Exposure to a broad range of research topics
- Practice of leading group discussions
The discussion is limited to one hour. The discussion is lead by a moderator, who may also set a focus for the discussion. The moderator will kick off the meeting by giving a short summary of the paper and raising a few points for discussion. The moderator should try to incorporate all participants into the discussion. The moderator role rotates through all participants. The moderator is encouraged to help with the selection of a paper that week.
Time and location: Monday 11:00 am in CSE Department, RM 2265.
- Organizer: Pooyan Jamshidi ([email protected])
- Contact person: Jianhai Su ([email protected])
Subscribe for announcements:
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mailing list:
[email protected]by sending an email to the organizer.
“Fundamental Tradeoffs between Invariance and Sensitivity to Adversarial Perturbations”. Florian Tramèr, Jens Behrmann, Nicholas Carlini, Nicolas Papernot, Jörn-Henrik Jacobsen. Moderator: Ying
“Invariant Risk Minimization”. Martin Arjovsky, L´eon Bottou, Ishaan Gulrajani, David Lopez-Paz. (Continue) Moderator: Mohammad
"The Three Pillars of Machine Programming". Justin Gottschlich, Armando Solar-Lezama, Nesime Tatbul, et al. Moderator: Pooyan
“Invariant Risk Minimization”. Martin Arjovsky, L´eon Bottou, Ishaan Gulrajani, David Lopez-Paz. Moderator: Mohammad
“A Programmable Approach to Model Compression”. Vinu Joseph, Saurav Muralidharan, Animesh Garg, Michael Garland, Ganesh Gopalakrishnan. Moderator: Shahriar
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. "Deep Learning: Chapter 12, section 1.4 to section 1.6" MIT press, 2016. Moderator: Jianhai
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. "Deep Learning: Chapter 12, section 1.1 to section 1.3" MIT press, 2016. Moderator: Bharat
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. "Deep Learning: Chapter 11.5 onwards" MIT press, 2016. Moderator: Bharat
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. "Deep Learning: Chapter 11 onwards" MIT press, 2016. Moderator: Shahriar
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. "Deep Learning: Chapter 9.10 onwards" MIT press, 2016. Moderator: Ying
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. "Deep Learning: Chapter 9.6 onwards" MIT press, 2016. Moderator: Jianhai
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. "Deep Learning: Chapter 9.4 onwards" MIT press, 2016. Moderator: Shahriar
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. "Deep Learning: Chapter 9 onwards" MIT press, 2016. Moderator: Bharat
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. "Deep Learning: Section 8.7 onwards" MIT press, 2016. Moderator: Shahriar
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. "Deep Learning: Section 8.6 onwards" MIT press, 2016. Moderator: Jianhai
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. "Deep Learning: Section 8.4 onwards" MIT press, 2016. Moderator: Shahriar
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. "Deep Learning: Section 8.3 onwards" MIT press, 2016. Moderator: Ahmed
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. "Deep Learning: Section 7.9 onwards" MIT press, 2016. Moderator: Chris
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. "Deep Learning: Section 7.6 onwards (Semi-supervised, multi-task learning, early stopping)" MIT press, 2016. Moderator: Dr. Jamshidi
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. "Deep Learning: chapter 7.2 - 7.5" MIT press, 2016. Moderator: Shahriar
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. "Deep Learning: chapter 7" MIT press, 2016. Moderator: Ahmed
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. "Deep Learning: chapter 6" MIT press, 2016. Moderator: Ying
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. "Deep Learning: chapter 6" MIT press, 2016. Moderator: Ying
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. "Deep Learning: chapter 6" MIT press, 2016. Moderator: Ying
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. "Deep Learning: chapter 5 (ML basics)" MIT press, 2016. Moderator: Joshua
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. "Deep Learning: chapter 5 (ML basics)" MIT press, 2016. Moderator: Nawras
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. "Deep Learning: chapter 5 (ML basics)" MIT press, 2016. Moderator: Chrisogonas
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. "Deep Learning: chapter 5 (ML basics)" MIT press, 2016. Moderator: Chrisogonas
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. "Deep Learning: chapter 3 (Probability and Information Theory)" MIT press, 2016. Moderator: Shahriar
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. "Deep Learning: chapter 2 (Linear Algebra)" MIT press, 2016. Moderator: Jianhai
Neapolitan, Richard E. ["Learning bayesian networks: chapter 8 (Sections 8.1-8.2)"] Moderator: Marco
Borchani, Hanen, Ana M. Martínez, Andrés R. Masegosa, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón, Antonio Fernández, Anders L. Madsen, and Ramón Sáez. "Modeling concept drift: A probabilistic graphical model based approach." In International Symposium on Intelligent Data Analysis, pp. 72-83. Springer, Cham, 2015. Moderator: Emad
Sen, Rajat, Kirthevasan Kandasamy, and Sanjay Shakkottai. "Noisy Blackbox Optimization using Multi-fidelity Queries: A Tree Search Approach." In The 22nd International Conference on Artificial Intelligence and Statistics (AIStats), pp. 2096-2105. 2019. Moderator: Jianhai
