Shinsaku Sakaue

Any-stepsize Gradient Descent for Separable Data under Fenchel–Young Losses

Bandit and Delayed Feedback in Online Structured Prediction

Online Inverse Linear Optimization: Efficient Logarithmic-Regret Algorithm, Robustness to Suboptimality, and Lower Bound

Learning to Generate Projections for Reducing Dimensionality of Heterogeneous Linear Programming Problems

No-Regret M${}^{\natural}$-Concave Function Maximization: Stochastic Bandit Algorithms and NP-Hardness of Adversarial Full-Information Setting

Generalization Bound and Learning Methods for Data-Driven Projections in Linear Programming