Solving Generalized CDT Problems via Two-Parameter Eigenvalues. - Shinsaku Sakaue; Yuji Nakatsukasa; Akiko Takeda; Satoru Iwata 001 (SIAM J. Optim., 2016)

Practical Adversarial Combinatorial Bandit Algorithm via Compression of Decision Sets. - Shinsaku Sakaue; Masakazu Ishihata; Shin-ichi Minato (CoRR, 2017)

On maximizing a monotone k-submodular function subject to a matroid constraint. - Shinsaku Sakaue (Discret. Optim., 2017)

Exact Semidefinite Programming Relaxations with Truncated Moment Matrix for Binary Polynomial Optimization Problems. - Shinsaku Sakaue; Akiko Takeda; Sunyoung Kim; Naoki Ito (SIAM J. Optim., 2017)

Accelerated Best-First Search With Upper-Bound Computation for Submodular Function Maximization. - Shinsaku Sakaue; Masakazu Ishihata (AAAI, 2018)

Submodular Function Maximization Over Graphs via Zero-Suppressed Binary Decision Diagrams. - Shinsaku Sakaue; Masaaki Nishino; Norihito Yasuda (AAAI, 2018)

Efficient Bandit Combinatorial Optimization Algorithm with Zero-suppressed Binary Decision Diagrams. - Shinsaku Sakaue; Masakazu Ishihata; Shin-ichi Minato (AISTATS, 2018)

Provable Fast Greedy Compressive Summarization with Any Monotone Submodular Function. - Shinsaku Sakaue; Tsutomu Hirao; Masaaki Nishino; Masaaki Nagata (NAACL-HLT, 2018)

Weak Supermodularity Assists Submodularity-based Approaches to Non-convex Constrained Optimization. - Shinsaku Sakaue (CoRR, 2018)

Greedy and IHT Algorithms for Non-convex Optimization with Monotone Costs of Non-zeros. - Shinsaku Sakaue (AISTATS, 2019)

Beyond Adaptive Submodularity: Approximation Guarantees of Greedy Policy with Adaptive Submodularity Ratio. - Kaito Fujii; Shinsaku Sakaue (ICML, 2019)

Approximation Guarantees of Stochastic Greedy Algorithms for Non-monotone Submodular Maximization with a Size Constraint. - Shinsaku Sakaue (CoRR, 2019)

Best-first Search Algorithm for Non-convex Sparse Minimization. - Shinsaku Sakaue; Naoki Marumo (CoRR, 2019)

Practical Frank-Wolfe Method with Decision Diagrams for Computing Wardrop Equilibrium of Combinatorial Congestion Games. - Kengo Nakamura 001; Shinsaku Sakaue; Norihito Yasuda (AAAI, 2020)

Guarantees of Stochastic Greedy Algorithms for Non-monotone Submodular Maximization with Cardinality Constraint. - Shinsaku Sakaue (AISTATS, 2020)

On Maximization of Weakly Modular Functions: Guarantees of Multi-stage Algorithms, Tractability, and Hardness. - Shinsaku Sakaue (AISTATS, 2020)

Learning Individually Fair Classifier with Causal-Effect Constraint. - Yoichi Chikahara; Shinsaku Sakaue; Akinori Fujino (CoRR, 2020)

Differentiable Greedy Submodular Maximization: Guarantees, Gradient Estimators, and Applications. - Shinsaku Sakaue (CoRR, 2020)

Online, Submodular, and Polynomial Optimization with Discrete Structures. - Shinsaku Sakaue (2020)

Learning Individually Fair Classifier with Path-Specific Causal-Effect Constraint. - Yoichi Chikahara; Shinsaku Sakaue; Akinori Fujino; Hisashi Kashima (AISTATS, 2021)

Differentiable Greedy Algorithm for Monotone Submodular Maximization: Guarantees, Gradient Estimators, and Applications. - Shinsaku Sakaue (AISTATS, 2021)

Differentiable Equilibrium Computation with Decision Diagrams for Stackelberg Models of Combinatorial Congestion Games. - Shinsaku Sakaue; Kengo Nakamura 001 (NeurIPS, 2021)

Algorithmic Bayesian Persuasion with Combinatorial Actions. - Kaito Fujii; Shinsaku Sakaue (AAAI, 2022)

