111 citations · 283 across the 44 of their papers we have counts for
8 papers · 1 filter
Active Learning by Query by Committee with Robust Divergences
Hideitsu Hino, Shinto Eguchi
Active learning is a widely used methodology for various problems with high measurement costs. In active learning, the next object to be measured is selected by an acquisition func…
Unsupervised Domain Adaptation for Extra Features in the Target Domain Using Optimal Transport
Toshimitsu Aritake, Hideitsu Hino
Domain adaptation aims to transfer knowledge of labeled instances obtained from a source domain to a target domain to fill the gap between the domains. Most domain adaptation metho…
Gaussian Process Koopman Mode Decomposition
Takahiro Kawashima, Hideitsu Hino
In this paper, we propose a nonlinear probabilistic generative model of Koopman mode decomposition based on an unsupervised Gaussian process. Existing data-driven methods for Koopm…
Geometry of EM and related iterative algorithms
Hideitsu Hino, Shotaro Akaho, Noboru Murata
The Expectation--Maximization (EM) algorithm is a simple meta-algorithm that has been used for many years as a methodology for statistical inference when there are missing measurem…
Information Geometry of Dropout Training
Masanari Kimura, Hideitsu Hino
Dropout is one of the most popular regularization techniques in neural network training. Because of its power and simplicity of idea, dropout has been analyzed extensively and many…
Gradual Domain Adaptation via Normalizing Flows
Shogo Sagawa, Hideitsu Hino
Standard domain adaptation methods do not work well when a large gap exists between the source and target domains. Gradual domain adaptation is one of the approaches used to addres…
One-bit Submission for Locally Private Quasi-MLE: Its Asymptotic Normality and Limitation
Hajime Ono, Kazuhiro Minami, Hideitsu Hino
Local differential privacy~(LDP) is an information-theoretic privacy definition suitable for statistical surveys that involve an untrusted data curator. An LDP version of quasi-max…
Cost-effective Framework for Gradual Domain Adaptation with Multifidelity
Shogo Sagawa, Hideitsu Hino
In domain adaptation, when there is a large distance between the source and target domains, the prediction performance will degrade. Gradual domain adaptation is one of the solutio…