63 citations · 230 across the 28 of their papers we have counts for
12 papers · 1 filter
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…
Distance Metric Learning for Graph Structured Data
Tomoki Yoshida, Ichiro Takeuchi, Masayuki Karasuyama
Graphs are versatile tools for representing structured data. As a result, a variety of machine learning methods have been studied for graph data analysis. Although many such learni…
Active learning for level set estimation under input uncertainty and its extensions
Yu Inatsu, Masayuki Karasuyama, Keiichi Inoue +1
Testing under what conditions the product satisfies the desired properties is a fundamental problem in manufacturing industry. If the condition and the property are respectively re…
Statistically Discriminative Sub-trajectory Mining
Vo Nguyen Le Duy, Takuto Sakuma, Taiju Ishiyama +7
We study the problem of discriminative sub-trajectory mining. Given two groups of trajectories, the goal of this problem is to extract moving patterns in the form of sub-trajectori…
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…
Safe Triplet Screening for Distance Metric Learning
Tomoki Yoshida, Ichiro Takeuchi, Masayuki Karasuyama
We study safe screening for metric learning. Distance metric learning can optimize a metric over a set of triplets, each one of which is defined by a pair of same class instances a…
Safe Pattern Pruning: An Efficient Approach for Predictive Pattern Mining
Kazuya Nakagawa, Shinya Suzumura, Masayuki Karasuyama +2
In this paper we study predictive pattern mining problems where the goal is to construct a predictive model based on a subset of predictive patterns in the database. Our main contr…
Simultaneous Safe Screening of Features and Samples in Doubly Sparse Modeling
Atsushi Shibagaki, Masayuki Karasuyama, Kohei Hatano +1
The problem of learning a sparse model is conceptually interpreted as the process of identifying active features/samples and then optimizing the model over them. Recently introduce…
Homotopy Continuation Approaches for Robust SV Classification and Regression
Shinya Suzumura, Kohei Ogawa, Masashi Sugiyama +2
In support vector machine (SVM) applications with unreliable data that contains a portion of outliers, non-robustness of SVMs often causes considerable performance deterioration. A…
Safe Feature Pruning for Sparse High-Order Interaction Models
Kazuya Nakagawa, Shinya Suzumura, Masayuki Karasuyama +2
Taking into account high-order interactions among covariates is valuable in many practical regression problems. This is, however, computationally challenging task because the numbe…
Regularization Path of Cross-Validation Error Lower Bounds
Atsushi Shibagaki, Yoshiki Suzuki, Masayuki Karasuyama +1
Careful tuning of a regularization parameter is indispensable in many machine learning tasks because it has a significant impact on generalization performances. Nevertheless, curre…