32 citations · 76 across the 17 of their papers we have counts for
17 papers · 1 filter
Adjusting Regression Models for Conditional Uncertainty Calibration
Ruijiang Gao, Mingzhang Yin, James McInerney +1
Conformal Prediction methods have finite-sample distribution-free marginal coverage guarantees. However, they generally do not offer conditional coverage guarantees, which can be i…
The Implicit Delta Method
Nathan Kallus, James McInerney
Epistemic uncertainty quantification is a crucial part of drawing credible conclusions from predictive models, whether concerned about the prediction at a given point or any downst…
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…
Comment: Entropy Learning for Dynamic Treatment Regimes
Nathan Kallus
I congratulate Profs. Binyan Jiang, Rui Song, Jialiang Li, and Donglin Zeng (JSLZ) for an exciting development in conducting inferences on optimal dynamic treatment regimes (DTRs)…
Statistically Efficient Off-Policy Policy Gradients
Nathan Kallus, Masatoshi Uehara
Policy gradient methods in reinforcement learning update policy parameters by taking steps in the direction of an estimated gradient of policy value. In this paper, we consider the…
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…