23 citations · 66 across the 10 of their papers we have counts for
10 papers
LambdaRankIC: Directly Optimizing Rank IC for Financial Prediction
Yan Lin, Yihong Su, Yi Yang
In financial predictions, the performance of machine learning models is often assessed by Rank IC, which is the Spearman rank correlation between the model predictions and the real…
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…
Long-Term Value of Exploration: Measurements, Findings and Algorithms
Yi Su, Xiangyu Wang, Elaine Ya Le +12
Effective exploration is believed to positively influence the long-term user experience on recommendation platforms. Determining its exact benefits, however, has been challenging.…
Data-Driven Offline Decision-Making via Invariant Representation Learning
Han Qi, Yi Su, Aviral Kumar +1
The goal in offline data-driven decision-making is synthesize decisions that optimize a black-box utility function, using a previously-collected static dataset, with no active inte…
Online Adaptation to Label Distribution Shift
Ruihan Wu, Chuan Guo, Yi Su +1
Machine learning models often encounter distribution shifts when deployed in the real world. In this paper, we focus on adaptation to label distribution shift in the online setting…
Optimizing Rankings for Recommendation in Matching Markets
Yi Su, Magd Bayoumi, Thorsten Joachims
Based on the success of recommender systems in e-commerce, there is growing interest in their use in matching markets (e.g., labor). While this holds potential for improving market…
Off-policy Bandits with Deficient Support
Noveen Sachdeva, Yi Su, Thorsten Joachims
Learning effective contextual-bandit policies from past actions of a deployed system is highly desirable in many settings (e.g. voice assistants, recommendation, search), since it…
Adaptive Estimator Selection for Off-Policy Evaluation
Yi Su, Pavithra Srinath, Akshay Krishnamurthy
We develop a generic data-driven method for estimator selection in off-policy policy evaluation settings. We establish a strong performance guarantee for the method, showing that i…
Doubly robust off-policy evaluation with shrinkage
Yi Su, Maria Dimakopoulou, Akshay Krishnamurthy +1
We propose a new framework for designing estimators for off-policy evaluation in contextual bandits. Our approach is based on the asymptotically optimal doubly robust estimator, bu…
CAB: Continuous Adaptive Blending Estimator for Policy Evaluation and Learning
Yi Su, Lequn Wang, Michele Santacatterina +1
The ability to perform offline A/B-testing and off-policy learning using logged contextual bandit feedback is highly desirable in a broad range of applications, including recommend…