16 citations · 22 across the 14 of their papers we have counts for
24 papers
Sensitivity and Differential Privacy in Metric Voting with Distortion below Three
Shinsaku Sakaue, Kaito Fujii, Soh Kumabe +1
Voting rules aggregate individual preferences into collective decisions, but the rankings they receive contain only ordinal information. The metric distortion framework studies ord…
Simple Projection-Free Algorithm for Contextual Recommendation with Logarithmic Regret and Robustness
Shinsaku Sakaue
Contextual recommendation is a variant of contextual linear bandits in which the learner observes an (optimal) action rather than a reward scalar. Recently, Sakaue et al. (2025) de…
From Average Sensitivity to Small-Loss Regret Bounds under Random-Order Model
Shinsaku Sakaue, Yuichi Yoshida
We study online learning in the random-order model, where the multiset of loss functions is chosen adversarially but revealed in a uniformly random order. By extending the batch-to…
Finite and Corruption-Robust Regret Bounds in Online Inverse Linear Optimization under M-Convex Action Sets
Taihei Oki, Shinsaku Sakaue
We study online inverse linear optimization, also known as contextual recommendation, where a learner sequentially infers an agent's hidden objective vector from observed optimal a…
Non-Stationary Online Structured Prediction with Surrogate Losses
Shinsaku Sakaue, Han Bao, Yuzhou Cao
Online structured prediction, including online classification as a special case, is the task of sequentially predicting labels from input features. In this setting, the surrogate r…
Revisiting Online Learning Approach to Inverse Linear Optimization: A FenchelYoung Loss Perspective and Gap-Dependent Regret Analysis
Shinsaku Sakaue, Han Bao, Taira Tsuchiya
This paper revisits the online learning approach to inverse linear optimization studied by Bärmann et al. (2017), where the goal is to infer an unknown linear objective function of…