4 citations · 6 across the 8 of their papers we have counts for
9 papers · 1 filter
Statistical Properties of Robust Learning under Distributional Shifts
Zhiyi Li, Xiaojie Mao, Yunbei Xu +1
Distributional shifts arise when the target deployment environment differs from the source environment that generated the training data. Robust learning frameworks such as Distribu…
Contextual Linear Optimization with Partial Feedback
Yichun Hu, Nathan Kallus, Xiaojie Mao +1
Contextual linear optimization (CLO) uses predictive contextual features to reduce uncertainty in random cost coefficients in the objective and thereby improve decision-making perf…
Learning with Selectively Labeled Data from Multiple Decision-makers
Jian Chen, Zhehao Li, Xiaojie Mao
We study the problem of classification with selectively labeled data, whose distribution may differ from the full population due to historical decision-making. We exploit the fact…
Minimax Instrumental Variable Regression and Convergence Guarantees without Identification or Closedness
Andrew Bennett, Nathan Kallus, Xiaojie Mao +3
In this paper, we study nonparametric estimation of instrumental variable (IV) regressions. Recently, many flexible machine learning methods have been developed for instrumental va…
Fast Rates for Contextual Linear Optimization
Yichun Hu, Nathan Kallus, Xiaojie Mao
Incorporating side observations in decision making can reduce uncertainty and boost performance, but it also requires we tackle a potentially complex predictive relationship. While…
Smooth Contextual Bandits: Bridging the Parametric and Non-differentiable Regret Regimes
Yichun Hu, Nathan Kallus, Xiaojie Mao
We study a nonparametric contextual bandit problem where the expected reward functions belong to a Hölder class with smoothness parameter . We show how this interpolates between…