← Authors

Shinsaku Sakaue

23 papers · 2018–2025 · 6 conferences · across top CS/AI conferences

Achievements

🌈 Renaissance Researcher (5) 🗺️ Taxonomy Completionist (26) 🌉 Interdisciplinary Bridge 🧭 Keyword Pioneer 🌍 Conference Polyglot (6) 🏃 Academic Marathon (7) 🐣 Hot Topic Early Bird 🐝 Cross-Pollinator (12) 🐺 Lone Wolf (4) 🔬 Deep Specialist (14) 🗃️ Keyword Collector (84) 💎 Century Club (23) 🔥 Unstoppable (8)

Conferences

AISTATS (9) NIPS (7) ICML (3) AAAI (2) COLT (1) NAACL (1)

Top co-authors

Taihei Oki (9) Kaito Fujii (3) Han Bao (3) Kengo Nakamura (2) Taira Tsuchiya (2) Tsutomu Hirao (1) Norihito Yasuda (1) Akinori Fujino (1) Masaaki Nishino (1) Shinichi Hemmi (1)

Keywords

greedy algorithm (6) combinatorial optimization (6) convex optimization (4) approximation guarantee (3) submodular maximization (3) bipartite matching (2) decision diagram (2) cardinality constraint (2) online algorithm (2) approximation algorithm (2) generalization bound (2) submodular function (2) combinatorial congestion game (2) matroid intersection (2) equilibrium computation (2) regret bound (2) differentiable optimization (2) map inference (1) multiclass classification (1) non-convex optimization (1)

Papers

Learning to Generate Projections for Reducing Dimensionality of Heterogeneous Linear Programming Problems ICML 2025
Revisiting Online Learning Approach to Inverse Linear Optimization: A Fenchel–Young Loss Perspective and Gap-Dependent Regret Analysis AISTATS 2025
Inverse Optimization with Prediction Market: A Characterization of Scoring Rules for Elciting System States AISTATS 2025
No-Regret M${}^{\natural}$-Concave Function Maximization: Stochastic Bandit Algorithms and NP-Hardness of Adversarial Full-Information Setting NIPS 2024
Online Structured Prediction with Fenchel–Young Losses and Improved Surrogate Regret for Online Multiclass Classification with Logistic Loss COLT 2024
Generalization Bound and Learning Methods for Data-Driven Projections in Linear Programming NIPS 2024
Improved Generalization Bound and Learning of Sparsity Patterns for Data-Driven Low-Rank Approximation AISTATS 2023
Faster Discrete Convex Function Minimization with Predictions: The M-Convex Case NIPS 2023
Rethinking Warm-Starts with Predictions: Learning Predictions Close to Sets of Optimal Solutions for Faster $\text{L}$-/$\text{L}^\natural$-Convex Function Minimization ICML 2023
Sample Complexity of Learning Heuristic Functions for Greedy-Best-First and A* Search NIPS 2022
Discrete-Convex-Analysis-Based Framework for Warm-Starting Algorithms with Predictions NIPS 2022
Lazy and Fast Greedy MAP Inference for Determinantal Point Process NIPS 2022
Algorithmic Bayesian Persuasion with Combinatorial Actions AAAI 2022
Differentiable Greedy Algorithm for Monotone Submodular Maximization: Guarantees, Gradient Estimators, and Applications AISTATS 2021
Differentiable Equilibrium Computation with Decision Diagrams for Stackelberg Models of Combinatorial Congestion Games NIPS 2021
Learning Individually Fair Classifier with Path-Specific Causal-Effect Constraint AISTATS 2021
Guarantees of Stochastic Greedy Algorithms for Non-monotone Submodular Maximization with Cardinality Constraint AISTATS 2020
Practical Frank–Wolfe Method with Decision Diagrams for Computing Wardrop Equilibrium of Combinatorial Congestion Games AAAI 2020
On Maximization of Weakly Modular Functions: Guarantees of Multi-stage Algorithms, Tractability, and Hardness AISTATS 2020
Beyond Adaptive Submodularity: Approximation Guarantees of Greedy Policy with Adaptive Submodularity Ratio ICML 2019
Greedy and IHT Algorithms for Non-convex Optimization with Monotone Costs of Non-zeros AISTATS 2019
Efficient Bandit Combinatorial Optimization Algorithm with Zero-suppressed Binary Decision Diagrams AISTATS 2018
Provable Fast Greedy Compressive Summarization with Any Monotone Submodular Function NAACL 2018