Shinsaku Sakaue

63 papers A* 16A 9B 1Journal 35Unranked 1

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Finite and Corruption-Robust Regret Bounds in Online Inverse Linear Optimization under M-Convex Action Sets. CoRR Taihei Oki, Shinsaku Sakaue
2026	J	`jnl`	From Average Sensitivity to Small-Loss Regret Bounds under Random-Order Model. CoRR Shinsaku Sakaue, Yuichi Yoshida
2026	J	`jnl`	Rate Constant Matrix Contraction Method for Stiff Master Equations with Detailed Balance. SIAM J. Sci. Comput. Satoru Iwata, Taihei Oki, Shinsaku Sakaue
2026	J	`jnl`	Simple Projection-Free Algorithm for Contextual Recommendation with Logarithmic Regret and Robustness. CoRR Shinsaku Sakaue
2025	J	`jnl`	Any-stepsize Gradient Descent for Separable Data under Fenchel-Young Losses. CoRR Han Bao, Shinsaku Sakaue, Yuki Takezawa
2025	J	`jnl`	Bandit and Delayed Feedback in Online Structured Prediction. CoRR Yuki Shibukawa, Taira Tsuchiya, Shinsaku Sakaue, Kenji Yamanishi
2025	A	`conf`	Inverse Optimization with Prediction Market: A Characterization of Scoring Rules for Elciting System States. AISTATS Han Bao, Shinsaku Sakaue
2025	A*	`conf`	Learning to Generate Projections for Reducing Dimensionality of Heterogeneous Linear Programming Problems. ICML Tomoharu Iwata, Shinsaku Sakaue
2025	J	`jnl`	Non-Stationary Online Structured Prediction with Surrogate Losses. CoRR Shinsaku Sakaue, Han Bao, Yuzhou Cao
2025	J	`jnl`	Online Inverse Linear Optimization: Improved Regret Bound, Robustness to Suboptimality, and Toward Tight Regret Analysis. CoRR Shinsaku Sakaue, Taira Tsuchiya, Han Bao, Taihei Oki
2025	A	`conf`	Revisiting Online Learning Approach to Inverse Linear Optimization: A Fenchel-Young Loss Perspective and Gap-Dependent Regret Analysis. AISTATS Shinsaku Sakaue, Han Bao, Taira Tsuchiya
2025	J	`jnl`	Revisiting Online Learning Approach to Inverse Linear Optimization: A Fenchel-Young Loss Perspective and Gap-Dependent Regret Analysis. CoRR Shinsaku Sakaue, Han Bao, Taira Tsuchiya
2024	A*	`conf`	Generalization Bound and Learning Methods for Data-Driven Projections in Linear Programming. NeurIPS Shinsaku Sakaue, Taihei Oki
2024	A*	`conf`	No-Regret M NeurIPS Taihei Oki, Shinsaku Sakaue
2024	J	`jnl`	No-Regret M CoRR Taihei Oki, Shinsaku Sakaue
2024	A*	`conf`	Online Structured Prediction with Fenchel-Young Losses and Improved Surrogate Regret for Online Multiclass Classification with Logistic Loss. COLT Shinsaku Sakaue, Han Bao, Taira Tsuchiya, Taihei Oki
2024	J	`jnl`	Online Structured Prediction with Fenchel-Young Losses and Improved Surrogate Regret for Online Multiclass Classification with Logistic Loss. CoRR Shinsaku Sakaue, Han Bao, Taira Tsuchiya, Taihei Oki
2023	J	`jnl`	Data-Driven Projection for Reducing Dimensionality of Linear Programs: Generalization Bound and Learning Methods. CoRR Shinsaku Sakaue, Taihei Oki
2023	A*	`conf`	Faster Discrete Convex Function Minimization with Predictions: The M-Convex Case. NeurIPS Taihei Oki, Shinsaku Sakaue
2023	J	`jnl`	Faster Discrete Convex Function Minimization with Predictions: The M-Convex Case. CoRR Taihei Oki, Shinsaku Sakaue
