Adaptive Learning Rate for Follow-the-Regularized-Leader: Competitive Analysis and Best-of-Both-Worlds

1 March 2024

Papers citing "Adaptive Learning Rate for Follow-the-Regularized-Leader: Competitive Analysis and Best-of-Both-Worlds"

6 / 6 papers shown

Title
A Near-optimal, Scalable and Corruption-tolerant Framework for Stochastic Bandits: From Single-Agent to Multi-Agent and Beyond Zicheng Hu Cheng Chen 72 0 0 11 Feb 2025
Optimism in the Face of Ambiguity Principle for Multi-Armed Bandits Mengmeng Li Daniel Kuhn Bahar Taşkesen 25 0 0 30 Sep 2024
$A Simple and Adaptive Learning Rate for FTRL in Online Learning with Minimax Regret of $Θ(T^{2/3})$ and its Application to Best-of-Both-Worlds$ A Simple and Adaptive Learning Rate for FTRL in Online Learning with Minimax Regret of $Θ(T^{2/3})$ and its Application to Best-of-Both-Worlds Taira Tsuchiya Shinji Ito 21 0 0 30 May 2024
First- and Second-Order Bounds for Adversarial Linear Contextual Bandits Julia Olkhovskaya J. Mayo T. Erven Gergely Neu Chen-Yu Wei 51 10 0 01 May 2023
Best-of-three-worlds Analysis for Linear Bandits with Follow-the-regularized-leader Algorithm Fang-yuan Kong Canzhe Zhao Shuai Li 35 11 0 13 Mar 2023
A Best-of-Both-Worlds Algorithm for Bandits with Delayed Feedback Saeed Masoudian Julian Zimmert Yevgeny Seldin 31 18 0 29 Jun 2022