80 citations · 177 across the 25 of their papers we have counts for
5 papers · 1 filter
Adaptive inference for functionals of M-estimands
James Leiner, Aurélien Bibaut, Nathan Kallus +2
Reinforcement learning and contextual bandit algorithms have become increasingly common in sequential decision-making applications. When these methods are deployed in high-stakes d…
Bandit Simulation for Average Reward Inference
Samya Praharaj, Chih-Yu Chang, Koulik Khamaru +1
Multi-arm bandit algorithms are increasingly used in online platforms, clinical trials, and social science experiments, but valid statistical inference on their performance remains…
Stabilizing Bandits using Regularization: Precise Regret and A Quantitative Central Limit Theorem
Budhaditya Halder, Ishan Sengupta, Koustav Chowdhury +2
Statistical inference with bandit data presents fundamental challenges owing to adaptive sampling, which violates the independence assumptions underlying classical asymptotic theor…
Efficient Inference after Directionally Stable Adaptive Experiments
Zikai Shen, Houssam Zenati, Nathan Kallus +3
We study inference on scalar-valued pathwise differentiable targets after adaptive data collection, such as a bandit algorithm. We introduce a novel target-specific condition, dire…
Avoiding the Price of Adaptivity: Inference in Linear Contextual Bandits via Stability
Samya Praharaj, Koulik Khamaru
Statistical inference in contextual bandits is challenging due to the adaptive, non-i.i.d. nature of the data. A growing body of work shows that classical least-squares inference c…