2 citations · 2 across the 4 of their papers we have counts for
4 papers
Faster Discrete Convex Function Minimization with Predictions: The M-Convex Case
Taihei Oki, Shinsaku Sakaue
Recent years have seen a growing interest in accelerating optimization algorithms with machine-learned predictions. Sakaue and Oki (NeurIPS 2022) have developed a general framework…
Rethinking Warm-Starts with Predictions: Learning Predictions Close to Sets of Optimal Solutions for Faster -/-Convex Function Minimization
Shinsaku Sakaue, Taihei Oki
An emerging line of work has shown that machine-learned predictions are useful to warm-start algorithms for discrete optimization problems, such as bipartite matching. Previous stu…
Algorithmic Bayesian persuasion with combinatorial actions
Kaito Fujii, Shinsaku Sakaue
Bayesian persuasion is a model for understanding strategic information revelation: an agent with an informational advantage, called a sender, strategically discloses information by…
On maximizing a monotone k-submodular function subject to a matroid constraint
Shinsaku Sakaue
A -submodular function is an extension of a submodular function in that its input is given by disjoint subsets instead of a single subset. For unconstrained nonnegative -…