462 citations · 2.5k across the 127 of their papers we have counts for
22 papers · 1 filter
Neural Contextual Bandits with Deep Representation and Shallow Exploration
Pan Xu, Zheng Wen, Handong Zhao +1
We study a general class of contextual bandits, where each context-action pair is associated with a raw feature vector, but the reward generating function is unknown. We propose a…
Logarithmic Regret for Reinforcement Learning with Linear Function Approximation
Jiafan He, Dongruo Zhou, Quanquan Gu
Reinforcement learning (RL) with linear function approximation has received increasing attention recently. However, existing work has focused on obtaining -type regret bo…
Provable Multi-Objective Reinforcement Learning with Generative Models
Dongruo Zhou, Jiahao Chen, Quanquan Gu
Multi-objective reinforcement learning (MORL) is an extension of ordinary, single-objective reinforcement learning (RL) that is applicable to many real-world tasks where multiple o…
Direction Matters: On the Implicit Bias of Stochastic Gradient Descent with Moderate Learning Rate
Jingfeng Wu, Difan Zou, Vladimir Braverman +1
Understanding the algorithmic bias of \emph{stochastic gradient descent} (SGD) is one of the key challenges in modern machine learning and deep learning theory. Most of the existin…
Faster Convergence of Stochastic Gradient Langevin Dynamics for Non-Log-Concave Sampling
Difan Zou, Pan Xu, Quanquan Gu
We provide a new convergence analysis of stochastic gradient Langevin dynamics (SGLD) for sampling from a class of distributions that can be non-log-concave. At the core of our app…
Do Wider Neural Networks Really Help Adversarial Robustness?
Boxi Wu, Jinghui Chen, Deng Cai +2
Adversarial training is a powerful type of defense against adversarial examples. Previous empirical results suggest that adversarial training requires wider networks for better per…