212 citations · 595 across the 39 of their papers we have counts for
52 papers · 1 filter
A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning
Zixiang Chen, Chris Junchi Li, Angela Yuan +2
With the increasing need for handling large state and action spaces, general function approximation has become a key technique in reinforcement learning (RL). In this paper, we pro…
Computationally Efficient Horizon-Free Reinforcement Learning for Linear Mixture MDPs
Dongruo Zhou, Quanquan Gu
Recent studies have shown that episodic reinforcement learning (RL) is not more difficult than contextual bandits, even with a long planning horizon and unknown state transitions.…
On the Convergence of Certified Robust Training with Interval Bound Propagation
Yihan Wang, Zhouxing Shi, Quanquan Gu +1
Interval Bound Propagation (IBP) is so far the base of state-of-the-art methods for training neural networks with certifiable robustness guarantees when potential adversarial pertu…
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…
Iterative Teacher-Aware Learning
Luyao Yuan, Dongruo Zhou, Junhong Shen +5
In human pedagogy, teachers and students can interact adaptively to maximize communication efficiency. The teacher adjusts her teaching method for different students, and the stude…
Linear Contextual Bandits with Adversarial Corruptions
Heyang Zhao, Dongruo Zhou, Quanquan Gu
We study the linear contextual bandit problem in the presence of adversarial corruption, where the interaction between the player and a possibly infinite decision set is contaminat…