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05/28/2023
The Statistical Benefits of Quantile Temporal-Difference Learning for Value Estimation
We study the problem of temporal-difference-based policy evaluation in r...
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03/02/2023
Understanding plasticity in neural networks
Plasticity, the ability of a neural network to quickly change its predic...
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12/11/2022
Generalization Through the Lens of Learning Dynamics
A machine learning (ML) system must learn not only to match the output o...
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12/06/2022
Understanding Self-Predictive Learning for Reinforcement Learning
We study the learning dynamics of self-predictive learning for reinforce...
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06/05/2022
Learning Dynamics and Generalization in Reinforcement Learning
Solving a reinforcement learning (RL) problem poses two competing challe...
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04/20/2022
Understanding and Preventing Capacity Loss in Reinforcement Learning
The reinforcement learning (RL) problem is rife with sources of non-stat...
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12/24/2021
DARTS without a Validation Set: Optimizing the Marginal Likelihood
The success of neural architecture search (NAS) has historically been li...
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06/04/2021
Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning
We challenge a common assumption underlying most supervised deep learnin...
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05/19/2021
Provable Guarantees on the Robustness of Decision Rules to Causal Interventions
Robustness of decision rules to shifts in the data-generating process is...
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03/10/2021
Robustness to Pruning Predicts Generalization in Deep Neural Networks
Existing generalization measures that aim to capture a model's simplicit...
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02/25/2021
On The Effect of Auxiliary Tasks on Representation Dynamics
While auxiliary tasks play a key role in shaping the representations lea...
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02/24/2021
PsiPhi-Learning: Reinforcement Learning with Demonstrations using Successor Features and Inverse Temporal Difference Learning
We study reinforcement learning (RL) with no-reward demonstrations, a se...
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10/27/2020
A Bayesian Perspective on Training Speed and Model Selection
We take a Bayesian perspective to illustrate a connection between traini...
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06/08/2020
Revisiting the Train Loss: an Efficient Performance Estimator for Neural Architecture Search
Reliable yet efficient evaluation of generalisation performance of a pro...
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05/01/2020
On the Benefits of Invariance in Neural Networks
Many real world data analysis problems exhibit invariant structure, and ...
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03/27/2020
Unpacking Information Bottlenecks: Unifying Information-Theoretic Objectives in Deep Learning
The information bottleneck (IB) principle offers both a mechanism to exp...
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03/12/2020
Invariant Causal Prediction for Block MDPs
Generalization across environments is critical to the successful applica...
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01/31/2019
A Geometric Perspective on Optimal Representations for Reinforcement Learning
This paper proposes a new approach to representation learning based on g...
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01/30/2019
A Comparative Analysis of Expected and Distributional Reinforcement Learning
Since their introduction a year ago, distributional approaches to reinfo...
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05/13/2018
GAN Q-learning
Distributional reinforcement learning (distributional RL) has seen empir...
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02/20/2018
