research
∙
08/04/2023
A stochastic optimization approach to train non-linear neural networks with a higher-order variation regularization
While highly expressive parametric models including deep neural networks...
share
research
∙
07/11/2023
A stochastic optimization approach to minimize robust density power-based divergences for general parametric density models
Density power divergence (DPD) [Basu et al. (1998), Biometrika], which i...
research
∙
06/28/2023
Forecasting of the development of a partially-observed dynamical time series with the aid of time-invariance and linearity
A dynamical system produces a dependent multivariate sequence called dyn...
research
∙
03/31/2023
An interpretable neural network-based non-proportional odds model for ordinal regression with continuous response
This study proposes an interpretable neural network-based non-proportion...
research
∙
04/18/2022
A Greedy and Optimistic Approach to Clustering with a Specified Uncertainty of Covariates
In this study, we examine a clustering problem in which the covariates o...
research
∙
12/28/2021
Improving Nonparametric Classification via Local Radial Regression with an Application to Stock Prediction
For supervised classification problems, this paper considers estimating ...
research
∙
12/07/2021
A generalization gap estimation for overparameterized models via the Langevin functional variance
This paper discusses the estimation of the generalization gap, the diffe...
research
∙
12/01/2021
Minimax Analysis for Inverse Risk in Nonparametric Planer Invertible Regression
We study a minimax risk of estimating inverse functions on a plane, whil...
research
∙
12/24/2020
Dependence of variance on covariate design in nonparametric link regression
This note discusses a nonparametric approach to link regression aiming a...
research
∙
05/02/2020
Stochastic Neighbor Embedding of Multimodal Relational Data for Image-Text Simultaneous Visualization
Multimodal relational data analysis has become of increasing importance ...
research
∙
02/08/2020
Extrapolation Towards Imaginary 0-Nearest Neighbour and Its Improved Convergence Rate
k-nearest neighbour (k-NN) is one of the simplest and most widely-used m...
research
∙
07/22/2019
Hyperlink Regression via Bregman Divergence
A collection of U (∈N) data vectors is called a U-tuple, and the assoc...
research
∙
02/27/2019
Representation Learning with Weighted Inner Product for Universal Approximation of General Similarities
We propose weighted inner product similarity (WIPS) for neural-network b...
research
∙
02/22/2019
Robust Graph Embedding with Noisy Link Weights
We propose β-graph embedding for robustly learning feature vectors from ...
research
∙
10/04/2018
Graph Embedding with Shifted Inner Product Similarity and Its Improved Approximation Capability
We propose shifted inner-product similarity (SIPS), which is a novel yet...
research
∙
05/31/2018
On representation power of neural network-based graph embedding and beyond
The representation power of similarity functions used in neural network-...
research
∙
02/13/2018