Van Seijen, Harm, Mehdi Fatemi, Joshua Romoff, Romain Laroche, Tavian Barnes, and Jeffrey Tsang. "Hybrid reward architecture for reinforcement learning." In Advances in Neural Information Processing Systems, pp. 5392-5402. 2017. Moderator: Yuxiang
Sciuto, Christian, Kaicheng Yu, Martin Jaggi, Claudiu Musat, and Mathieu Salzmann. "Evaluating the Search Phase of Neural Architecture Search." arXiv preprint arXiv:1902.08142 (2019). Moderator: Rui
Kim, Jinhan, Robert Feldt, and Shin Yoo. "Guiding deep learning system testing using surprise adequacy." Proceedings of the 41th International Conference on Software Engineering (ICSE), 2019. Moderator: Ying
Pearl, Judea. "The seven tools of causal inference, with reflections on machine learning." Communications of the ACM 62, no. 3 (2019): 54-60. Moderator: Marco Valtorta
Baker, Bowen, Otkrist Gupta, Nikhil Naik, and Ramesh Raskar. "Designing neural network architectures using reinforcement learning." International Conference on Learning Representations (ICLR), 2017. Moderator: Jianhai Su
Majumdar, Abhinandan, Leonardo Piga, Indrani Paul, Joseph L. Greathouse, Wei Huang, and David H. Albonesi. "Dynamic gpgpu power management using adaptive model predictive control." In 2017 IEEE International Symposium on High Performance Computer Architecture (HPCA), pp. 613-624. IEEE, 2017. Moderator: Shahriar
Mammadli, Rahim, Felix Wolf, and Ali Jannesari. "The Art of Getting Deep Neural Networks in Shape." ACM Transactions on Architecture and Code Optimization (TACO) 15, no. 4 (2019): 62. Moderator: RUI XIN
Han, Xue, Tingting Yu, and David Lo. "PerfLearner: learning from bug reports to understand and generate performance test frames" In Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering, pp. 17-28. ACM, 2018. Moderator: Yang Ren
Machalica, Mateusz, Alex Samylkin, Meredith Porth, and Satish Chandra. "Predictive Test Selection" In ICSE 2019. Moderator: Alireza
Zhao, Yanpeng, Yetian Chen, Kewei Tu, and Jin Tian. "Curriculum learning of Bayesian network structures" In Asian Conference on Machine Learning, pp. 269-284. 2016. Moderator: Mohammad Ali Javidian
Ardelean, Dan, Amer Diwan, and Chandra Erdman. "Performance Analysis of Cloud Applications" In 15th USENIX Symposium on Networked Systems Design and Implementation (NSDI 18). USENIX Association, 2018. Moderator: Jianhai Su
Machado, Marlos C., Marc G. Bellemare, Erik Talvitie, Joel Veness, Matthew Hausknecht, and Michael Bowling. "Revisiting the arcade learning environment: Evaluation protocols and open problems for general agents" Journal of Artificial Intelligence Research 61 (2018): 523-562. Moderator: Yuxiang Sun
Liam Li, Kevin Jamieson, Afshin Rostamizadeh, Ekaterina Gonina, Moritz Hardt, Benjamin Recht, and Ameet Talwalkar. "Massively Parallel Hyperparameter Tuning" Moderator: Shahriar
Pearl, Judea, and Elias Bareinboim. "Transportability of causal and statistical relations: A formal approach." In Proceedings of the 25th AAAI Conference on Artificial Intelligence, 2011. Moderator: Mohammad Ali Javidian
Adnan Darwiche. 2018. Human-level intelligence or animal-like abilities?. Commun. ACM 61, 10 (September 2018), 56-67. Moderator: Marco Valtorta
Shu Wang, Chi Li, Henry Hoffmann, Shan Lu, William Sentosa, Achmad Imam Kistijantoro. "Understanding and Auto-Adjusting Performance-Sensitive Configurations." Moderator: YANG REN
Venkataraman, Shivaram, Zongheng Yang, Michael J. Franklin, Benjamin Recht, and Ion Stoica. "Ernest: Efficient Performance Prediction for Large-Scale Advanced Analytics." In NSDI, pp. 363-378. 2016. Moderator: Edward Tsien
Negrinho, Renato, and Geoff Gordon. "Deeparchitect: Automatically designing and training deep architectures." arXiv preprint arXiv:1704.08792, 2017. Moderator: Rui Xin
Pei, Kexin, Yinzhi Cao, Junfeng Yang, and Suman Jana. "Deepxplore: Automated whitebox testing of deep learning systems." In Proceedings of the 26th Symposium on Operating Systems Principles (SOSP), pp. 1-18. ACM, 2017. Moderator: Hussein Almulla
Hangl, Simon, Sebastian Stabinger, and Justus Piater. "Autonomous skill-centric testing using deep learning." In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 95-102. IEEE, 2017. Moderator: Jason O'Kane
Mnih, Volodymyr, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves et al. "Human-level control through deep reinforcement learning." Nature 518, no. 7540 (2015): 529. Moderator: Yuxiang Sun
Blasi, Arianna, Alberto Goffi, Konstantin Kuznetsov, Alessandra Gorla, Michael D. Ernst, Mauro Pezzè, and Sergio Delgado Castellanos. "Translating code comments to procedure specifications" In Proceedings of the 27th ACM SIGSOFT International Symposium on Software Testing and Analysis, pp. 242-253. ACM, 2018. Moderator: Gregory Gay
Brown, David Bingham, Michael Vaughn, Ben Liblit, and Thomas Reps. "The care and feeding of wild-caught mutants." In Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering, pp. 511-522. ACM, 2017. Moderator: Alireza Salahirad
Mishra, Nikita, Connor Imes, John D. Lafferty, and Henry Hoffmann. "CALOREE: learning control for predictable latency and low energy." In Proceedings of the Twenty-Third International Conference on Architectural Support for Programming Languages and Operating Systems, pp. 184-198. ACM, 2018. Moderator: Shahriar
Gunawi, Haryadi S., Mingzhe Hao, Tanakorn Leesatapornwongsa, Tiratat Patana-anake, Thanh Do, Jeffry Adityatama, Kurnia J. Eliazar et al. "What bugs live in the cloud? a study of 3000+ issues in cloud systems." In Proceedings of the ACM Symposium on Cloud Computing, pp. 1-14. ACM, 2014. Moderator: Yang