14 papers
Beyond IGO-Flow: Toward Convergence Analysis of IGO in Continuous Spaces
Ryosuke Kimura, Youhei Akimoto
Information-Geometric Optimization (IGO) provides a unified framework for black-box optimization by interpreting the adaptation of a search distribution as a natural gradient updat…
Mixed-Categorical Black-Box Optimization via Information-Geometric Bilevel Decomposition
Marc Ong, Shinichi Shirakawa, Youhei Akimoto
Mixed categorical-continuous optimization arises in many practical domains, yet remains challenging. In the black-box setting, evolution strategy-based approaches have shown promis…
Accelerating Black-Box Bilevel Optimization with Rank-Based Upper-Level Value Function Approximation
Marc Ong, Youhei Akimoto
Bilevel optimization is a field of significant theoretical and practical interest, yet solving such optimization problems remains challenging. Evolutionary methods have been employ…
Convergence rate of the (1+1)-evolution strategy on locally strongly convex functions with lipschitz continuous gradient
Daiki Morinaga, Kazuto Fukuchi, Jun Sakuma +1
Evolution strategy (ES) is one of the promising classes of algorithms for black-box continuous optimization. Despite its broad successes in applications, theoretical analysis on th…
Feature selection based on cluster assumption in PU learning
Motonobu Uchikoshi, Youhei Akimoto
Feature selection is essential for efficient data mining and sometimes encounters the positive-unlabeled (PU) learning scenario, where only a few positive labels are available, whi…
Challenges of Interaction in Optimizing Mixed Categorical-Continuous Variables
Youhei Akimoto, Xilin Gao, Ze Kai Ng +1
Optimization of mixed categorical-continuous variables is prevalent in real-world applications of black-box optimization. Recently, CatCMA has been proposed as a method for optimiz…