5 papers · 1 filter
A Further Efficient Algorithm with Best-of-Both-Worlds Guarantees for -Set Semi-Bandit Problem
Botao Chen, Jongyeong Lee, Chansoo Kim +1
This paper studies the optimality and complexity of Follow-the-Perturbed-Leader (FTPL) policy in -set semi-bandit problems. FTPL has been studied extensively as a promising cand…
Note on Follow-the-Perturbed-Leader in Combinatorial Semi-Bandit Problems
Botao Chen, Junya Honda
This paper studies the optimality and complexity of Follow-the-Perturbed-Leader (FTPL) policy in size-invariant combinatorial semi-bandit problems. Recently, Honda et al. (2023) an…
Exploration by Optimization with Hybrid Regularizers: Logarithmic Regret with Adversarial Robustness in Partial Monitoring
Taira Tsuchiya, Shinji Ito, Junya Honda
Partial monitoring is a generic framework of online decision-making problems with limited feedback. To make decisions from such limited feedback, it is necessary to find an appropr…
Learning with Posterior Sampling for Revenue Management under Time-varying Demand
Kazuma Shimizu, Junya Honda, Shinji Ito +1
This paper discusses the revenue management (RM) problem to maximize revenue by pricing items or services. One challenge in this problem is that the demand distribution is unknown…
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…