Takashi Washio | DeepAI

DeepAI

Featured Co-authors

Bernhard Schölkopf
237 publications
Yang Xu
77 publications
Zhi-Hua Zhou
72 publications
Gholamreza Haffari
68 publications
Dominik Janzing
50 publications
Aapo Hyvärinen
41 publications
Yoshinobu Kawahara
32 publications
Ye Zhu
29 publications
Shohei Shimizu
22 publications
Mahito Sugiyama
21 publications
Satoshi Hara
20 publications

∙ 09/29/2021

Breaking the curse of dimensionality with Isolation Kernel

The curse of dimensionality has been studied in different aspects. Howev...

Kai Ming Ting, et al. ∙

∙ 09/24/2020

Isolation Distributional Kernel: A New Tool for Point Group Anomaly Detection

We introduce Isolation Distributional Kernel as a new way to measure the...

Kai Ming Ting, et al. ∙

∙ 07/02/2019

Isolation Kernel: The X Factor in Efficient and Effective Large Scale Online Kernel Learning

Large scale online kernel learning aims to build an efficient and scalab...

Kai Ming Ting, et al. ∙

∙ 02/09/2019

A new simple and effective measure for bag-of-word inter-document similarity measurement

To measure the similarity of two documents in the bag-of-words (BoW) vec...

Sunil Aryal, 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...

Yuka Yoneda, et al. ∙

∙ 02/19/2018

Analysis of Cause-Effect Inference via Regression Errors

We address the problem of inferring the causal relation between two vari...

Patrick Blöbaum, et al. ∙

∙ 10/11/2016

Error Asymmetry in Causal and Anticausal Regression

It is generally difficult to make any statements about the expected pred...

Patrick Blöbaum, et al. ∙

∙ 08/02/2014

A Bayesian estimation approach to analyze non-Gaussian data-generating processes with latent classes

A large amount of observational data has been accumulated in various fie...

Naoki Tanaka, et al. ∙

∙ 01/22/2014

Causal Discovery in a Binary Exclusive-or Skew Acyclic Model: BExSAM

Discovering causal relations among observed variables in a given data se...

Takanori Inazumi, et al. ∙

∙ 01/22/2014

Identifiability of an Integer Modular Acyclic Additive Noise Model and its Causal Structure Discovery

The notion of causality is used in many situations dealing with uncertai...

Joe Suzuki, et al. ∙

∙ 01/20/2014

Anomaly detection in reconstructed quantum states using a machine-learning technique

The accurate detection of small deviations in given density matrices is ...

Satoshi Hara, et al. ∙

∙ 03/29/2013

ParceLiNGAM: A causal ordering method robust against latent confounders

We consider learning a causal ordering of variables in a linear non-Gaus...

Tatsuya Tashiro, et al. ∙

∙ 04/09/2012

Estimation of causal orders in a linear non-Gaussian acyclic model: a method robust against latent confounders

We consider to learn a causal ordering of variables in a linear non-Gaus...

Tatsuya Tashiro, et al. ∙

∙ 03/01/2012

Learning a Common Substructure of Multiple Graphical Gaussian Models

Properties of data are frequently seen to vary depending on the sampled ...

Satoshi Hara, et al. ∙

∙ 02/14/2012

Discovering causal structures in binary exclusive-or skew acyclic models

Discovering causal relations among observed variables in a given data se...

Takanori Inazumi, et al. ∙

∙ 01/13/2011

DirectLiNGAM: A direct method for learning a linear non-Gaussian structural equation model

Structural equation models and Bayesian networks have been widely used t...

Shohei Shimizu, et al. ∙