4 citations · 14 across the 6 of their papers we have counts for
6 papers
Reward-Directed Conditional Diffusion: Provable Distribution Estimation and Reward Improvement
Hui Yuan, Kaixuan Huang, Chengzhuo Ni +2
We explore the methodology and theory of reward-directed generation via conditional diffusion models. Directed generation aims to generate samples with desired properties as measur…
Unified Off-Policy Learning to Rank: a Reinforcement Learning Perspective
Zeyu Zhang, Yi Su, Hui Yuan +5
Off-policy Learning to Rank (LTR) aims to optimize a ranker from data collected by a deployed logging policy. However, existing off-policy learning to rank methods often make stron…
Adversarial Attacks on Online Learning to Rank with Stochastic Click Models
Zichen Wang, Rishab Balasubramanian, Hui Yuan +3
We propose the first study of adversarial attacks on online learning to rank. The goal of the adversary is to misguide the online learning to rank algorithm to place the target ite…
Bandit Theory and Thompson Sampling-Guided Directed Evolution for Sequence Optimization
Hui Yuan, Chengzhuo Ni, Huazheng Wang +4
Directed Evolution (DE), a landmark wet-lab method originated in 1960s, enables discovery of novel protein designs via evolving a population of candidate sequences. Recent advances…
Learning Entangled Single-Sample Gaussians in the Subset-of-Signals Model
Yingyu Liang, Hui Yuan
In the setting of entangled single-sample distributions, the goal is to estimate some common parameter shared by a family of distributions, given one single sample from each di…
Learning Entangled Single-Sample Distributions via Iterative Trimming
Hui Yuan, Yingyu Liang
In the setting of entangled single-sample distributions, the goal is to estimate some common parameter shared by a family of distributions, given one \emph{single} sample from each…