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06/23/2023
GKD: Generalized Knowledge Distillation for Auto-regressive Sequence Models
Knowledge distillation is commonly used for compressing neural networks ...
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06/16/2023
Bootstrapped Representations in Reinforcement Learning
In reinforcement learning (RL), state representations are key to dealing...
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05/30/2023
Bigger, Better, Faster: Human-level Atari with human-level efficiency
We introduce a value-based RL agent, which we call BBF, that achieves su...
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04/25/2023
Proto-Value Networks: Scaling Representation Learning with Auxiliary Tasks
Auxiliary tasks improve the representations learned by deep reinforcemen...
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02/24/2023
The Dormant Neuron Phenomenon in Deep Reinforcement Learning
In this work we identify the dormant neuron phenomenon in deep reinforce...
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01/31/2023
Revisiting Bellman Errors for Offline Model Selection
Offline model selection (OMS), that is, choosing the best policy from a ...
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12/08/2022
A Novel Stochastic Gradient Descent Algorithm for Learning Principal Subspaces
Many machine learning problems encode their data as a matrix with a poss...
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11/28/2022
Offline Q-Learning on Diverse Multi-Task Data Both Scales And Generalizes
The potential of offline reinforcement learning (RL) is that high-capaci...
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06/03/2022
Beyond Tabula Rasa: Reincarnating Reinforcement Learning
Learning tabula rasa, that is without any prior knowledge, is the preval...
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03/01/2022
On the Generalization of Representations in Reinforcement Learning
In reinforcement learning, state representations are used to tractably d...
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12/09/2021
DR3: Value-Based Deep Reinforcement Learning Requires Explicit Regularization
Despite overparameterization, deep networks trained via supervised learn...
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08/30/2021
Deep Reinforcement Learning at the Edge of the Statistical Precipice
Deep reinforcement learning (RL) algorithms are predominantly evaluated ...
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06/06/2021
Control-Oriented Model-Based Reinforcement Learning with Implicit Differentiation
The shortcomings of maximum likelihood estimation in the context of mode...
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01/13/2021
Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning
Reinforcement learning methods trained on few environments rarely learn ...
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10/27/2020
Implicit Under-Parameterization Inhibits Data-Efficient Deep Reinforcement Learning
We identify an implicit under-parameterization phenomenon in value-based...
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07/21/2020
IITK at SemEval-2020 Task 10: Transformers for Emphasis Selection
This paper describes the system proposed for addressing the research pro...
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07/13/2020
Revisiting Fundamentals of Experience Replay
Experience replay is central to off-policy algorithms in deep reinforcem...
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06/24/2020
RL Unplugged: Benchmarks for Offline Reinforcement Learning
Offline methods for reinforcement learning have the potential to help br...
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04/29/2020
Neural Additive Models: Interpretable Machine Learning with Neural Nets
Deep neural networks (DNNs) are powerful black-box predictors that have ...
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07/10/2019
Striving for Simplicity in Off-policy Deep Reinforcement Learning
Reflecting on the advances of off-policy deep reinforcement learning (RL...
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02/19/2019
Learning to Generalize from Sparse and Underspecified Rewards
We consider the problem of learning from sparse and underspecified rewar...
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01/25/2019