23 citations · 56 across the 24 of their papers we have counts for
24 papers
Improved Regret Analysis for Parallel Gaussian Process Bandit Optimization
Shion Takeno, Shogo Iwazaki
This paper studies the regret analysis for parallel Gaussian process (GP) bandit optimization. The known regret upper bounds for the widely used GP batched upper confidence bound a…
Towards Regret Guarantees for One-Step Lookahead Bayesian Optimization
Shion Takeno
This paper studies theoretical guarantees of a one-step lookahead Bayesian optimization (BO) method. Although the empirical effectiveness of one-step lookahead BO methods, such as…
Safe Distributionally Robust Feature Selection under Covariate Shift
Hiroyuki Hanada, Satoshi Akahane, Noriaki Hashimoto +2
In practical machine learning, the environments encountered during the model development and deployment phases often differ, especially when a model is used by many users in divers…
On Regret Bounds of Thompson Sampling for Bayesian Optimization
Shion Takeno, Shogo Iwazaki
We study a widely used Bayesian optimization method, Gaussian process Thompson sampling (GP-TS), under the assumption that the objective function is a sample path from a GP. Compar…
Randomized Kriging Believer for Parallel Bayesian Optimization with Regret Bounds
Shuhei Sugiura, Ichiro Takeuchi, Shion Takeno
We consider the optimization problem of an expensive-to-evaluate black-box function, in which we can obtain noisy function values in parallel. For this problem, parallel Bayesian o…
Regret Analysis of Posterior Sampling-Based Expected Improvement for Bayesian Optimization
Shion Takeno, Yu Inatsu, Masayuki Karasuyama +1
Bayesian optimization is a powerful tool for optimizing an expensive-to-evaluate black-box function. In particular, the effectiveness of expected improvement (EI) has been demonstr…
Dose-finding design based on level set estimation in phase I cancer clinical trials
Keiichiro Seno, Kota Matsui, Shogo Iwazaki +3
The primary objective of phase I cancer clinical trials is to evaluate the safety of a new experimental treatment and to find the maximum tolerated dose (MTD). We show that the MTD…
Improved Regret Analysis in Gaussian Process Bandits: Optimality for Noiseless Reward, RKHS norm, and Non-Stationary Variance
Shogo Iwazaki, Shion Takeno
We study the Gaussian process (GP) bandit problem, whose goal is to minimize regret under an unknown reward function lying in some reproducing kernel Hilbert space (RKHS). The maxi…
Distributionally Robust Active Learning for Gaussian Process Regression
Shion Takeno, Yoshito Okura, Yu Inatsu +9
Gaussian process regression (GPR) or kernel ridge regression is a widely used and powerful tool for nonlinear prediction. Therefore, active learning (AL) for GPR, which actively co…
Near-Optimal Algorithm for Non-Stationary Kernelized Bandits
Shogo Iwazaki, Shion Takeno
This paper studies a non-stationary kernelized bandit (KB) problem, also called time-varying Bayesian optimization, where one seeks to minimize the regret under an unknown reward f…
Regret Analysis for Randomized Gaussian Process Upper Confidence Bound
Shion Takeno, Yu Inatsu, Masayuki Karasuyama
Gaussian process upper confidence bound (GP-UCB) is a theoretically established algorithm for Bayesian optimization (BO), where we assume the objective function follows a GP. O…
Active Learning for Level Set Estimation Using Randomized Straddle Algorithms
Yu Inatsu, Shion Takeno, Kentaro Kutsukake +1
Level set estimation (LSE), the problem of identifying the set of input points where a function takes value above (or below) a given threshold, is important in practical applicatio…
Distributionally Robust Safe Sample Elimination under Covariate Shift
Hiroyuki Hanada, Tatsuya Aoyama, Satoshi Akahane +9
We consider a machine learning setup where one training dataset is used to train multiple models across slightly different data distributions. This occurs when customized models ar…
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…
Posterior Sampling-Based Bayesian Optimization with Tighter Bayesian Regret Bounds
Shion Takeno, Yu Inatsu, Masayuki Karasuyama +1
Among various acquisition functions (AFs) in Bayesian optimization (BO), Gaussian process upper confidence bound (GP-UCB) and Thompson sampling (TS) are well-known options with est…
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…
Randomized Gaussian Process Upper Confidence Bound with Tighter Bayesian Regret Bounds
Shion Takeno, Yu Inatsu, Masayuki Karasuyama
Gaussian process upper confidence bound (GP-UCB) is a theoretically promising approach for black-box optimization; however, the confidence parameter is considerably large in th…
Bounding Box-based Multi-objective Bayesian Optimization of Risk Measures under Input Uncertainty
Yu Inatsu, Shion Takeno, Hiroyuki Hanada +2
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…
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…
Bayesian Optimization for Cascade-type Multi-stage Processes
Shunya Kusakawa, Shion Takeno, Yu Inatsu +6
Complex processes in science and engineering are often formulated as multistage decision-making problems. In this paper, we consider a type of multistage decision-making process ca…
Sequential- and Parallel- Constrained Max-value Entropy Search via Information Lower Bound
Shion Takeno, Tomoyuki Tamura, Kazuki Shitara +1
Max-value entropy search (MES) is one of the state-of-the-art approaches in Bayesian optimization (BO). In this paper, we propose a novel variant of MES for constrained problems, 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…
Multi-objective Bayesian Optimization using Pareto-frontier Entropy
Shinya Suzuki, Shion Takeno, Tomoyuki Tamura +2
This paper studies an entropy-based multi-objective Bayesian optimization (MBO). The entropy search is successful approach to Bayesian optimization. However, for MBO, existing entr…
Multi-fidelity Bayesian Optimization with Max-value Entropy Search and its parallelization
Shion Takeno, Hitoshi Fukuoka, Yuhki Tsukada +4
In a standard setting of Bayesian optimization (BO), the objective function evaluation is assumed to be highly expensive. Multi-fidelity Bayesian optimization (MFBO) accelerates BO…