13 citations · 22 across the 6 of their papers we have counts for
5 papers · 1 filter
Extreme Value Policy Optimization for Safe Reinforcement Learning
Shiqing Gao, Yihang Zhou, Shuai Shao +5
Ensuring safety is a critical challenge in applying Reinforcement Learning (RL) to real-world scenarios. Constrained Reinforcement Learning (CRL) addresses this by maximizing retur…
Unveiling the Vulnerability of Graph-LLMs: An Interpretable Multi-Dimensional Adversarial Attack on TAGs
Bowen Fan, Zhilin Guo, Xunkai Li +5
Graph Neural Networks (GNNs) have become a pivotal framework for modeling graph-structured data, enabling a wide range of applications from social network analysis to molecular che…
Towards Fundamental Limits for Active Multi-distribution Learning
Chicheng Zhang, Yihan Zhou
Multi-distribution learning extends agnostic Probably Approximately Correct (PAC) learning to the setting in which a family of distributions, , is considered…
Near-Polynomially Competitive Active Logistic Regression
Yihan Zhou, Eric Price, Trung Nguyen
We address the problem of active logistic regression in the realizable setting. It is well known that active learning can require exponentially fewer label queries compared to pass…
A Competitive Algorithm for Agnostic Active Learning
Eric Price, Yihan Zhou
For some hypothesis classes and input distributions, active agnostic learning needs exponentially fewer samples than passive learning; for other classes and distributions, it offer…