40 citations · 88 across the 20 of their papers we have counts for
8 papers · 1 filter
Dimension-Optimal Dynamic Regret in Exp-Concave Online Learning
Yanyi Li, Wendao Wu, Haihan Zhang +6
Tracking changing comparators is a central problem in online learning. For exp-concave losses, previous work established the dependence on the horizon and Eu…
Beyond Independent Manipulation: Individual Fairness-aware Strategic Classification with Peer Imitation
Xinpeng Lv, Chunyuan Zheng, Yunxin Mao +9
Strategic classification (SC) investigates scenarios where agents manipulate their features to obtain favorable decisions from predictive models. Existing fairness-aware SC approac…
Partial Fairness Awareness: Belief-Guided Strategic Mechanism for Strategic Agents
Xinpeng Lv, Chunyuan Zheng, Yunxin Mao +8
Strategic machine learning investigates scenarios where agents manipulate their features to receive favorable decisions from predictive models. To address fairness concerns intrins…
CausalRM: Causal-Theoretic Reward Modeling for RLHF from Observational User Feedbacks
Hao Wang, Licheng Pan, Zhichao Chen +7
Despite the success of reinforcement learning from human feedback (RLHF) in aligning language models, current reward modeling heavily relies on experimental feedback data collected…
Learning Counterfactual Outcomes Under Rank Preservation
Peng Wu, Haoxuan Li, Chunyuan Zheng +5
Counterfactual inference aims to estimate the counterfactual outcome at the individual level given knowledge of an observed treatment and the factual outcome, with broad applicatio…
A Partial Initialization Strategy to Mitigate the Overfitting Problem in CATE Estimation with Hidden Confounding
Chuan Zhou, Yaxuan Li, Chunyuan Zheng +3
Estimating the conditional average treatment effect (CATE) from observational data plays a crucial role in areas such as e-commerce, healthcare, and economics. Existing studies mai…
Be Aware of the Neighborhood Effect: Modeling Selection Bias under Interference
Haoxuan Li, Chunyuan Zheng, Sihao Ding +4
Selection bias in recommender system arises from the recommendation process of system filtering and the interactive process of user selection. Many previous studies have focused on…
StableDR: Stabilized Doubly Robust Learning for Recommendation on Data Missing Not at Random
Haoxuan Li, Chunyuan Zheng, Peng Wu
In recommender systems, users always choose the favorite items to rate, which leads to data missing not at random and poses a great challenge for unbiased evaluation and learning o…