A unified framework for bandit multiple testing

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A unified framework for bandit multiple testing

15 July 2021

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Papers citing "A unified framework for bandit multiple testing"

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Title
Adaptive Learn-then-Test: Statistically Valid and Efficient Hyperparameter Selection Matteo Zecchin Sangwoo Park Osvaldo Simeone LM&MA 265 5 0 24 Sep 2024
E-values, Multiple Testing and Beyond Guanxun Li Xianyang Zhang 65 1 0 05 Dec 2023
Online multiple testing with e-values Ziyu Xu Aaditya Ramdas 76 4 0 10 Nov 2023
Anytime-valid t-tests and confidence sequences for Gaussian means with unknown variance Hongjian Wang Aaditya Ramdas 79 3 0 05 Oct 2023
Game-theoretic statistics and safe anytime-valid inference Aaditya Ramdas Peter Grünwald V. Vovk Glenn Shafer 114 130 0 04 Oct 2022
Adaptive Identification of Populations with Treatment Benefit in Clinical Trials: Machine Learning Challenges and Solutions Alicia Curth Alihan Huyuk M. Schaar 59 3 0 11 Aug 2022
Multi-disciplinary fairness considerations in machine learning for clinical trials Isabel Chien Nina Deliu Richard Turner Adrian Weller S. Villar Niki Kilbertus FaML 73 23 0 18 May 2022
E-values as unnormalized weights in multiple testing Nikolaos Ignatiadis Ruodu Wang Aaditya Ramdas 133 24 0 26 Apr 2022
Anytime-valid sequential testing for elicitable functionals via supermartingales P. Casgrain Martin Larsson J. Ziegel 68 9 0 12 Apr 2022
False discovery rate control with e-values Ruodu Wang Aaditya Ramdas 126 100 0 06 Sep 2020
ROI Maximization in Stochastic Online Decision-Making Nicolò Cesa-Bianchi Tommaso Cesari Yishay Mansour Vianney Perchet 97 4 0 28 May 2019