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cs.LG2024
Adaptive Learning Rate for Follow-the-Regularized-Leader: Competitive Analysis and Best-of-Both-Worlds
Shinji Ito, Taira Tsuchiya, Junya Honda
Follow-The-Regularized-Leader (FTRL) is known as an effective and versatile approach in online learning, where appropriate choice of the learning rate is crucial for smaller regret…
cs.LG2023★ 1 cited
Optimality of Thompson Sampling with Noninformative Priors for Pareto Bandits
Jongyeong Lee, Junya Honda, Chao-Kai Chiang +1
In the stochastic multi-armed bandit problem, a randomized probability matching policy called Thompson sampling (TS) has shown excellent performance in various reward models. In ad…
cs.LG2022
Best-of-Both-Worlds Algorithms for Partial Monitoring
Taira Tsuchiya, Shinji Ito, Junya Honda
This study considers the partial monitoring problem with -actions and -outcomes and provides the first best-of-both-worlds algorithms, whose regrets are favorably bounded bot…