40 citations · 118 across the 8 of their papers we have counts for
5 papers · 1 filter
Offline Learning in Markov Games with General Function Approximation
Yuheng Zhang, Yu Bai, Nan Jiang
We study offline multi-agent reinforcement learning (RL) in Markov games, where the goal is to learn an approximate equilibrium -- such as Nash equilibrium and (Coarse) Correlated…
Practical Contextual Bandits with Feedback Graphs
Mengxiao Zhang, Yuheng Zhang, Olga Vrousgou +2
While contextual bandit has a mature theory, effectively leveraging different feedback patterns to enhance the pace of learning remains unclear. Bandits with feedback graphs, which…
Improved High-Probability Regret for Adversarial Bandits with Time-Varying Feedback Graphs
Haipeng Luo, Hanghang Tong, Mengxiao Zhang +1
We study high-probability regret bounds for adversarial -armed bandits with time-varying feedback graphs over rounds. For general strongly observable graphs, we develop an a…
Improved Algorithms for Neural Active Learning
Yikun Ban, Yuheng Zhang, Hanghang Tong +2
We improve the theoretical and empirical performance of neural-network(NN)-based active learning algorithms for the non-parametric streaming setting. In particular, we introduce tw…
The Secret Revealer: Generative Model-Inversion Attacks Against Deep Neural Networks
Yuheng Zhang, Ruoxi Jia, Hengzhi Pei +3
This paper studies model-inversion attacks, in which the access to a model is abused to infer information about the training data. Since its first introduction, such attacks have r…