1 citations · 2 across the 9 of their papers we have counts for
9 papers
Refined Sample Complexity for Markov Games with Independent Linear Function Approximation
Yan Dai, Qiwen Cui, Simon S. Du
Markov Games (MG) is an important model for Multi-Agent Reinforcement Learning (MARL). It was long believed that the "curse of multi-agents" (i.e., the algorithmic performance drop…
The Crucial Role of Normalization in Sharpness-Aware Minimization
Yan Dai, Kwangjun Ahn, Suvrit Sra
Sharpness-Aware Minimization (SAM) is a recently proposed gradient-based optimizer (Foret et al., ICLR 2021) that greatly improves the prediction performance of deep neural network…
Joint k-TE Space Image Reconstruction and Data Fitting for T2 Mapping
Yan Dai, Xun Jia, Yen-Peng Liao +2
Objectives: To develop a joint k-TE reconstruction algorithm to reconstruct the T2-weighted (T2W) images and T2 map simultaneously. Materials and Methods: The joint k-TE reconstruc…
Refined Regret for Adversarial MDPs with Linear Function Approximation
Yan Dai, Haipeng Luo, Chen-Yu Wei +1
We consider learning in an adversarial Markov Decision Process (MDP) where the loss functions can change arbitrarily over episodes and the state space can be arbitrarily large.…
Banker Online Mirror Descent: A Universal Approach for Delayed Online Bandit Learning
Jiatai Huang, Yan Dai, Longbo Huang
We propose Banker Online Mirror Descent (Banker-OMD), a novel framework generalizing the classical Online Mirror Descent (OMD) technique in the online learning literature. The Bank…
Follow-the-Perturbed-Leader for Adversarial Markov Decision Processes with Bandit Feedback
Yan Dai, Haipeng Luo, Liyu Chen
We consider regret minimization for Adversarial Markov Decision Processes (AMDPs), where the loss functions are changing over time and adversarially chosen, and the learner only ob…
Variance-Aware Sparse Linear Bandits
Yan Dai, Ruosong Wang, Simon S. Du
It is well-known that for sparse linear bandits, when ignoring the dependency on sparsity which is much smaller than the ambient dimension, the worst-case minimax regret is $\widet…
Adaptive Best-of-Both-Worlds Algorithm for Heavy-Tailed Multi-Armed Bandits
Jiatai Huang, Yan Dai, Longbo Huang
In this paper, we generalize the concept of heavy-tailed multi-armed bandits to adversarial environments, and develop robust best-of-both-worlds algorithms for heavy-tailed multi-a…
Scale-Free Adversarial Multi-Armed Bandit with Arbitrary Feedback Delays
Jiatai Huang, Yan Dai, Longbo Huang
We consider the Scale-Free Adversarial Multi-Armed Bandit (MAB) problem with unrestricted feedback delays. In contrast to the standard assumption that all losses are -bounde…