989 citations · 6.1k across the 184 of their papers we have counts for
9 papers · 2 filters
Unpacking Reward Shaping: Understanding the Benefits of Reward Engineering on Sample Complexity
Abhishek Gupta, Aldo Pacchiano, Yuexiang Zhai +2
Reinforcement learning provides an automated framework for learning behaviors from high-level reward specifications, but in practice the choice of reward function can be crucial fo…
The Role of Coverage in Online Reinforcement Learning
Tengyang Xie, Dylan J. Foster, Yu Bai +2
Coverage conditions -- which assert that the data logging distribution adequately covers the state space -- play a fundamental role in determining the sample complexity of offline…
Recurrent Convolutional Neural Networks Learn Succinct Learning Algorithms
Surbhi Goel, Sham Kakade, Adam Tauman Kalai +1
Neural networks (NNs) struggle to efficiently solve certain problems, such as learning parities, even when there are simple learning algorithms for those problems. Can NNs discover…
The Power and Limitation of Pretraining-Finetuning for Linear Regression under Covariate Shift
Jingfeng Wu, Difan Zou, Vladimir Braverman +2
We study linear regression under covariate shift, where the marginal distribution over the input covariates differs in the source and the target domains, while the conditional dist…
Hidden Progress in Deep Learning: SGD Learns Parities Near the Computational Limit
Boaz Barak, Benjamin L. Edelman, Surbhi Goel +3
There is mounting evidence of emergent phenomena in the capabilities of deep learning methods as we scale up datasets, model sizes, and training times. While there are some account…
Matryoshka Representation Learning
Aditya Kusupati, Gantavya Bhatt, Aniket Rege +8
Learned representations are a central component in modern ML systems, serving a multitude of downstream tasks. When training such representations, it is often the case that computa…
Risk Bounds of Multi-Pass SGD for Least Squares in the Interpolation Regime
Difan Zou, Jingfeng Wu, Vladimir Braverman +2
Stochastic gradient descent (SGD) has achieved great success due to its superior performance in both optimization and generalization. Most of existing generalization analyses are m…
A Complete Characterization of Linear Estimators for Offline Policy Evaluation
Juan C. Perdomo, Akshay Krishnamurthy, Peter Bartlett +1
Offline policy evaluation is a fundamental statistical problem in reinforcement learning that involves estimating the value function of some decision-making policy given data colle…
Understanding Contrastive Learning Requires Incorporating Inductive Biases
Nikunj Saunshi, Jordan Ash, Surbhi Goel +5
Contrastive learning is a popular form of self-supervised learning that encourages augmentations (views) of the same input to have more similar representations compared to augmenta…