Efficient and Robust Algorithms for Adversarial Linear Contextual Bandits

1 February 2020

Papers citing "Efficient and Robust Algorithms for Adversarial Linear Contextual Bandits"

15 / 15 papers shown

Title
Second Order Bounds for Contextual Bandits with Function Approximation Aldo Pacchiano 220 4 0 24 Sep 2024
On Bits and Bandits: Quantifying the Regret-Information Trade-off Itai Shufaro Nadav Merlis Nir Weinberger Shie Mannor 159 0 0 26 May 2024
LC-Tsallis-INF: Generalized Best-of-Both-Worlds Linear Contextual Bandits Masahiro Kato Shinji Ito 139 0 0 05 Mar 2024
Beyond UCB: Optimal and Efficient Contextual Bandits with Regression Oracles Dylan J. Foster Alexander Rakhlin 365 207 0 12 Feb 2020
Comments on the Du-Kakade-Wang-Yang Lower Bounds Benjamin Van Roy Shi Dong 135 38 0 18 Nov 2019
Weighted Linear Bandits for Non-Stationary Environments Yoan Russac Claire Vernade Olivier Cappé 130 108 0 19 Sep 2019
Model selection for contextual bandits Dylan J. Foster A. Krishnamurthy Haipeng Luo OffRL 169 90 0 03 Jun 2019
Iterate averaging as regularization for stochastic gradient descent Gergely Neu Lorenzo Rosasco MoMe 76 61 0 22 Feb 2018
Explore no more: Improved high-probability regret bounds for non-stochastic bandits Gergely Neu 381 183 0 10 Jun 2015
First-order regret bounds for combinatorial semi-bandits Gergely Neu 184 58 0 23 Feb 2015
Non-strongly-convex smooth stochastic approximation with convergence rate O(1/n) Francis R. Bach Eric Moulines 91 405 0 10 Jun 2013
An efficient algorithm for learning with semi-bandit feedback Gergely Neu Gábor Bartók 112 80 0 13 May 2013
Thompson Sampling for Contextual Bandits with Linear Payoffs Shipra Agrawal Navin Goyal 195 1,000 0 15 Sep 2012
Regret in Online Combinatorial Optimization Jean-Yves Audibert Sébastien Bubeck Gábor Lugosi OffRL 54 257 0 20 Apr 2012
Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design Niranjan Srinivas Andreas Krause Sham Kakade Matthias Seeger 146 1,622 0 21 Dec 2009