50 citations · 94 across the 15 of their papers we have counts for
19 papers
Adaptive Scenario Subset Selection for Worst-Case Optimization and its Application to Well Placement Optimization
Atsuhiro Miyagi, Kazuto Fukuchi, Jun Sakuma +1
In this study, we consider simulation-based worst-case optimization problems with continuous design variables and a finite scenario set. To reduce the number of simulations require…
Black-Box Min--Max Continuous Optimization Using CMA-ES with Worst-case Ranking Approximation
Atsuhiro Miyagi, Kazuto Fukuchi, Jun Sakuma +1
In this study, we investigate the problem of min-max continuous optimization in a black-box setting . A popular approach updates and simultaneously…
Monotone Improvement of Information-Geometric Optimization Algorithms with a Surrogate Function
Youhei Akimoto
A surrogate function is often employed to reduce the number of objective function evaluations for optimization. However, the effect of using a surrogate model in evolutionary appro…
A Two-phase Framework with a Bézier Simplex-based Interpolation Method for Computationally Expensive Multi-objective Optimization
Ryoji Tanabe, Youhei Akimoto, Ken Kobayashi +3
This paper proposes a two-phase framework with a Bézier simplex-based interpolation method (TPB) for computationally expensive multi-objective optimization. The first phase in TPB…
Unsupervised Causal Binary Concepts Discovery with VAE for Black-box Model Explanation
Thien Q. Tran, Kazuto Fukuchi, Youhei Akimoto +1
We aim to explain a black-box classifier with the form: `data X is classified as class Y because X \textit{has} A, B and \textit{does not have} C' in which A, B, and C are high-lev…
Level Generation for Angry Birds with Sequential VAE and Latent Variable Evolution
Takumi Tanabe, Kazuto Fukuchi, Jun Sakuma +1
Video game level generation based on machine learning (ML), in particular, deep generative models, has attracted attention as a technique to automate level generation. However, app…