2023	A	`conf`	Improved Generalization Bound and Learning of Sparsity Patterns for Data-Driven Low-Rank Approximation. AISTATS Shinsaku Sakaue, Taihei Oki
2023	J	`jnl`	Making individually fair predictions with causal pathways. Data Min. Knowl. Discov. Yoichi Chikahara, Shinsaku Sakaue, Akinori Fujino, Hisashi Kashima
2023	A*	`conf`	Nearly Tight Spectral Sparsification of Directed Hypergraphs. ICALP Kazusato Oko, Shinsaku Sakaue, Shin-ichi Tanigawa
2023	J	`jnl`	Rate Constant Matrix Contraction Method for Stiff Master Equations with Detailed Balance. CoRR Satoru Iwata, Taihei Oki, Shinsaku Sakaue
2023	A*	`conf`	Rethinking Warm-Starts with Predictions: Learning Predictions Close to Sets of Optimal Solutions for Faster L-/L ICML Shinsaku Sakaue, Taihei Oki
2023	J	`jnl`	Rethinking Warm-Starts with Predictions: Learning Predictions Close to Sets of Optimal Solutions for Faster L-/L CoRR Shinsaku Sakaue, Taihei Oki
2022	A*	`conf`	Algorithmic Bayesian Persuasion with Combinatorial Actions. AAAI Kaito Fujii, Shinsaku Sakaue
2022	A*	`conf`	Discrete-Convex-Analysis-Based Framework for Warm-Starting Algorithms with Predictions. NeurIPS Shinsaku Sakaue, Taihei Oki
2022	J	`jnl`	Discrete-Convex-Analysis-Based Framework for Warm-Starting Algorithms with Predictions. CoRR Shinsaku Sakaue, Taihei Oki
2022	B	`conf`	Exact and Scalable Network Reliability Evaluation for Probabilistic Correlated Failures. GLOBECOM Ryoma Onaka, Kengo Nakamura, Takeru Inoue, Masaaki Nishino, Norihito Yasuda, Shinsaku Sakaue
2022	J	`jnl`	Improved Generalization Bound and Learning of Sparsity Patterns for Data-Driven Low-Rank Approximation. CoRR Shinsaku Sakaue, Taihei Oki
2022	A*	`conf`	Lazy and Fast Greedy MAP Inference for Determinantal Point Process. NeurIPS Shinichi Hemmi, Taihei Oki, Shinsaku Sakaue, Kaito Fujii, Satoru Iwata
2022	J	`jnl`	Lazy and Fast Greedy MAP Inference for Determinantal Point Process. CoRR Shinichi Hemmi, Taihei Oki, Shinsaku Sakaue, Kaito Fujii, Satoru Iwata
2022	J	`jnl`	Nearly Tight Spectral Sparsification of Directed Hypergraphs by a Simple Iterative Sampling Algorithm. CoRR Kazusato Oko, Shinsaku Sakaue, Shin-ichi Tanigawa
2022	A*	`conf`	Sample Complexity of Learning Heuristic Functions for Greedy-Best-First and A* Search. NeurIPS Shinsaku Sakaue, Taihei Oki
2022	J	`jnl`	Sample Complexity of Learning Heuristic Functions for Greedy-Best-First and A* Search. CoRR Shinsaku Sakaue, Taihei Oki
2022	J	`jnl`	Sparse Regularized Optimal Transport with Deformed q-Entropy. Entropy Han Bao, Shinsaku Sakaue
2021	J	`jnl`	Algorithmic Bayesian persuasion with combinatorial actions. CoRR Kaito Fujii, Shinsaku Sakaue
2021	A*	`conf`	Differentiable Equilibrium Computation with Decision Diagrams for Stackelberg Models of Combinatorial Congestion Games. NeurIPS Shinsaku Sakaue, Kengo Nakamura
2021	J	`jnl`	Differentiable Equilibrium Computation with Decision Diagrams for Stackelberg Models of Combinatorial Congestion Games. CoRR Shinsaku Sakaue, Kengo Nakamura
2021	A	`conf`	Differentiable Greedy Algorithm for Monotone Submodular Maximization: Guarantees, Gradient Estimators, and Applications. AISTATS Shinsaku Sakaue
2021	A	`conf`	Learning Individually Fair Classifier with Path-Specific Causal-Effect Constraint. AISTATS Yoichi Chikahara, Shinsaku Sakaue, Akinori Fujino, Hisashi Kashima
