Ryota Tomioka

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Klaus-Robert MüllerTU Berlin & Korea University & Google Brain & MPIIVerified email at tu-berlin.de
Masashi SugiyamaDirector, RIKEN Center for Advanced Intelligence Project / Professor, The University of TokyoVerified email at k.u-tokyo.ac.jp
Taiji SuzukiThe University of TokyoVerified email at mist.i.u-tokyo.ac.jp
Hisashi KashimaProfessor, Kyoto UniversityVerified email at i.kyoto-u.ac.jp
Benjamin BlankertzTechnische Universität Berlin, Neurotechnology groupVerified email at tu-berlin.de
Sebastian NowozinPartner Research Manager, Machine Intelligence, Microsoft Research (Cambridge, UK)Verified email at microsoft.com
Milan VojnovicProfessor, Department of Statistics, LSEVerified email at lse.ac.uk
Kohei HayashiResearcher, Preferred NetworksVerified email at preferred.jp
Motoaki KawanabeSenior Researcher, Advanced Telecommunications Research Institute InternationalVerified email at atr.jp
Dan AlistarhIST AustriaVerified email at ist.ac.at
Jerry LiMicrosoft ResearchVerified email at mit.edu
Kenji YamanishiThe University of TokyoVerified email at mist.i.u-tokyo.ac.jp
Behnam NeyshaburStaff Research Scientist, GoogleVerified email at google.com
Nathan SrebroProfessor, TTIC and University of ChicagoVerified email at ttic.edu
Guido NolteDept. of Neurophysiology and Pathophysiology, UKE, HamburgVerified email at uke.de
Shinichi NakajimaMachine Learning Group, Technische Universität BerlinVerified email at tu-berlin.de
Botond CsekeVolkswagen Data LabVerified email at volkswagen.de
Stefan HaufeCharité - Universitätsmedizin BerlinVerified email at charite.de
Diane BouchacourtFacebook AI ResearchVerified email at fb.com
Demjan GrubicGoogleVerified email at google.com

Ryota Tomioka

Microsoft Research Cambridge

Verified email at microsoft.com - Homepage

Machine Learning Convex Optimization Neural Networks


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Optimizing spatial filters for robust EEG single-trial analysis B Blankertz, R Tomioka, S Lemm, M Kawanabe, KR Muller IEEE Signal processing magazine 25 (1), 41-56, 2007	1787	2007
f-gan: Training generative neural samplers using variational divergence minimization S Nowozin, B Cseke, R Tomioka Advances in Neural Information Processing Systems, 271-279, 2016	974	2016
QSGD: Communication-efficient SGD via gradient quantization and encoding D Alistarh, D Grubic, J Li, R Tomioka, M Vojnovic Advances in Neural Information Processing Systems 30, 1709-1720, 2017	632*	2017
Norm-based capacity control in neural networks B Neyshabur, R Tomioka, N Srebro Conference on Learning Theory, 1376-1401, 2015	317	2015
In search of the real inductive bias: On the role of implicit regularization in deep learning B Neyshabur, R Tomioka, N Srebro arXiv preprint arXiv:1412.6614, 2014	282	2014
Invariant Common Spatial Patterns: Alleviating Nonstationarities in Brain-Computer Interfacing. B Blankertz, M Kawanabe, R Tomioka, FU Hohlefeld, VV Nikulin, ... NIPS, 113-120, 2007	265	2007
Estimation of low-rank tensors via convex optimization R Tomioka, K Hayashi, H Kashima arXiv preprint arXiv:1010.0789, 2010	195	2010
A regularized discriminative framework for EEG analysis with application to brain–computer interface R Tomioka, KR Müller NeuroImage 49 (1), 415-432, 2010	174	2010
Statistical performance of convex tensor decomposition R Tomioka, T Suzuki, K Hayashi, H Kashima Advances in neural information processing systems 24, 972-980, 2011	160	2011
Multi-level variational autoencoder: Learning disentangled representations from grouped observations D Bouchacourt, R Tomioka, S Nowozin Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018	147	2018
Tensor factorization using auxiliary information A Narita, K Hayashi, R Tomioka, H Kashima Data Mining and Knowledge Discovery 25 (2), 298-324, 2012	140	2012
Convex tensor decomposition via structured schatten norm regularization R Tomioka, T Suzuki arXiv preprint arXiv:1303.6370, 2013	133	2013
Discovering emerging topics in social streams via link-anomaly detection T Takahashi, R Tomioka, K Yamanishi IEEE Transactions on Knowledge and Data Engineering 26 (1), 120-130, 2012	121	2012
Modeling sparse connectivity between underlying brain sources for EEG/MEG S Haufe, R Tomioka, G Nolte, KR Müller, M Kawanabe IEEE transactions on biomedical engineering 57 (8), 1954-1963, 2010	109	2010
Logistic regression for single trial EEG classification R Tomioka, K Aihara, KR Müller Advances in neural information processing systems 19, 1377, 2007	109	2007
Large-scale EEG/MEG source localization with spatial flexibility S Haufe, R Tomioka, T Dickhaus, C Sannelli, B Blankertz, G Nolte, ... NeuroImage 54 (2), 851-859, 2011	101	2011
Multivariate analysis of noise in genetic regulatory networks R Tomioka, H Kimura, TJ Kobayashi, K Aihara Journal of theoretical biology 229 (4), 501-521, 2004	97	2004
Spectrally weighted common spatial pattern algorithm for single trial EEG classification R Tomioka, G Dornhege, G Nolte, B Blankertz, K Aihara, KR Müller Dept. Math. Eng., Univ. Tokyo, Tokyo, Japan, Tech. Rep 40, 2006	95	2006
Geometry of optimization and implicit regularization in deep learning B Neyshabur, R Tomioka, R Salakhutdinov, N Srebro arXiv preprint arXiv:1705.03071, 2017	92	2017
The algebraic combinatorial approach for low-rank matrix completion. FJ Király, L Theran, R Tomioka J. Mach. Learn. Res. 16 (1), 1391-1436, 2015	92	2015

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