23 citations · 26 across the 3 of their papers we have counts for
6 papers
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…
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…