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stat.ML2026
Adaptive Learning Rates with Surrogate Probability for Follow-the-Perturbed-Leader
Jongyeong Lee, Junya Honda, Shinji Ito +1
Follow-the-regularized-leader framework has shown effectiveness and flexibility in online learning problems, where the choice of learning rates are known to be crucial. Recently, a…
stat.ML2026
Follow-the-Perturbed-Leader for Decoupled Bandits: Best-of-Both-Worlds and Practicality
Chaiwon Kim, Jongyeong Lee, Min-hwan Oh
We study the decoupled multi-armed bandit problem, where the learner separately selects one arm for exploration and one, possibly different, arm for exploitation at each round. In…
stat.ML2025
Revisiting Follow-the-Perturbed-Leader with Unbounded Perturbations in Bandit Problems
Jongyeong Lee, Junya Honda, Shinji Ito +1
Follow-the-Regularized-Leader (FTRL) policies have achieved Best-of-Both-Worlds (BOBW) results in various settings through hybrid regularizers, whereas analogous results for Follow…