Takashi Washio

is this you? claim profile

Professor at Osaka University, The Institute of Scientific and Industrial Research, Professor since 2006, The Institute of Scientific and Industrial Research, Associate Professor from 1996-2006

Featured Co-authors

Bernhard Schölkopf
187 publications
Zhi-Hua Zhou
65 publications
Yang Xu
50 publications
Gholamreza Haffari
49 publications
Dominik Janzing
43 publications
Aapo Hyvärinen
36 publications
Yoshinobu Kawahara
28 publications
Shohei Shimizu
22 publications
Mahito Sugiyama
19 publications
Ye Zhu
19 publications
Satoshi Hara
18 publications

Breaking the curse of dimensionality with Isolation Kernel

The curse of dimensionality has been studied in different aspects. Howev...
09/29/2021 ∙ by Kai Ming Ting, et al. ∙ 0 ∙ share

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

We introduce Isolation Distributional Kernel as a new way to measure the...
09/24/2020 ∙ by Kai Ming Ting, et al. ∙ 4 ∙ share

read it
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...
07/02/2019 ∙ by Kai Ming Ting, et al. ∙ 0 ∙ share

read it
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...
02/09/2019 ∙ by Sunil Aryal, et al. ∙ 0 ∙ share

read it
Learning Graph Representation via Formal Concept Analysis

We present a novel method that can learn a graph representation from mul...
12/08/2018 ∙ by Yuka Yoneda, et al. ∙ 0 ∙ share

read it
Analysis of Cause-Effect Inference via Regression Errors

We address the problem of inferring the causal relation between two vari...
02/19/2018 ∙ by Patrick Blöbaum, et al. ∙ 0 ∙ share

read it
Error Asymmetry in Causal and Anticausal Regression

It is generally difficult to make any statements about the expected pred...
10/11/2016 ∙ by Patrick Blöbaum, et al. ∙ 0 ∙ share

read it
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...
08/02/2014 ∙ by Naoki Tanaka, et al. ∙ 0 ∙ share

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

Discovering causal relations among observed variables in a given data se...
01/22/2014 ∙ by Takanori Inazumi, et al. ∙ 0 ∙ share

read it
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...
01/22/2014 ∙ by Joe Suzuki, et al. ∙ 0 ∙ share

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

The accurate detection of small deviations in given density matrices is ...
01/20/2014 ∙ by Satoshi Hara, et al. ∙ 0 ∙ share

read it
ParceLiNGAM: A causal ordering method robust against latent confounders

We consider learning a causal ordering of variables in a linear non-Gaus...
03/29/2013 ∙ by Tatsuya Tashiro, et al. ∙ 0 ∙ share

read it
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...
04/09/2012 ∙ by Tatsuya Tashiro, et al. ∙ 0 ∙ share

read it
Learning a Common Substructure of Multiple Graphical Gaussian Models

Properties of data are frequently seen to vary depending on the sampled ...
03/01/2012 ∙ by Satoshi Hara, et al. ∙ 0 ∙ share

read it
Discovering causal structures in binary exclusive-or skew acyclic models

Discovering causal relations among observed variables in a given data se...
02/14/2012 ∙ by Takanori Inazumi, et al. ∙ 0 ∙ share

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

Structural equation models and Bayesian networks have been widely used t...
01/13/2011 ∙ by Shohei Shimizu, et al. ∙ 0 ∙ share

read it