71 citations · 368 across the 49 of their papers we have counts for
7 papers · 1 filter
An ODE Method to Prove the Geometric Convergence of Adaptive Stochastic Algorithms
Youhei Akimoto, Anne Auger, Nikolaus Hansen
We consider stochastic algorithms derived from methods for solving deterministic optimization problems, especially comparison-based algorithms derived from stochastic approximation…
Adaptive Ranking Based Constraint Handling for Explicitly Constrained Black-Box Optimization
Naoki Sakamoto, Youhei Akimoto
We propose a novel constraint-handling technique for the covariance matrix adaptation evolution strategy (CMA-ES). The proposed technique is aimed at solving explicitly constrained…
Unauthorized AI cannot Recognize Me: Reversible Adversarial Example
Jiayang Liu, Weiming Zhang, Kazuto Fukuchi +2
In this study, we propose a new methodology to control how user's data is recognized and used by AI via exploiting the properties of adversarial examples. For this purpose, we prop…
Parameterless Stochastic Natural Gradient Method for Discrete Optimization and its Application to Hyper-Parameter Optimization for Neural Network
Kouhei Nishida, Hernan Aguirre, Shota Saito +2
Black box discrete optimization (BBDO) appears in wide range of engineering tasks. Evolutionary or other BBDO approaches have been applied, aiming at automating necessary tuning of…
Sample Reuse via Importance Sampling in Information Geometric Optimization
Shinichi Shirakawa, Youhei Akimoto, Kazuki Ouchi +1
In this paper we propose a technique to reduce the number of function evaluations, which is often the bottleneck of the black-box optimization, in the information geometric optimiz…
Drift Theory in Continuous Search Spaces: Expected Hitting Time of the (1+1)-ES with 1/5 Success Rule
Youhei Akimoto, Anne Auger, Tobias Glasmachers
This paper explores the use of the standard approach for proving runtime bounds in discrete domains---often referred to as drift analysis---in the context of optimization on a cont…
Dynamic Optimization of Neural Network Structures Using Probabilistic Modeling
Shinichi Shirakawa, Yasushi Iwata, Youhei Akimoto
Deep neural networks (DNNs) are powerful machine learning models and have succeeded in various artificial intelligence tasks. Although various architectures and modules for the DNN…