Mahito Sugiyama | DeepAI

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∙ 09/30/2022

Many-Body Approximation for Tensors

We propose a nonnegative tensor decomposition with focusing on the relat...

0 Kazu Ghalamkari, et al. ∙

∙ 05/25/2022

A Neural Tangent Kernel Formula for Ensembles of Soft Trees with Arbitrary Architectures

A soft tree is an actively studied variant of a decision tree that updat...

0 Ryuichi Kanoh, et al. ∙

∙ 10/25/2021

Fast Rank-1 NMF for Missing Data with KL Divergence

We propose a fast non-gradient based method of rank-1 non-negative matri...

0 Kazu Ghalamkari, et al. ∙

∙ 09/10/2021

A Neural Tangent Kernel Perspective of Infinite Tree Ensembles

In practical situations, the ensemble tree model is one of the most popu...

0 Ryuichi Kanoh, et al. ∙

∙ 03/05/2021

Unintended Effects on Adaptive Learning Rate for Training Neural Network with Output Scale Change

A multiplicative constant scaling factor is often applied to the model o...

0 Ryuichi Kanoh, et al. ∙

∙ 03/04/2021

A Closed Form Solution to Best Rank-1 Tensor Approximation via KL divergence Minimization

Tensor decomposition is a fundamentally challenging problem. Even the si...

0 Kazu Ghalamkari, et al. ∙

∙ 06/16/2020

Additive Poisson Process: Learning Intensity of Higher-Order Interaction in Stochastic Processes

We present the Additive Poisson Process (APP), a novel framework that ca...

0 Simon Luo, et al. ∙

∙ 06/09/2020

Rank Reduction, Matrix Balancing, and Mean-Field Approximation on Statistical Manifold

We present a unified view of three different problems; rank reduction of...

0 Kazu Ghalamkari, et al. ∙

∙ 06/08/2020

A Geometric Look at Double Descent Risk: Volumes, Singularities, and Distinguishabilities

The appearance of the double-descent risk phenomenon has received growin...

1 Prasad Cheema, et al. ∙

∙ 09/25/2019

Hierarchical Probabilistic Model for Blind Source Separation via Legendre Transformation

We present a novel blind source separation (BSS) method, called informat...

12 Simon Luo, et al. ∙

∙ 06/28/2019

Bias-Variance Trade-Off in Hierarchical Probabilistic Models Using Higher-Order Feature Interactions

Hierarchical probabilistic models are able to use a large number of para...

0 Simon Luo, et al. ∙

∙ 12/08/2018

Learning Graph Representation via Formal Concept Analysis

We present a novel method that can learn a graph representation from mul...

0 Yuka Yoneda, et al. ∙

∙ 05/21/2018

Transductive Boltzmann Machines

We present transductive Boltzmann machines (TBMs), which firstly achieve...

0 Mahito Sugiyama, et al. ∙

∙ 02/13/2018

Legendre Tensor Decomposition

We present a novel nonnegative tensor decomposition method, called Legen...

0 Mahito Sugiyama, et al. ∙

∙ 02/28/2017

Finding Significant Combinations of Continuous Features

We present an efficient feature selection method that can find all multi...

0 Mahito Sugiyama, et al. ∙

∙ 02/27/2017

Tensor Balancing on Statistical Manifold

We solve tensor balancing, rescaling an Nth order nonnegative tensor by ...

0 Mahito Sugiyama, et al. ∙

∙ 02/15/2016

Selective Inference Approach for Statistically Sound Predictive Pattern Mining

Discovering statistically significant patterns from databases is an impo...

0 Shinya Suzumura, et al. ∙

∙ 02/15/2015

Fast and Memory-Efficient Significant Pattern Mining via Permutation Testing

We present a novel algorithm, Westfall-Young light, for detecting patter...

0 Felipe Llinares-Lopez, et al. ∙

∙ 07/04/2014

Identifying Higher-order Combinations of Binary Features

Finding statistically significant interactions between binary variables ...

0 Felipe Llinares, et al. ∙

∙ 07/01/2014

Significant Subgraph Mining with Multiple Testing Correction

The problem of finding itemsets that are statistically significantly enr...

0 Mahito Sugiyama, et al. ∙

∙ 11/10/2012

Efficient network-guided multi-locus association mapping with graph cuts

As an increasing number of genome-wide association studies reveal the li...

0 Chloé-Agathe Azencott, et al. ∙

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