Nearly Optimal Regret for Stochastic Linear Bandits with Heavy-Tailed Payoffs

28 April 2020

Papers citing "Nearly Optimal Regret for Stochastic Linear Bandits with Heavy-Tailed Payoffs"

11 / 11 papers shown

Title
Catoni Contextual Bandits are Robust to Heavy-tailed Rewards Chenlu Ye Yujia Jin Alekh Agarwal Tong Zhang 165 0 0 04 Feb 2025
Almost Optimal Algorithms for Linear Stochastic Bandits with Heavy-Tailed Payoffs Han Shao Xiaotian Yu Irwin King Michael R. Lyu 45 46 0 25 Oct 2018
Online Stochastic Linear Optimization under One-bit Feedback Lijun Zhang Tianbao Yang Rong Jin Zhi Zhou 52 65 0 25 Sep 2015
Bandit Convex Optimization: sqrt{T} Regret in One Dimension Sébastien Bubeck O. Dekel Tomer Koren Yuval Peres 109 36 0 23 Feb 2015
Loss minimization and parameter estimation with heavy tails Daniel J. Hsu Sivan Sabato 128 188 0 07 Jul 2013
Bandits with heavy tail Sébastien Bubeck Nicolò Cesa-Bianchi Gábor Lugosi 172 290 0 08 Sep 2012
PAC-Bayesian Inequalities for Martingales Yevgeny Seldin François Laviolette Nicolò Cesa-Bianchi John Shawe-Taylor P. Auer 105 127 0 31 Oct 2011
Stochastic convex optimization with bandit feedback Alekh Agarwal Dean Phillips Foster Daniel J. Hsu Sham Kakade Alexander Rakhlin 159 240 0 08 Jul 2011
Lipschitz Bandits without the Lipschitz Constant Sébastien Bubeck Gilles Stoltz Jia Yuan Yu 113 90 0 25 May 2011
Challenging the empirical mean and empirical variance: a deviation study O. Catoni 149 462 0 10 Sep 2010
Multi-Armed Bandits in Metric Spaces Robert D. Kleinberg Aleksandrs Slivkins E. Upfal 350 468 0 29 Sep 2008