6 papers
Bandit Max-Min Fair Allocation
Tsubasa Harada, Shinji Ito, Hanna Sumita
In this paper, we study a new decision-making problem called the bandit max-min fair allocation (BMMFA) problem. The goal of this problem is to maximize the minimum utility among a…
Corrupted Learning Dynamics in Games
Taira Tsuchiya, Shinji Ito, Haipeng Luo
Learning in games refers to scenarios where multiple players interact in a shared environment, each aiming to minimize their regret. An equilibrium can be computed at a fast rate o…
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…
Prediction-Correction Algorithm for Time-Varying Smooth Non-Convex Optimization
Hidenori Iwakiri, Tomoya Kamijima, Shinji Ito +1
Time-varying optimization problems are prevalent in various engineering fields, and the ability to solve them accurately in real-time is becoming increasingly important. The predic…
Best-of-Three-Worlds Linear Bandit Algorithm with Variance-Adaptive Regret Bounds
Shinji Ito, Kei Takemura
This paper proposes a linear bandit algorithm that is adaptive to environments at two different levels of hierarchy. At the higher level, the proposed algorithm adapts to a variety…
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…