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
Milan VojnovicProfessor, Department of Statistics, LSEVerified email at lse.ac.uk
Sebastian NowozinDeepMindVerified email at deepmind.com
Dan AlistarhIST AustriaVerified email at ist.ac.at
Jerry LiMicrosoft ResearchVerified email at microsoft.com
Kohei HayashiResearcher, Preferred NetworksVerified email at preferred.jp
Motoaki KawanabeSenior Researcher, Advanced Telecommunications Research Institute InternationalVerified email at atr.jp
Behnam NeyshaburSenior Staff Research Scientist, GoogleVerified email at google.com
Nathan SrebroProfessor, TTIC and University of ChicagoVerified email at ttic.edu
Kenji YamanishiThe University of TokyoVerified email at mist.i.u-tokyo.ac.jp
Guido NolteDept. of Neurophysiology and Pathophysiology, UKE, HamburgVerified email at uke.de
Botond CsekeVolkswagen Data LabVerified email at volkswagen.de
Shinichi NakajimaMachine Learning Group, Technische Universität BerlinVerified email at tu-berlin.de
Stefan HaufeTechnische Universität Berlin, Physikalisch-Technische Bundesanstalt, Charité - UniversitätsmedizinVerified email at tu-berlin.de
Demjan GrubicGoogleVerified email at google.com
Diane BouchacourtFacebook AI ResearchVerified email at fb.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	2047	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	1410	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, 2017	1225	2017
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	476	2014
Norm-based capacity control in neural networks B Neyshabur, R Tomioka, N Srebro Conference on Learning Theory, 1376-1401, 2015	459	2015
Invariant common spatial patterns: Alleviating nonstationarities in brain-computer interfacing B Blankertz, M Kawanabe, R Tomioka, F Hohlefeld, K Müller, V Nikulin Advances in neural information processing systems 20, 2007	298	2007
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	252	2018
Estimation of low-rank tensors via convex optimization R Tomioka, K Hayashi, H Kashima arXiv preprint arXiv:1010.0789, 2010	215	2010
A regularized discriminative framework for EEG analysis with application to brain–computer interface R Tomioka, KR Müller NeuroImage 49 (1), 415-432, 2010	181	2010
Tensor factorization using auxiliary information A Narita, K Hayashi, R Tomioka, H Kashima Data Mining and Knowledge Discovery 25 (2), 298-324, 2012	176	2012
Statistical performance of convex tensor decomposition R Tomioka, T Suzuki, K Hayashi, H Kashima Advances in neural information processing systems 24, 2011	176	2011
Convex tensor decomposition via structured schatten norm regularization R Tomioka, T Suzuki Advances in neural information processing systems 26, 2013	152	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	131	2012
Logistic regression for single trial EEG classification R Tomioka, K Aihara, KR Müller Advances in neural information processing systems 19, 2006	123	2006
Geometry of optimization and implicit regularization in deep learning B Neyshabur, R Tomioka, R Salakhutdinov, N Srebro arXiv preprint arXiv:1705.03071, 2017	117	2017
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	115	2011
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	113	2010
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	101	2006
Super-Linear Convergence of Dual Augmented Lagrangian Algorithm for Sparsity Regularized Estimation. R Tomioka, T Suzuki, M Sugiyama Journal of Machine Learning Research 12 (5), 2011	100	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	100	2004

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