16 citations · 30 across the 24 of their papers we have counts for
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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…
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…
Bandit and Delayed Feedback in Online Structured Prediction
Yuki Shibukawa, Taira Tsuchiya, Shinsaku Sakaue +1
Online structured prediction is a task of sequentially predicting outputs with complex structures based on inputs and past observations, encompassing online classification. Recent…
Online Inverse Linear Optimization: Efficient Logarithmic-Regret Algorithm, Robustness to Suboptimality, and Lower Bound
Shinsaku Sakaue, Taira Tsuchiya, Han Bao +1
In online inverse linear optimization, a learner observes time-varying sets of feasible actions and an agent's optimal actions, selected by solving linear optimization over the fea…