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Chelsea Finn

@chelseabfinn

20 Jul

@mike_h_wu

Thrilled to share new work on AI for education: can we give detailed, high-quality feedback to students?

Post: ai.stanford.edu/blog/prototran…
NYT Coverage: nytimes.com/2021/07/20/tec…

A collab w. the amazing @mike_h_wu @chrispiech & co 🧵

2/ Student feedback is a fundamental problem in scaling education.

Providing good feedback is hard: existing approaches provide canned responses, cryptic error messages, or simply provide the answer.

3/ Providing feedback is also hard for ML: not a ton of data, teachers frequently change their assignments, and student solutions are open-ended and long-tailed.

Supervised learning doesn’t work. We weren’t sure if this problem can even be solved using ML.

Read 12 tweets

Chelsea Finn

@chelseabfinn

1 Apr

@_anniechen_

How can robots generalize to new environments & tasks?

We find that using in-the-wild videos of people can allow learned reward functions to do so!
Paper: arxiv.org/abs/2103.16817

Led by @_anniechen_, @SurajNair_1
🧵(1/5)

To get reward functions that generalize, we train domain-agnostic video discriminators (DVD) with:
* a lot of diverse human data, and
* a narrow & small amount of robot demos

The idea is super simple: predict if two videos are performing the same task or not.
(2/5)

This discriminator can be used as a reward by feeding in a human video of the desired task and a video of the robot’s behavior.

We use it by planning with a learned visual dynamics model.
(3/5)

Read 5 tweets

Chelsea Finn

@chelseabfinn

8 Jul 20

@allan_zhou1

Convolution is an example of structure we build into neural nets. Can we _discover_ convolutions & other symmetries from data?

Excited to introduce:
Meta-Learning Symmetries by Reparameterization
arxiv.org/abs/2007.02933

w/ @allan_zhou1 @TensorProduct @StanfordAILab
Thread👇

To think about this question, we first look at how equivariances are represented in neural nets.

They can be seen as certain weight-sharing & weight-sparsity patterns. For example, consider convolutions.
(2/8)

We reparametrize a weight matrix into a sharing matrix & underlying filter parameters

It turns out this can provably represent any equivariant structure + filter parameters, for all group-equivariant convolutions with finite groups.
(3/8)

Read 8 tweets

Chelsea Finn

@chelseabfinn

7 Jul 20

@abhishekunique7

Supervised ML methods (i.e. ERM) assume that train & test data are from the same distribution, & deteriorate when this assumption is broken.

To help, we introduce adaptive risk minimization (ARM):
arxiv.org/abs/2007.02931

With M Zhang, H Marklund @abhishekunique7 @svlevine
(1/6)

Prior works on dIstributionally-robust optimization (DRO) aim to be _robust_ to distribution shift.

Group DRO aims for robustness to shifts in groups underlying the dataset. (e.g. see arxiv.org/abs/1611.02041)
(2/6)

Recently, this paper showed promising results on group DRO with neural nets:
arxiv.org/abs/1911.08731

However, DRO methods often trade-off between robustness & test-time performance.
(3/6)

Read 6 tweets

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