7 citations · 19 across the 31 of their papers we have counts for
10 papers · 1 filter
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…
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…
A Simple and Adaptive Learning Rate for FTRL in Online Learning with Minimax Regret of and its Application to Best-of-Both-Worlds
Taira Tsuchiya, Shinji Ito
Follow-the-Regularized-Leader (FTRL) is a powerful framework for various online learning problems. By designing its regularizer and learning rate to be adaptive to past observation…
Contaminated Online Convex Optimization
Tomoya Kamijima, Shinji Ito
In online convex optimization, some efficient algorithms have been designed for each of the individual classes of objective functions, e.g., convex, strongly convex, and exp-concav…
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…
Follow-the-Perturbed-Leader with Fréchet-type Tail Distributions: Optimality in Adversarial Bandits and Best-of-Both-Worlds
Jongyeong Lee, Junya Honda, Shinji Ito +1
This paper studies the optimality of the Follow-the-Perturbed-Leader (FTPL) policy in both adversarial and stochastic -armed bandits. Despite the widespread use of the Follow-th…
LC-Tsallis-INF: Generalized Best-of-Both-Worlds Linear Contextual Bandits
Masahiro Kato, Shinji Ito
We investigate the \emph{linear contextual bandit problem} with independent and identically distributed (i.i.d.) contexts. In this problem, we aim to develop a \emph{Best-of-Both-W…
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…
Fast Rates in Stochastic Online Convex Optimization by Exploiting the Curvature of Feasible Sets
Taira Tsuchiya, Shinji Ito
In this work, we explore online convex optimization (OCO) and introduce a new condition and analysis that provides fast rates by exploiting the curvature of feasible sets. In onlin…
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…