4 papers
Foundations of Reliable Inference: Reliability-Efficiency Co-Design
Jiayi Huang
Reliable inference requires that artificial intelligence (AI) models provide trustworthy uncertainty estimates, not merely accurate predictions. Recent advances in Bayesian learnin…
Horizon-Free and Instance-Dependent Regret Bounds for Reinforcement Learning with General Function Approximation
Jiayi Huang, Han Zhong, Liwei Wang +1
To tackle long planning horizon problems in reinforcement learning with general function approximation, we propose the first algorithm, termed as UCRL-WVTR, that achieves both \emp…
Tackling Heavy-Tailed Rewards in Reinforcement Learning with Function Approximation: Minimax Optimal and Instance-Dependent Regret Bounds
Jiayi Huang, Han Zhong, Liwei Wang +1
While numerous works have focused on devising efficient algorithms for reinforcement learning (RL) with uniformly bounded rewards, it remains an open question whether sample or tim…
Breaking the Moments Condition Barrier: No-Regret Algorithm for Bandits with Super Heavy-Tailed Payoffs
Han Zhong, Jiayi Huang, Lin F. Yang +1
Despite a large amount of effort in dealing with heavy-tailed error in machine learning, little is known when moments of the error can become non-existential: the random noise …