2020	J	`jnl`	Differentiable Greedy Submodular Maximization: Guarantees, Gradient Estimators, and Applications. CoRR Shinsaku Sakaue
2020	A	`conf`	Guarantees of Stochastic Greedy Algorithms for Non-monotone Submodular Maximization with Cardinality Constraint. AISTATS Shinsaku Sakaue
2020	J	`jnl`	Learning Individually Fair Classifier with Causal-Effect Constraint. CoRR Yoichi Chikahara, Shinsaku Sakaue, Akinori Fujino
2020	A	`conf`	On Maximization of Weakly Modular Functions: Guarantees of Multi-stage Algorithms, Tractability, and Hardness. AISTATS Shinsaku Sakaue
2020	—	`—`	Online, Submodular, and Polynomial Optimization with Discrete Structures. Shinsaku Sakaue
2020	A*	`conf`	Practical Frank-Wolfe Method with Decision Diagrams for Computing Wardrop Equilibrium of Combinatorial Congestion Games. AAAI Kengo Nakamura, Shinsaku Sakaue, Norihito Yasuda
2019	J	`jnl`	Approximation Guarantees of Stochastic Greedy Algorithms for Non-monotone Submodular Maximization with a Size Constraint. CoRR Shinsaku Sakaue
2019	J	`jnl`	Best-first Search Algorithm for Non-convex Sparse Minimization. CoRR Shinsaku Sakaue, Naoki Marumo
2019	A*	`conf`	Beyond Adaptive Submodularity: Approximation Guarantees of Greedy Policy with Adaptive Submodularity Ratio. ICML Kaito Fujii, Shinsaku Sakaue
2019	J	`jnl`	Beyond Adaptive Submodularity: Approximation Guarantees of Greedy Policy with Adaptive Submodularity Ratio. CoRR Kaito Fujii, Shinsaku Sakaue
2019	A	`conf`	Greedy and IHT Algorithms for Non-convex Optimization with Monotone Costs of Non-zeros. AISTATS Shinsaku Sakaue
2018	A*	`conf`	Accelerated Best-First Search With Upper-Bound Computation for Submodular Function Maximization. AAAI Shinsaku Sakaue, Masakazu Ishihata
2018	A	`conf`	Efficient Bandit Combinatorial Optimization Algorithm with Zero-suppressed Binary Decision Diagrams. AISTATS Shinsaku Sakaue, Masakazu Ishihata, Shin-ichi Minato
2018	—	`conf`	Provable Fast Greedy Compressive Summarization with Any Monotone Submodular Function. NAACL-HLT Shinsaku Sakaue, Tsutomu Hirao, Masaaki Nishino, Masaaki Nagata
2018	A*	`conf`	Submodular Function Maximization Over Graphs via Zero-Suppressed Binary Decision Diagrams. AAAI Shinsaku Sakaue, Masaaki Nishino, Norihito Yasuda
2018	J	`jnl`	Weak Supermodularity Assists Submodularity-based Approaches to Non-convex Constrained Optimization. CoRR Shinsaku Sakaue
2017	J	`jnl`	Exact Semidefinite Programming Relaxations with Truncated Moment Matrix for Binary Polynomial Optimization Problems. SIAM J. Optim. Shinsaku Sakaue, Akiko Takeda, Sunyoung Kim, Naoki Ito
2017	J	`jnl`	On maximizing a monotone k-submodular function subject to a matroid constraint. Discret. Optim. Shinsaku Sakaue
2017	J	`jnl`	Practical Adversarial Combinatorial Bandit Algorithm via Compression of Decision Sets. CoRR Shinsaku Sakaue, Masakazu Ishihata, Shin-ichi Minato
2016	J	`jnl`	On maximizing a monotone k-submodular function subject to a matroid constraint. CoRR Shinsaku Sakaue
2016	J	`jnl`	Solving Generalized CDT Problems via Two-Parameter Eigenvalues. SIAM J. Optim. Shinsaku Sakaue, Yuji Nakatsukasa, Akiko Takeda, Satoru Iwata