11 papers
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…
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…
Design Stability in Adaptive Experiments: Implications for Treatment Effect Estimation
Saikat Sengupta, Koulik Khamaru, Suvrojit Ghosh +1
We study the problem of estimating the average treatment effect (ATE) under sequentially adaptive treatment assignment mechanisms. In contrast to classical completely randomized de…
Uncertainty Quantification With Multiple Sources
Mufang Ying, Wenge Guo, Koulik Khamaru +1
Weighted conformal prediction (WCP) has been commonly used to quantify prediction uncertainty under covariate shift. However, the effectiveness of WCP relies heavily on the degree…
Stable Thompson Sampling: Valid Inference via Variance Inflation
Budhaditya Halder, Shubhayan Pan, Koulik Khamaru
We consider the problem of statistical inference when the data is collected via a Thompson Sampling-type algorithm. While Thompson Sampling (TS) is known to be both asymptotically…
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…