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<!DOCTYPE html>
<html>
<head>
<title>L2M PI 21</title>
<meta http-equiv="Content-Type" content="text/html; charset=UTF-8" />
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<script type="text/javascript" src="assets/plotly/plotly-latest.min.js"></script>
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<textarea id="source">
### Lifelong Learning: <br>Theory and Context
PI: Joshua T. Vogelstein, [JHU](https://www.jhu.edu/) <br>
Co-PI: Vova Braverman, [JHU](https://www.jhu.edu/) <br>
Ali Geisa, Jayanta Dey, Will LeVine, Hayden Helm, Ronak Mehta,
Carey E. Priebe
<!-- | Joshua T. Vogelstein <br> -->
<!-- [Microsoft Research](https://www.microsoft.com/en-us/research/): Weiwei Yang | Jonathan Larson | Bryan Tower | Chris White -->
---
### Outline
- background
- theoretically motivate lifelong learning metrics
- properly situation lifelong learning within hierarchy of learning paradigms
---
class: middle
# .center[Background]
---
### What is learning (Valiant)?
basically, doing better than chance with enough data
basically, doing arbitrarily well with enough data
.ye[weak learning theorem states that if a problem is weakly learnable, then it is also strongly learnable]
---
### Limitations of this formal definition
- there is only 1 task
- requires large sample sizes for theory to be relevant
- all data are from the same fixed distribution
- evaluation is with respect to the data distribution
---
### What is learning (Mitchell)?
A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.
-- Tom Mitchell, 1997
- Pro's
- multiple tasks
- uncouples experience (data) with tasks
- explicit mention of improving due to data
- implicitly requires transfer
- Con's
- not formalized
---
class: middle
# .center[Jovo Framework]
---
##### In-Distribution vs Out-of-Distribution Learning
- the key differences
- evaluation distribution is uncoupled from data distributions
- multiple datasets & distributions
---
### Formalizing OOD Learnability
basically, using non-task data to improve performance at all
basically, using non-task data to perform arbitrarily well
---
### Quantifying learning
The above two definitions enable one to assess .ye[whether] an agent $f$ has learned, but not .ye[how much] it learned.
basically, using non-task data to improve performance over what it could achieve using only task data
---
### Weak OOD Learner Theorem
Theorem 1: With *only* out-of-distribution data, there exists some problems that are weakly, but not strongly, learnable.
This implies that OOD learning is different *in kind* (and .ye[harder]) from in-distribution learning.
<!-- - Lifelong learning is a special case of OOD learning. -->
---
### Transfer Learning Theorem
Theorem 2: Weak OOD learnability implies transfer learnability (i.e., learning efficiency > 1). That is, if one can weakly learn, one can also transfer learn, but not necessarily vice versa.
- This implies that transfer learnability is a fundamental property of learning problems
- In other words, inability to transfer is equivalent to inability to learn at all.
If one cannot transfer, one cannot learn in any meaningful sense.
---
### Learning Efficiency Applications
Each of the previous definitions are all special cases of $LE^t_f(\mathbf{S}^A, \mathbf{S}^B)$, for specific choices of $\mathbf{S}^A$ and $\mathbf{S}^B$
- Learning: $\mathbf{S}^A=\mathbf{S}\_0$ and $\mathbf{S}^B=\mathbf{S}\_n$.
- Transfer learning: $\mathbf{S}^A=\mathbf{S}\_n^t$ and $\mathbf{S}^B=\mathbf{S}\_n$.
- Multitask learning: for each $t$, $\mathbf{S}^A=\mathbf{S}\_n^t$ and $\mathbf{S}^B=\mathbf{S}\_n$.
- Forward learning: $\mathbf{S}^A=\mathbf{S}\_n^t$ and $\mathbf{S}^B=\mathbf{S}\_n^{< t}$.
- Backward learning: $\mathbf{S}^A=\mathbf{S}\_n^{< t}$ and $\mathbf{S}^B=\mathbf{S}\_n$.
