7 citations · 19 across the 31 of their papers we have counts for
5 papers · 1 filter
Prediction with Expert Advice under Heavy-Tailed Losses
Yuki Shibukawa, Shinji Ito
We study prediction with expert advice over rounds with experts under heavy-tailed losses whose -moment is bounded by for some . In this setting, ex…
A Perturbation Approach to Unconstrained Linear Bandits
Andrew Jacobsen, Dorian Baudry, Shinji Ito +1
We revisit the standard perturbation-based approach of Abernethy et al. (2008) in the context of unconstrained Bandit Linear Optimization (uBLO). We show the surprising result that…
Combinatorial Allocation Bandits with Nonlinear Arm Utility
Yuki Shibukawa, Koichi Tanaka, Yuta Saito +1
A matching platform is a system that matches participants of different types, such as companies and job-seekers. In such a platform, maximizing matches may concentrate assignments…
Scale-Invariant Fast Convergence in Games
Taira Tsuchiya, Haipeng Luo, Shinji Ito
Scale-invariance in games has recently emerged as a widely valued desirable property. Yet, almost all fast convergence guarantees in learning in games require prior knowledge of th…
Adversarial Learning in Games with Bandit Feedback: Logarithmic Pure-Strategy Maximin Regret
Shinji Ito, Haipeng Luo, Arnab Maiti +2
Learning to play zero-sum games is a fundamental problem in game theory and machine learning. While significant progress has been made in minimizing external regret in the self-pla…