Exact and Scalable Network Reliability Evaluation for Probabilistic Correlated Failures. - Ryoma Onaka; Kengo Nakamura 001; Takeru Inoue; Masaaki Nishino; Norihito Yasuda; Shinsaku Sakaue (GLOBECOM, 2022)

Lazy and Fast Greedy MAP Inference for Determinantal Point Process. - Shinichi Hemmi; Taihei Oki; Shinsaku Sakaue; Kaito Fujii; Satoru Iwata 001 (NeurIPS, 2022)

Sample Complexity of Learning Heuristic Functions for Greedy-Best-First and A* Search. - Shinsaku Sakaue; Taihei Oki (NeurIPS, 2022)

Discrete-Convex-Analysis-Based Framework for Warm-Starting Algorithms with Predictions. - Shinsaku Sakaue; Taihei Oki (NeurIPS, 2022)

Nearly Tight Spectral Sparsification of Directed Hypergraphs by a Simple Iterative Sampling Algorithm. - Kazusato Oko; Shinsaku Sakaue; Shin-ichi Tanigawa (CoRR, 2022)

Sparse Regularized Optimal Transport with Deformed q-Entropy. - Han Bao 002; Shinsaku Sakaue (Entropy, 2022)

Improved Generalization Bound and Learning of Sparsity Patterns for Data-Driven Low-Rank Approximation. - Shinsaku Sakaue; Taihei Oki (AISTATS, 2023)

Nearly Tight Spectral Sparsification of Directed Hypergraphs. - Kazusato Oko; Shinsaku Sakaue; Shin-ichi Tanigawa (ICALP, 2023)

Rethinking Warm-Starts with Predictions: Learning Predictions Close to Sets of Optimal Solutions for Faster L-/L♮-Convex Function Minimization. - Shinsaku Sakaue; Taihei Oki (ICML, 2023)

Faster Discrete Convex Function Minimization with Predictions: The M-Convex Case. - Taihei Oki; Shinsaku Sakaue (NeurIPS, 2023)

Data-Driven Projection for Reducing Dimensionality of Linear Programs: Generalization Bound and Learning Methods. - Shinsaku Sakaue; Taihei Oki (CoRR, 2023)

Rate Constant Matrix Contraction Method for Stiff Master Equations with Detailed Balance. - Satoru Iwata 001; Taihei Oki; Shinsaku Sakaue (CoRR, 2023)

Making individually fair predictions with causal pathways. - Yoichi Chikahara; Shinsaku Sakaue; Akinori Fujino; Hisashi Kashima (Data Min. Knowl. Discov., 2023)

Online Structured Prediction with Fenchel-Young Losses and Improved Surrogate Regret for Online Multiclass Classification with Logistic Loss. - Shinsaku Sakaue; Han Bao 002; Taira Tsuchiya; Taihei Oki (COLT, 2024)

No-Regret M♮-Concave Function Maximization: Stochastic Bandit Algorithms and NP-Hardness of Adversarial Full-Information Setting. - Taihei Oki; Shinsaku Sakaue (NeurIPS, 2024)

Generalization Bound and Learning Methods for Data-Driven Projections in Linear Programming. - Shinsaku Sakaue; Taihei Oki (NeurIPS, 2024)

Inverse Optimization with Prediction Market: A Characterization of Scoring Rules for Elciting System States. - Han Bao 002; Shinsaku Sakaue (AISTATS, 2025)

Revisiting Online Learning Approach to Inverse Linear Optimization: A Fenchel-Young Loss Perspective and Gap-Dependent Regret Analysis. - Shinsaku Sakaue; Han Bao 002; Taira Tsuchiya (AISTATS, 2025)

Online Inverse Linear Optimization: Improved Regret Bound, Robustness to Suboptimality, and Toward Tight Regret Analysis. - Shinsaku Sakaue; Taira Tsuchiya; Han Bao 002; Taihei Oki (CoRR, 2025)

Any-stepsize Gradient Descent for Separable Data under Fenchel-Young Losses. - Han Bao 002; Shinsaku Sakaue; Yuki Takezawa (CoRR, 2025)

Bandit and Delayed Feedback in Online Structured Prediction. - Yuki Shibukawa; Taira Tsuchiya; Shinsaku Sakaue; Kenji Yamanishi (CoRR, 2025)