48 citations · 70 across the 13 of their papers we have counts for
13 papers
(1+1)-CMA-ES with Margin for Discrete and Mixed-Integer Problems
Yohei Watanabe, Kento Uchida, Ryoki Hamano +3
The covariance matrix adaptation evolution strategy (CMA-ES) is an efficient continuous black-box optimization method. The CMA-ES possesses many attractive features, including inva…
CMA-ES with Learning Rate Adaptation: Can CMA-ES with Default Population Size Solve Multimodal and Noisy Problems?
Masahiro Nomura, Youhei Akimoto, Isao Ono
The covariance matrix adaptation evolution strategy (CMA-ES) is one of the most successful methods for solving black-box continuous optimization problems. One practically useful as…
Towards Practical Preferential Bayesian Optimization with Skew Gaussian Processes
Shion Takeno, Masahiro Nomura, Masayuki Karasuyama
We study preferential Bayesian optimization (BO) where reliable feedback is limited to pairwise comparison called duels. An important challenge in preferential BO, which uses the p…
Marginal Probability-Based Integer Handling for CMA-ES Tackling Single-and Multi-Objective Mixed-Integer Black-Box Optimization
Ryoki Hamano, Shota Saito, Masahiro Nomura +1
This study targets the mixed-integer black-box optimization (MI-BBO) problem where continuous and integer variables should be optimized simultaneously. The CMA-ES, our focus in thi…
CMA-ES with Margin: Lower-Bounding Marginal Probability for Mixed-Integer Black-Box Optimization
Ryoki Hamano, Shota Saito, Masahiro Nomura +1
This study targets the mixed-integer black-box optimization (MI-BBO) problem where continuous and integer variables should be optimized simultaneously. The CMA-ES, our focus in thi…
Fast Moving Natural Evolution Strategy for High-Dimensional Problems
Masahiro Nomura, Isao Ono
In this work, we propose a new variant of natural evolution strategies (NES) for high-dimensional black-box optimization problems. The proposed method, CR-FM-NES, extends a recentl…
ADBCMM : Acronym Disambiguation by Building Counterfactuals and Multilingual Mixing
Yixuan Weng, Fei Xia, Bin Li +2
Scientific documents often contain a large number of acronyms. Disambiguation of these acronyms will help researchers better understand the meaning of vocabulary in the documents.…
Natural Evolution Strategy for Unconstrained and Implicitly Constrained Problems with Ridge Structure
Masahiro Nomura, Isao Ono
In this paper, we propose a new natural evolution strategy for unconstrained black-box function optimization (BBFO) problems and implicitly constrained BBFO problems. BBFO problems…
Warm Starting CMA-ES for Hyperparameter Optimization
Masahiro Nomura, Shuhei Watanabe, Youhei Akimoto +2
Hyperparameter optimization (HPO), formulated as black-box optimization (BBO), is recognized as essential for automation and high performance of machine learning approaches. The CM…
Simple and Scalable Parallelized Bayesian Optimization
Masahiro Nomura
In recent years, leveraging parallel and distributed computational resources has become essential to solve problems of high computational cost. Bayesian optimization (BO) has shown…
Efficient Hyperparameter Optimization under Multi-Source Covariate Shift
Masahiro Nomura, Yuta Saito
A typical assumption in supervised machine learning is that the train (source) and test (target) datasets follow completely the same distribution. This assumption is, however, ofte…
A Simple Heuristic for Bayesian Optimization with A Low Budget
Masahiro Nomura, Kenshi Abe
The aim of black-box optimization is to optimize an objective function within the constraints of a given evaluation budget. In this problem, it is generally assumed that the comput…
Towards Resolving Propensity Contradiction in Offline Recommender Learning
Yuta Saito, Masahiro Nomura
We study offline recommender learning from explicit rating feedback in the presence of selection bias. A current promising solution for the bias is the inverse propensity score (IP…