Search Results for author: Takashi Washio

Found 14 papers, 1 papers with code

Breaking the curse of dimensionality with Isolation Kernel

no code implementations29 Sep 2021 Kai Ming Ting, Takashi Washio, Ye Zhu, Yang Xu

The curse of dimensionality has been studied in different aspects.

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

1 code implementation24 Sep 2020 Kai Ming Ting, Bi-Cun Xu, Takashi Washio, Zhi-Hua Zhou

Existing approaches based on kernel mean embedding, which convert a point kernel to a distributional kernel, have two key issues: the point kernel employed has a feature map with intractable dimensionality; and it is {\em data independent}.

Group Anomaly Detection

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

no code implementations2 Jul 2019 Kai Ming Ting, Jonathan R. Wells, Takashi Washio

A current key approach focuses on ways to produce an approximate finite-dimensional feature map, assuming that the kernel used has a feature map with intractable dimensionality---an assumption traditionally held in kernel-based methods.

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

no code implementations9 Feb 2019 Sunil Aryal, Kai Ming Ting, Takashi Washio, Gholamreza Haffari

To measure the similarity of two documents in the bag-of-words (BoW) vector representation, different term weighting schemes are used to improve the performance of cosine similarity---the most widely used inter-document similarity measure in text mining.

Learning Graph Representation via Formal Concept Analysis

no code implementations8 Dec 2018 Yuka Yoneda, Mahito Sugiyama, Takashi Washio

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

Analysis of cause-effect inference by comparing regression errors

no code implementations19 Feb 2018 Patrick Blöbaum, Dominik Janzing, Takashi Washio, Shohei Shimizu, Bernhard Schölkopf

We address the problem of inferring the causal direction between two variables by comparing the least-squares errors of the predictions in both possible directions.

Causal Inference

Error Asymmetry in Causal and Anticausal Regression

no code implementations11 Oct 2016 Patrick Blöbaum, Takashi Washio, Shohei Shimizu

It is generally difficult to make any statements about the expected prediction error in an univariate setting without further knowledge about how the data were generated.

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

no code implementations2 Aug 2014 Naoki Tanaka, Shohei Shimizu, Takashi Washio

A large amount of observational data has been accumulated in various fields in recent times, and there is a growing need to estimate the generating processes of these data.

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

no code implementations22 Jan 2014 Takanori Inazumi, Takashi Washio, Shohei Shimizu, Joe Suzuki, Akihiro Yamamoto, Yoshinobu Kawahara

Discovering causal relations among observed variables in a given data set is a major objective in studies of statistics and artificial intelligence.

Causal Discovery

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

no code implementations20 Jan 2014 Satoshi Hara, Takafumi Ono, Ryo Okamoto, Takashi Washio, Shigeki Takeuchi

We demonstrate that the proposed method can more accurately detect small erroneous deviations in reconstructed density matrices, which contain intrinsic fluctuations due to the limited number of samples, than a naive method of checking the trace distance from the average of the given density matrices.

Anomaly Detection

ParceLiNGAM: A causal ordering method robust against latent confounders

no code implementations29 Mar 2013 Tatsuya Tashiro, Shohei Shimizu, Aapo Hyvarinen, Takashi Washio

In this paper, we propose a new algorithm for learning causal orders that is robust against one typical violation of the model assumptions: latent confounders.

Weighted Likelihood Policy Search with Model Selection

no code implementations NeurIPS 2012 Tsuyoshi Ueno, Kohei Hayashi, Takashi Washio, Yoshinobu Kawahara

Reinforcement learning (RL) methods based on direct policy search (DPS) have been actively discussed to achieve an efficient approach to complicated Markov decision processes (MDPs).

Model Selection reinforcement-learning

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