30 citations · 68 across the 14 of their papers we have counts for
19 papers
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…
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…
Fast symplectic integrator for Nesterov-type acceleration method
Shin-itiro Goto, Hideitsu Hino
In this paper, explicit stable integrators based on symplectic and contact geometries are proposed for a non-autonomous ordinarily differential equation (ODE) found in improving co…
-Geodesical Skew Divergence
Masanari Kimura, Hideitsu Hino
The asymmetric skew divergence smooths one of the distributions by mixing it, to a degree determined by the parameter , with the other distribution. Such divergence is an approx…
Stopping Criterion for Active Learning Based on Error Stability
Hideaki Ishibashi, Hideitsu Hino
Active learning is a framework for supervised learning to improve the predictive performance by adaptively annotating a small number of samples. To realize efficient active learnin…