Search Results for author:
Found 11 papers, 1 papers with code
Multi-Objective Bayesian Optimization with Active Preference Learning
There are a lot of real-world black-box optimization problems that need to optimize multiple criteria simultaneously.
Posterior Sampling-Based Bayesian Optimization with Tighter Bayesian Regret Bounds
We show that PIMS achieves the tighter BCR bound and avoids the hyperparameter tuning, unlike GP-UCB.
Towards Practical Preferential Bayesian Optimization with Skew Gaussian Processes
This observation motivates us to improve the MCMC-based estimation for skew GP, for which we show the practical efficiency of Gibbs sampling and derive the low variance MC estimator.
Randomized Gaussian Process Upper Confidence Bound with Tighter Bayesian Regret Bounds
Gaussian process upper confidence bound (GP-UCB) is a theoretically promising approach for black-box optimization; however, the confidence parameter $\beta$ is considerably large in the theorem and chosen heuristically in practice.
Bounding Box-based Multi-objective Bayesian Optimization of Risk Measures under Input Uncertainty
In this study, we propose a novel multi-objective Bayesian optimization (MOBO) method to efficiently identify the Pareto front (PF) defined by risk measures for black-box functions under the presence of input uncertainty (IU).
Bayesian Optimization for Distributionally Robust Chance-constrained Problem
In black-box function optimization, we need to consider not only controllable design variables but also uncontrollable stochastic environment variables.
Bayesian Optimization for Cascade-type Multi-stage Processes
A cascade process is a multistage process in which the output of one stage is used as an input for the subsequent stage.
Sequential- and Parallel- Constrained Max-value Entropy Search via Information Lower Bound
Max-value entropy search (MES) is one of the state-of-the-art approaches in Bayesian optimization (BO).
Cost-effective search for lower-error region in material parameter space using multifidelity Gaussian process modeling
Hence, we considered estimating a region of material parameter space in which a computational model produces precipitates having shapes similar to those observed in the experimental images.
Multi-objective Bayesian Optimization using Pareto-frontier Entropy
The entropy search is successful approach to Bayesian optimization.
Multi-fidelity Bayesian Optimization with Max-value Entropy Search and its parallelization
In this paper, we focus on the information-based approach, which is a popular and empirically successful approach in BO.
