Shinsaku Sakaue
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    • Any-stepsize gradient descent for separable data under Fenchel–Young losses
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    • Learning to generate projections for reducing dimensionality of heterogeneous linear programming problems
    • 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
    • Generalization bound and learning methods for data-driven projections in linear programming
    • No-regret $\mathrm{M}^\natural$-concave function maximization: Stochastic bandit algorithms and NP-hardness of adversarial full-information setting
    • 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
    • Making individually fair predictions with causal pathways
    • Improved generalization bound and learning of sparsity patterns for data-driven low-rank approximation
    • Exact and scalable network reliability evaluation for probabilistic correlated failures
    • Discrete-convex-analysis-based framework for warm-starting algorithms with predictions
    • Lazy and fast greedy MAP inference for determinantal point process
    • Sample complexity of learning heuristic functions for greedy-best-first and A* search
    • 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
    • Learning individually fair classifier with path-specific causal-effect constraint
    • Guarantees of stochastic greedy algorithms for non-monotone submodular maximization with cardinality constraints
    • On maximization of weakly modular functions: Guarantees of multi-stage algorithms, tractability, and hardness
    • Practical Frank–Wolfe method with decision diagrams for computing Wardrop equilibrium of combinatorial congestion games
    • Best-first search algorithm for non-convex sparse minimization
    • 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
    • Provable fast greedy compressive summarization with any monotone submodular function
    • Efficient bandit combinatorial optimization algorithm with zero-suppressed binary decision diagrams
    • Accelerated best-first search with upper-bound computation for submodular function maximization
    • Submodular function maximization over graphs via zero-suppressed binary decision diagrams
    • Exact semidefinite programming relaxations with truncated moment matrix for binary polynomial optimization problems
    • On maximizing a monotone $k$-submodular function subject to a matroid constraint
    • Using multiparameter eigenvalues for solving quadratic programming with quadratic equality constraints
    • Solving generalized CDT problems via two-parameter eigenvalues
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Any-stepsize gradient descent for separable data under Fenchel–Young losses

Sep 19, 2025·
Han Bao
,
Shinsaku Sakaue
,
Yuki Takezawa
· 0 min read
PDF
Type
Conference paper
Publication
Advances in Neural Information Processing Systems (NeurIPS), Spotlight, to appear.
Last updated on Sep 19, 2025
Bandit and delayed feedback in online structured prediction Sep 19, 2025 →

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