75 citations · 459 across the 65 of their papers we have counts for
6 papers · 2 filters
Bayesian Active Learning for Structured Output Design
Kota Matsui, Shunya Kusakawa, Keisuke Ando +3
In this paper, we propose an active learning method for an inverse problem that aims to find an input that achieves a desired structured-output. The proposed method provides new ac…
Bayesian Experimental Design for Finding Reliable Level Set under Input Uncertainty
Shogo Iwazaki, Yu Inatsu, Ichiro Takeuchi
In the manufacturing industry, it is often necessary to repeat expensive operational testing of machine in order to identify the range of input conditions under which the machine o…
Computing Full Conformal Prediction Set with Approximate Homotopy
Eugene Ndiaye, Ichiro Takeuchi
If you are predicting the label of a new object with , how confident are you that ? Conformal prediction methods provide an elegant framework for answering…
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…