148 citations · 972 across the 92 of their papers we have counts for
13 papers · 1 filter
Reward Maximization for Pure Exploration: Minimax Optimal Good Arm Identification for Nonparametric Multi-Armed Bandits
Brian Cho, Dominik Meier, Kyra Gan +1
In multi-armed bandits, the tasks of reward maximization and pure exploration are often at odds with each other. The former focuses on exploiting arms with the highest means, while…
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 Central Role of the Loss Function in Reinforcement Learning
Kaiwen Wang, Nathan Kallus, Wen Sun
This paper illustrates the central role of loss functions in data-driven decision making, providing a comprehensive survey on their influence in cost-sensitive classification (CSC)…
Estimating Heterogeneous Treatment Effects by Combining Weak Instruments and Observational Data
Miruna Oprescu, Nathan Kallus
Accurately predicting conditional average treatment effects (CATEs) is crucial in personalized medicine and digital platform analytics. Since the treatments of interest often canno…
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…
Efficient and Sharp Off-Policy Evaluation in Robust Markov Decision Processes
Andrew Bennett, Nathan Kallus, Miruna Oprescu +2
We study the evaluation of a policy under best- and worst-case perturbations to a Markov decision process (MDP), using transition observations from the original MDP, whether they a…