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<!DOCTYPE html>
<html>
<head>
<title>Learning</title>
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<textarea id="source">
### Lifelong Learning: <br>Theory and Practice and Coresets
PI: Joshua T. Vogelstein, [JHU](https://www.jhu.edu/) <br>
Co-PI: Vova Braverman, [JHU](https://www.jhu.edu/) <br>
Jayanta Dey, Will LeVine, Hayden Helm, Ali Geisa, 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 -->
---
#### Conceptual Details
- Inputs: training (X,Y) pairs per task
- Outputs: proglearn forest/network
- Assumptions:
- X is d-dimensional feature vector
- Y is categorical
- Task-aware
- Data are provided in batches per task
- Open research questions: generalize to streaming/RL settings
#### Software details
- Python 3.6+,
- Documented, with tutorials
- Dependencies: keras, scikit-learn, scipy, numpy, joblib
- Also includes several random forest improvements over existing python implementations
- Also can perform federated learning
---
### [http://proglearn.neurodata.io/](http://proglearn.neurodata.io/)
---
### Sharable Concept
A key sharable component is an .ye[idea]: one can learn representations using any approach you want, for any task you want.
As long as each task has the same input and output space, disparate representations can be ensembled together, meaning you can push the test input through each representer, and take the average, which will be better than any on their own if the tasks are sufficiently similar.
This generalizes classical ensembling, which averages the output of each algorithm, here we are .ye[ensembling the learned internal representation] of each algorithm.
So any lifelong learning algorithm approach developed here can be ensembled with any other approach, assuming they are both using the same inputs and outputs.
This works for classification & reinforcement learning (we suspect).
---
---
### Composable Hypotheses
.center[ .ye[$h(\cdot) := w \circ v \circ u (\cdot) = w(v(u(\cdot)))$]]
- Let $u$ be .ye[representer] data to a new representation,
$$ u : \mathcal{Q} \to \tilde{\mathcal{Q}}$$
- Let $v$ be .ye[voter] which operate on the transformed data outputs votes (score functions, posteriors) on all possible actions
$$ v : \tilde{\mathcal{Q}} \to \mathcal{V}$$
- Let $w$ be .ye[decider] which decides which actions to take on the basis of the votes
$$ w : \mathcal{V} \to \mathcal{A}$$
---
<!-- TODO@ali: can we use an svg here? or a higher res png if you can't get a vector graphic? -->
---
### Simple Examples
- Linear Discriminant Analysis (shallow)
- $u$: projection onto a line
- $v$: fraction of points per over/under threshold
- $w$: maximum a posteriori class
--
- Decision Tree (deep)
- $u$: union of polytopes
- $v$: fraction of points per class per leaf node
- $w$: maximum a posteriori class
---
### Predictive Ensembling
- Ensemble votes from multiple voters in a decider
$$
w \circ
\begin{bmatrix}
v_1 \circ u_1 \\\\
v_2 \circ u_2 \\\\
\vdots \\\\
v_m \circ u_m
\end{bmatrix}
$$
---
---
#### Predictive Ensembling Example
- Decision Forest
- $u_b$ for $B$ trees: union of overlapping polytopes
- $v_b$ for $B$ trees: fraction of points per class per leaf node
- $w$: maximum a posteriori class averaging over trees
---
### Progressive Learning
- .ye[Different transformers can composed with voters]
- Learn many different transformers $u_t(\cdot)$'s
- For each $u\_t$, learn voter per task $v\_{t,t'}$'s
- Use the decider to weight the various options
- This is .ye[ensembling representations].
### Notes
- We learn new representation for each task.
- Dimensionality of internal representation grows linearly with number of tasks.
---
### Representational Ensembling
- Ensemble representations from multiple transformers in a voter
- Assume $m$ transformers and $n$ voters
- Let $u =
\begin{bmatrix}
u_1 \\\\
u_2 \\\\
\vdots \\\\
u_m
\end{bmatrix}$, and
$
w \circ
\begin{bmatrix}
v_1 \circ u \\\\
v_2 \circ u \\\\
\vdots \\\\
v_n \circ u
\end{bmatrix}
$
---
---
#### Representational Ensembling Examples
- Uncertainty Forests
- $u$: tree structures
- $v$: posterior estimators
- $w$: max
- Deep Nets
- $u$: "backbone" (all but last layer)
- $v$: softmax layer
- $w$: max
---
### Acknowledgements
<!-- <div class="small-container">
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##### JHU
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<div class="centered">Meghana Madhya</div>
</div>
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-->
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<div class="centered">Richard Gou</div>
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##### Microsoft Research
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</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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