10 citations · 23 across the 9 of their papers we have counts for
16 papers
Bayesian Optimization for Simultaneous Selection of Machine Learning Algorithms and Hyperparameters on Shared Latent Space
Kazuki Ishikawa, Ryota Ozaki, Yohei Kanzaki +2
Selecting the optimal combination of a machine learning (ML) algorithm and its hyper-parameters is crucial for the development of high-performance ML systems. However, since the co…
Pareto-frontier Entropy Search with Variational Lower Bound Maximization
Masanori Ishikura, Masayuki Karasuyama
This study considers multi-objective Bayesian optimization (MOBO) through the information gain of the Pareto-frontier. To calculate the information gain, a predictive distribution…
Multi-Objective Bayesian Optimization with Active Preference Learning
Ryota Ozaki, Kazuki Ishikawa, Youhei Kanzaki +4
There are a lot of real-world black-box optimization problems that need to optimize multiple criteria simultaneously. However, in a multi-objective optimization (MOO) problem, iden…
Bayesian Optimization for Distributionally Robust Chance-constrained Problem
Yu Inatsu, Shion Takeno, Masayuki Karasuyama +1
In black-box function optimization, we need to consider not only controllable design variables but also uncontrollable stochastic environment variables. In such cases, it is necess…
Computational Design of Stable and Highly Ion-conductive Materials using Multi-objective Bayesian Optimization: Case Studies on Diffusion of Oxygen and Lithium
Masayuki Karasuyama, Hiroki Kasugai, Tomoyuki Tamura +1
Ion-conducting solid electrolytes are widely used for a variety of purposes. Therefore, designing highly ion-conductive materials is in strongly demand. Because of advancement in c…
Cost-effective search for lower-error region in material parameter space using multifidelity Gaussian process modeling
Shion Takeno, Yuhki Tsukada, Hitoshi Fukuoka +3
Information regarding precipitate shapes is critical for estimating material parameters. Hence, we considered estimating a region of material parameter space in which a computation…