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Committee Date Time Place Paper Title / Authors Abstract Paper #
Okinawa Miyakojima Marine Terminal Visualization of Relational data by Embedding to Direct Product Space
Kazuki Miyazaki, Ryuji Watanabe, Tetsuo Furukawa (Kyutech) NC2019-63
The aim of this work is to develop a modeling method of relational data. Relational data is a dataset observed obtained ... [more] NC2019-63
NC, IPSJ-BIO, IBISML, IPSJ-MPS [detail] 2016-07-06
Okinawa Okinawa Institute of Science and Technology A Supervised Learning Approach to Causal Inference for Bivariate Time Series
Yoichi Chikahara, Akinori Fujino (NTT) IBISML2016-2
Causal inference in time series is a problem to estimate the underlying causal relationship between time-dependent varia... [more] IBISML2016-2
Iwate Hotel Hanamaki [Invited Talk] Karnel method for Bayesian inference and its applications
Kenji Fukumizu (ISM) SP2014-69
As a kernel framework for statsitical inference, "kernel mean embedding" has been recently developed, in which probabili... [more] SP2014-69
IBISML 2013-11-13
Tokyo Tokyo Institute of Technology, Kuramae-Kaikan [Poster Presentation] Distributional Statistics Estimation via Kernel Mean Embeddings -- Density Function, Credible interval, and Moment Estimation --
Motonobu Kanagawa (SOKENDAI), Kenji Fukumizu (ISM) IBISML2013-55
The RKHS embedding approach for nonparametric statistical inference, in which probability distributions are represented ... [more] IBISML2013-55
NLP 2011-03-10
Tokyo Tokyo University of Science A Manifold Learning Approach for Analyzing Chaos in A Dripping Faucet System
Hiromichi Suetani (Kagoshima Univ./JST/RIKEN), Hiroki Kuroiwa, Hiroki Hata (Kagoshima Univ.), Shotaro Akaho (AIST) NLP2010-173
Dripping water from a faucet is very familiar to us and it provides various nonlinear phenomena including chaos. When i... [more] NLP2010-173
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