research
∙
09/20/2023
Drift Control of High-Dimensional RBM: A Computational Method Based on Neural Networks
Motivated by applications in queueing theory, we consider a stochastic c...
share
research
∙
05/28/2023
Sample Complexity of Variance-reduced Distributionally Robust Q-learning
Dynamic decision making under distributional shifts is of fundamental in...
research
∙
02/26/2023
A Finite Sample Complexity Bound for Distributionally Robust Q-learning
We consider a reinforcement learning setting in which the deployment env...
research
∙
05/19/2022
Calibration Matters: Tackling Maximization Bias in Large-scale Advertising Recommendation Systems
Calibration is defined as the ratio of the average predicted click rate ...
research
∙
01/09/2022
Selecting the Best Optimizing System
We formulate selecting the best optimizing system (SBOS) problems and pr...
research
∙
06/02/2021
Testing Group Fairness via Optimal Transport Projections
We present a statistical testing framework to detect if a given machine ...
research
∙
07/08/2020
Robust Bayesian Classification Using an Optimistic Score Ratio
We build a Bayesian contextual classification model using an optimistic ...
research
∙
06/10/2020
Distributional Robust Batch Contextual Bandits
Policy learning using historical observational data is an important prob...
research
∙
06/04/2019
Confidence Regions in Wasserstein Distributionally Robust Estimation
Wasserstein distributionally robust optimization (DRO) estimators are ob...
research
∙
01/23/2019