7 citations · 17 across the 12 of their papers we have counts for
12 papers
Provably Efficient Iterated CVaR Reinforcement Learning with Function Approximation and Human Feedback
Yu Chen, Yihan Du, Pihe Hu +3
Risk-sensitive reinforcement learning (RL) aims to optimize policies that balance the expected reward and risk. In this paper, we present a novel risk-sensitive RL framework that e…
Provably Safe Reinforcement Learning with Step-wise Violation Constraints
Nuoya Xiong, Yihan Du, Longbo Huang
In this paper, we investigate a novel safe reinforcement learning problem with step-wise violation constraints. Our problem differs from existing works in that we consider stricter…
Multi-task Representation Learning for Pure Exploration in Linear Bandits
Yihan Du, Longbo Huang, Wen Sun
Despite the recent success of representation learning in sequential decision making, the study of the pure exploration scenario (i.e., identify the best option and minimize the sam…
Dueling Bandits: From Two-dueling to Multi-dueling
Yihan Du, Siwei Wang, Longbo Huang
We study a general multi-dueling bandit problem, where an agent compares multiple options simultaneously and aims to minimize the regret due to selecting suboptimal arms. This sett…
Provably Efficient Risk-Sensitive Reinforcement Learning: Iterated CVaR and Worst Path
Yihan Du, Siwei Wang, Longbo Huang
In this paper, we study a novel episodic risk-sensitive Reinforcement Learning (RL) problem, named Iterated CVaR RL, which aims to maximize the tail of the reward-to-go at each ste…
Branching Reinforcement Learning
Yihan Du, Wei Chen
In this paper, we propose a novel Branching Reinforcement Learning (Branching RL) model, and investigate both Regret Minimization (RM) and Reward-Free Exploration (RFE) metrics for…
Collaborative Pure Exploration in Kernel Bandit
Yihan Du, Wei Chen, Yuko Kuroki +1
In this paper, we formulate a Collaborative Pure Exploration in Kernel Bandit problem (CoPE-KB), which provides a novel model for multi-agent multi-task decision making under limit…
Combinatorial Pure Exploration with Bottleneck Reward Function
Yihan Du, Yuko Kuroki, Wei Chen
In this paper, we study the Combinatorial Pure Exploration problem with the Bottleneck reward function (CPE-B) under the fixed-confidence (FC) and fixed-budget (FB) settings. In CP…
Continuous Mean-Covariance Bandits
Yihan Du, Siwei Wang, Zhixuan Fang +1
Existing risk-aware multi-armed bandit models typically focus on risk measures of individual options such as variance. As a result, they cannot be directly applied to important rea…
A One-Size-Fits-All Solution to Conservative Bandit Problems
Yihan Du, Siwei Wang, Longbo Huang
In this paper, we study a family of conservative bandit problems (CBPs) with sample-path reward constraints, i.e., the learner's reward performance must be at least as well as a gi…
Combinatorial Pure Exploration of Dueling Bandit
Wei Chen, Yihan Du, Longbo Huang +1
In this paper, we study combinatorial pure exploration for dueling bandits (CPE-DB): we have multiple candidates for multiple positions as modeled by a bipartite graph, and in each…
Combinatorial Pure Exploration with Full-Bandit or Partial Linear Feedback
Yihan Du, Yuko Kuroki, Wei Chen
In this paper, we first study the problem of combinatorial pure exploration with full-bandit feedback (CPE-BL), where a learner is given a combinatorial action space $\mathcal{X} \…