11 citations · 19 across the 7 of their papers we have counts for
6 papers · 1 filter
Tight and fast generalization error bound of graph embedding in metric space
Atsushi Suzuki, Atsushi Nitanda, Taiji Suzuki +3
Recent studies have experimentally shown that we can achieve in non-Euclidean metric space effective and efficient graph embedding, which aims to obtain the vertices' representatio…
Detecting Hierarchical Changes in Latent Variable Models
Shintaro Fukushima, Kenji Yamanishi
This paper addresses the issue of detecting hierarchical changes in latent variable models (HCDL) from data streams. There are three different levels of changes for latent variable…
Adaptive Minimax Regret against Smooth Logarithmic Losses over High-Dimensional -Balls via Envelope Complexity
Kohei Miyaguchi, Kenji Yamanishi
We develop a new theoretical framework, the \emph{envelope complexity}, to analyze the minimax regret with logarithmic loss functions and derive a Bayesian predictor that adaptivel…
Stable Geodesic Update on Hyperbolic Space and its Application to Poincare Embeddings
Yosuke Enokida, Atsushi Suzuki, Kenji Yamanishi
A hyperbolic space has been shown to be more capable of modeling complex networks than a Euclidean space. This paper proposes an explicit update rule along geodesics in a hyperboli…
High-dimensional Penalty Selection via Minimum Description Length Principle
Kohei Miyaguchi, Kenji Yamanishi
We tackle the problem of penalty selection of regularization on the basis of the minimum description length (MDL) principle. In particular, we consider that the design space of the…
Predicting Glaucoma Visual Field Loss by Hierarchically Aggregating Clustering-based Predictors
Motohide Higaki, Kai Morino, Hiroshi Murata +2
This study addresses the issue of predicting the glaucomatous visual field loss from patient disease datasets. Our goal is to accurately predict the progress of the disease in indi…