Thompson Sampling

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7 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

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DARTS: Targeting Prognostic Covariates in Budget-Constrained Sequential Experiments
May 7, 2026Kateryna Husar, Alexander VolfovskyThompson Sampling Causal Effect Estimation
Department of Statistical Science Duke University Durham, NC 27705
Optimal Posterior Sampling for Policy Identification in Tabular Markov Decision Processes
May 5, 2026Cyrille Kone, Kevin JamiesonThompson Sampling Markov Decision Processes
Univ. Lille, CNRS, Inria, Centrale Lille, UMR 9189-CRIStAL, F-59000 Lille, France · University of Washington, Seattle, USA
A Finite Time Analysis of Thompson Sampling for Bayesian Optimization with Preferential Feedback
Apr 27, 2026Joseph Lazzaro, Davide Buffelli, Da-shan Shiu +1Thompson Sampling Pairwise Preference Evaluation
Imperial College London · MediaTek Research
Prior Diffusiveness and Regret in the Linear-Gaussian Bandit
Jan 5, 2026Yifan Zhu, John C. Duchi, Benjamin Van RoyThompson Sampling Multi-Armed Bandits
Department of Electrical Engineering, Stanford University, California, United States.
Thompson Sampling for Infinite-Horizon Discounted Decision Processes
May 14, 2024Daniel Adelman, Cagla Keceli, Alba V. Olivares-NadalThompson Sampling Regret Minimization in RL
Booth School of Business, The University of Chicago, Chicago, IL 60637, USA · United Airlines, Chicago, IL 60605, USA · UNSW Business School, UNSW, Kensington NSW 2033, Australia
Generalized Regret Analysis of Thompson Sampling using Fractional Posteriors
Sep 12, 2023Prateek Jaiswal, Debdeep Pati, Anirban Bhattacharya +1Thompson Sampling Multi-Armed Bandits
Department of Quantitative Methods, Daniels School of Business, Purdue University, West Lafayette, IN 47906, USA. · Department of Statistics, University of Wisconsin-Madison, Madison, WI 53706, USA. · Department of Statistics, Texas A&M University, College Station, TX 77843, USA.

May 7, 2026, stat.MLJ/K move · Enter open · S save

DARTS: Targeting Prognostic Covariates in Budget-Constrained Sequential Experiments

Kateryna Husar, Alexander Volfovsky

Department of Statistical Science Duke University Durham, NC 27705

Thompson Sampling Causal Effect Estimation Feature Selection Bayesian Experimental Design

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Randomized controlled trials typically assume that prognostic covariates are known and available at no cost. In practice, obtaining high-dimensional pretreatment data is costly, forcing a trade-off between covariate-adaptive precision and a measurement budget. We introduce Dynamic Adaptive Rerandomization via Thompson Sampling (DARTS), which treats covariate acquisition as a sequential optimization problem embedded within a design-based causal inference task. A budgeted combinatorial Thompson sampler learns which covariates are most prognostic across successive batches; selected covariates then drive rerandomization and regression adjustment to reduce batch-level average treatment effect variance. Our primary theoretical contribution is a decoupling result: adaptive covariate selection based on past batches preserves batch-level randomization validity, and the cumulative inverse-variance weighted estimator achieves at least nominal asymptotic coverage. We further derive a Bayes risk bound for the acquisition layer that matches the minimax lower bound up to logarithmic factors. Empirically, DARTS systematically concentrates the budget on informative features, significantly closing the efficiency gap to oracle designs while maintaining strict inferential validity.

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Latest papers 36

DARTS: Targeting Prognostic Covariates in Budget-Constrained Sequential Experiments

Optimal Posterior Sampling for Policy Identification in Tabular Markov Decision Processes

A Finite Time Analysis of Thompson Sampling for Bayesian Optimization with Preferential Feedback

Prior Diffusiveness and Regret in the Linear-Gaussian Bandit

Thompson Sampling for Infinite-Horizon Discounted Decision Processes

Generalized Regret Analysis of Thompson Sampling using Fractional Posteriors