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stat.ML2026
Tight Regret Bound for Online Inverse Linear Optimization via Multiscale Matrix Weights
Shinsaku Sakaue
We study online inverse linear optimization with a fixed unknown linear utility: in each round, an environment presents a compact action set, the learner recommends an action from…
stat.ML2026
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…
stat.ML2025
Any-stepsize Gradient Descent for Separable Data under Fenchel-Young Losses
Han Bao, Shinsaku Sakaue, Yuki Takezawa
The gradient descent (GD) has been one of the most common optimizer in machine learning. In particular, the loss landscape of a neural network is typically sharpened during the ini…