256 citations · 627 across the 30 of their papers we have counts for
12 papers · 1 filter
AdaLoss: A computationally-efficient and provably convergent adaptive gradient method
Xiaoxia Wu, Yuege Xie, Simon Du +1
We propose a computationally-friendly adaptive learning rate schedule, "AdaLoss", which directly uses the information of the loss function to adjust the stepsize in gradient descen…
Gap-Dependent Bounds for Two-Player Markov Games
Zehao Dou, Zhuoran Yang, Zhaoran Wang +1
As one of the most popular methods in the field of reinforcement learning, Q-learning has received increasing attention. Recently, there have been more theoretical works on the reg…
Global Convergence of Gradient Descent for Asymmetric Low-Rank Matrix Factorization
Tian Ye, Simon S. Du
We study the asymmetric low-rank factorization problem: \[\min_{\mathbf{U} \in \mathbb{R}^{m \times d}, \mathbf{V} \in \mathbb{R}^{n \times d}} \frac{1}{2}\|\mathbf{U}\mathbf{V}^\t…
Corruption Robust Active Learning
Yifang Chen, Simon S. Du, Kevin Jamieson
We conduct theoretical studies on streaming-based active learning for binary classification under unknown adversarial label corruptions. In this setting, every time before the lear…
On the Power of Multitask Representation Learning in Linear MDP
Rui Lu, Gao Huang, Simon S. Du
While multitask representation learning has become a popular approach in reinforcement learning (RL), theoretical understanding of why and when it works remains limited. This paper…
Provable Adaptation across Multiway Domains via Representation Learning
Zhili Feng, Shaobo Han, Simon S. Du
This paper studies zero-shot domain adaptation where each domain is indexed on a multi-dimensional array, and we only have data from a small subset of domains. Our goal is to produ…