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09/29/2021
Breaking the curse of dimensionality with Isolation Kernel
The curse of dimensionality has been studied in different aspects. Howev...
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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...
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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...
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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...
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12/08/2018
Learning Graph Representation via Formal Concept Analysis
We present a novel method that can learn a graph representation from mul...
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02/19/2018
Analysis of Cause-Effect Inference via Regression Errors
We address the problem of inferring the causal relation between two vari...
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10/11/2016
Error Asymmetry in Causal and Anticausal Regression
It is generally difficult to make any statements about the expected pred...
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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...
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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...
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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...
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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 ...
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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...
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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...
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03/01/2012
Learning a Common Substructure of Multiple Graphical Gaussian Models
Properties of data are frequently seen to vary depending on the sampled ...
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02/14/2012
Discovering causal structures in binary exclusive-or skew acyclic models
Discovering causal relations among observed variables in a given data se...
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01/13/2011