7 citations · 32 across the 24 of their papers we have counts for
4 papers · 1 filter
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…
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…
Computing Valid p-values for Image Segmentation by Selective Inference
Kosuke Tanizaki, Noriaki Hashimoto, Yu Inatsu +2
Image segmentation is one of the most fundamental tasks of computer vision. In many practical applications, it is essential to properly evaluate the reliability of individual segme…
Active learning for enumerating local minima based on Gaussian process derivatives
Yu Inatsu, Daisuke Sugita, Kazuaki Toyoura +1
We study active learning (AL) based on Gaussian Processes (GPs) for efficiently enumerating all of the local minimum solutions of a black-box function. This problem is challenging…