Zobrazeno 1 - 10
of 58
pro vyhledávání: '"Inatsu, Yu"'
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 applications. When the function is expensive-to-evaluate and black-box, the \tex
Externí odkaz:
http://arxiv.org/abs/2408.03144
Autor:
Hanada, Hiroyuki, Tatsuya, Aoyama, Satoshi, Akahane, Tanaka, Tomonari, Okura, Yoshito, Inatsu, Yu, Hashimoto, Noriaki, Takeno, Shion, Murayama, Taro, Lee, Hanju, Kojima, Shinya, Takeuchi, Ichiro
In this study, we propose a machine learning method called Distributionally Robust Safe Sample Screening (DRSSS). DRSSS aims to identify unnecessary training samples, even when the distribution of the training samples changes in the future. To achiev
Externí odkaz:
http://arxiv.org/abs/2406.05964
Autor:
Hanada, Hiroyuki, Akahane, Satoshi, Aoyama, Tatsuya, Tanaka, Tomonari, Okura, Yoshito, Inatsu, Yu, Hashimoto, Noriaki, Murayama, Taro, Hanju, Lee, Kojima, Shinya, Takeuchi, Ichiro
In this study, we propose a method Distributionally Robust Safe Screening (DRSS), for identifying unnecessary samples and features within a DR covariate shift setting. This method effectively combines DR learning, a paradigm aimed at enhancing model
Externí odkaz:
http://arxiv.org/abs/2404.16328
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 established theoretical properties regarding Bayesian cumulative regret (
Externí odkaz:
http://arxiv.org/abs/2311.03760
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. Then, randomized G
Externí odkaz:
http://arxiv.org/abs/2302.01511
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). Existing BO methods for
Externí odkaz:
http://arxiv.org/abs/2301.11588
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 necessary to solve the optimization problem by taking into account the uncer
Externí odkaz:
http://arxiv.org/abs/2201.13112
Autor:
Kusakawa, Shunya, Takeno, Shion, Inatsu, Yu, Kutsukake, Kentaro, Iwazaki, Shogo, Nakano, Takashi, Ujihara, Toru, Karasuyama, Masayuki, Takeuchi, Ichiro
Publikováno v:
Neural Computation (2022) 34 (12): 2408-2431
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 called a cascade process. A cascade process is a multistage process in w
Externí odkaz:
http://arxiv.org/abs/2111.08330
In this paper, we study statistical inference of change-points (CPs) in multi-dimensional sequence. In CP detection from a multi-dimensional sequence, it is often desirable not only to detect the location, but also to identify the subset of the compo
Externí odkaz:
http://arxiv.org/abs/2110.08989
In practical data analysis under noisy environment, it is common to first use robust methods to identify outliers, and then to conduct further analysis after removing the outliers. In this paper, we consider statistical inference of the model estimat
Externí odkaz:
http://arxiv.org/abs/2104.10840