
Breaking the curse of dimensionality with Isolation Kernel
The curse of dimensionality has been studied in different aspects. Howev...
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...
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...
read it

A new simple and effective measure for bagofword interdocument similarity measurement
To measure the similarity of two documents in the bagofwords (BoW) vec...
read it

Learning Graph Representation via Formal Concept Analysis
We present a novel method that can learn a graph representation from mul...
read it

Analysis of CauseEffect Inference via Regression Errors
We address the problem of inferring the causal relation between two vari...
read it

Error Asymmetry in Causal and Anticausal Regression
It is generally difficult to make any statements about the expected pred...
read it

A Bayesian estimation approach to analyze nonGaussian datagenerating processes with latent classes
A large amount of observational data has been accumulated in various fie...
read it

Causal Discovery in a Binary Exclusiveor Skew Acyclic Model: BExSAM
Discovering causal relations among observed variables in a given data se...
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...
read it

Anomaly detection in reconstructed quantum states using a machinelearning technique
The accurate detection of small deviations in given density matrices is ...
read it

ParceLiNGAM: A causal ordering method robust against latent confounders
We consider learning a causal ordering of variables in a linear nonGaus...
read it

Estimation of causal orders in a linear nonGaussian acyclic model: a method robust against latent confounders
We consider to learn a causal ordering of variables in a linear nonGaus...
read it

Learning a Common Substructure of Multiple Graphical Gaussian Models
Properties of data are frequently seen to vary depending on the sampled ...
read it

Discovering causal structures in binary exclusiveor skew acyclic models
Discovering causal relations among observed variables in a given data se...
read it

DirectLiNGAM: A direct method for learning a linear nonGaussian structural equation model
Structural equation models and Bayesian networks have been widely used t...
read it
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 19962006