---
### Lifelong Learning $\subsetneq$ OOD learning
---
### Biological learning is on top
---
### Discussion
- unified definition and quantification of learning
- presented hierarchy of learning paradigms
- limitations of current framework: in biology, there are no tasks
---
### Transition Opportunities
### [http://proglearn.neurodata.io/](http://proglearn.neurodata.io/)
- code continues to improve (no time to discuss here)
- ensembling representations (rather than decision rules) continues to be a promising path to solving OOD (including lifelong) and eventually biological learning
---
### Acknowledgements
<!-- <div class="small-container">
<img src="faces/ebridge.jpg"/>
<div class="centered">Eric Bridgeford</div>
</div>
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<div class="centered">Ben Pedigo</div>
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<div class="centered">Jaewon Chung</div>
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<div class="centered">yummy</div>
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##### JHU
<div class="small-container">
<img src="faces/cep.png"/>
<div class="centered">Carey Priebe</div>
</div>
<!-- <div class="small-container">
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</div> -->
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<div class="centered">Drishti Mannan</div>
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<div class="centered">Jesse Patsolic</div>
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<div class="centered">Benjamin Falk</div>
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<div class="centered">Kwame Kutten</div>
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<!-- <div class="small-container">
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<div class="centered">Eric Perlman</div>
</div> -->
<!-- <div class="small-container">
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<div class="centered">Alex Loftus</div>
</div> -->
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<div class="centered">Brian Caffo</div>
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<div class="centered">Minh Tang</div>
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<div class="centered">Avanti Athreya</div>
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<!-- <div class="small-container">
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<div class="centered">Vince Lyzinski</div>
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<div class="centered">Daniel Sussman</div>
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<!-- <div class="small-container">
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<div class="centered">Youngser Park</div>
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<!-- <div class="small-container">
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<div class="centered">Shangsi Wang</div>
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<!-- <div class="small-container">
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<div class="centered">Tyler Tomita</div>
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<!-- <div class="small-container">
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<div class="centered">James Brown</div>
</div> -->
<!-- <div class="small-container">
<img src="faces/disa.jpg"/>
<div class="centered">Disa Mhembere</div>
</div> -->
<!-- <div class="small-container">
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<div class="centered">Greg Kiar</div>
</div> -->
<!-- <div class="small-container">
<img src="faces/jeremias.png"/>
<div class="centered">Jeremias Sulam</div>
</div> -->
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<img src="faces/meghana.png"/>
<div class="centered">Meghana Madhya</div>
</div>
<!-- <div class="small-container">
<img src="faces/percy.png"/>
<div class="centered">Percy Li</div>
</div>
-->
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<img src="faces/ronak.jpg"/>
<div class="centered">Ronak Mehta</div>
</div>
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<img src="faces/jayanta.jpg"/>
<div class="centered">Jayanta Dey</div>
</div>
<div class="small-container">
<img src="faces/will.jpg"/>
<div class="centered">Will LeVine</div>
</div>
<div class="small-container">
<img src="faces/hayden.png"/>
<div class="centered">Hayden Helm</div>
</div>
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<img src="faces/rguo.jpg"/>
<div class="centered">Richard Gou</div>
</div>
<div class="small-container">
<img src="faces/alig.jpg"/>
<div class="centered">Ali Geisa</div>
</div>
##### Microsoft Research
<div class="small-container">
<img src="faces/chwh-180x180.jpg"/>
<div class="centered">Chris White</div>
</div>
<div class="small-container">
<img src="faces/weiwei.jpg"/>
<div class="centered">Weiwei Yang</div>
</div>
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<img src="faces/jolarso150px.png"/>
<div class="centered">Jonathan Larson</div>
</div>
<div class="small-container">
<img src="faces/brtower-180x180.jpg"/>
<div class="centered">Bryan Tower</div>
</div>
##### DARPA L2M: All code open source and reproducible from [proglearn.neurodata.io/](http://proglearn.neurodata.io/)
<!-- Hava, Ben, Robert, Jennifer, Ted. -->
{[BME](https://www.bme.jhu.edu/),[CIS](http://cis.jhu.edu/), [ICM](https://icm.jhu.edu/), [KNDI](http://kavlijhu.org/)}@[JHU](https://www.jhu.edu/) | [neurodata](https://neurodata.io)
<br>
[jovo@jhu.edu](mailto:[email protected]) | <http://neurodata.io/talks> | [@neuro_data](https://twitter.com/neuro_data)
</div>
<!-- <img src="images/funding/nsf_fpo.png" STYLE="HEIGHT:95px;"/> -->
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---
background-image: url(images/l_and_v.jpeg)
.footnote[Questions?]
</textarea>
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