Shinsaku Sakaue
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    • Non-Stationary online structured prediction with surrogate losses
    • Any-stepsize gradient descent for separable data under Fenchel–Young losses
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    • Online structured prediction with Fenchel–Young losses and improved surrogate regret for online multiclass classification with logistic loss
    • Faster discrete convex function minimization with predictions: The $\mathrm{M}$-convex case
    • Rethinking warm-starts with predictions: Learning predictions close to sets of optimal solutions for faster $\mathrm{L}$-/$\mathrm{L}^\natural$-convex function minimization
    • Nearly tight spectral sparsification of directed hypergraphs
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    • On maximization of weakly modular functions: Guarantees of multi-stage algorithms, tractability, and hardness
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    • On maximizing a monotone $k$-submodular function subject to a matroid constraint
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Non-Stationary online structured prediction with surrogate losses

Oct 9, 2025·
Shinsaku Sakaue
,
Han Bao
,
Yuzhou Cao
· 0 min read
PDF
Type
Preprint
Publication
arXiv [cs.LG]
Last updated on Oct 9, 2025
Any-stepsize gradient descent for separable data under Fenchel–Young losses Sep 19, 2025 →

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