Shinsaku Sakaue
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    • Finite and corruption-robust regret bounds in online inverse linear optimization under M-convex action sets
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    • Rate constant matrix contraction method for stiff master equations with detailed balance
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    • Inverse optimization with prediction market: A characterization of scoring rules for elciting system states
    • Revisiting online learning approach to inverse linear optimization: A Fenchel–Young loss perspective and gap-dependent regret analysis
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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
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    • Sparse regularized optimal transport with deformed $q$-entropy
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    • Selecting molecules with diverse structures and properties by maximizing submodular functions of descriptors learned with graph neural networks
    • Differentiable equilibrium computation with decision diagrams for Stackelberg models of combinatorial congestion games
    • Differentiable greedy algorithm for monotone submodular maximization: Guarantees, gradient estimators, and applications
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    • On maximization of weakly modular functions: Guarantees of multi-stage algorithms, tractability, and hardness
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    • Beyond adaptive submodularity: Approximation guarantees of greedy policy with adaptive submodularity ratio
    • Greedy and IHT algorithms for non-convex optimization with monotone costs of non-zeros
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    • On maximizing a monotone $k$-submodular function subject to a matroid constraint
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Finite and corruption-robust regret bounds in online inverse linear optimization under M-convex action sets

May 1, 2026·
Taihei Oki
,
Shinsaku Sakaue
· 0 min read
PAPER
Type
Conference paper
Publication
International Conference on Machine Learning (ICML), to appear
Last updated on May 1, 2026
Non-stationary online structured prediction with surrogate losses May 1, 2026 →

© 2026 Me. This work is licensed under CC BY NC ND 4.0

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