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Ryota Tomioka
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2020 – today
- 2024
- [c40]Marco Federici, Patrick Forré, Ryota Tomioka, Bastiaan S. Veeling:
Latent Representation and Simulation of Markov Processes via Time-Lagged Information Bottleneck. ICLR 2024 - 2023
- [c39]Leon Klein, Andrew Y. K. Foong, Tor Erlend Fjelde, Bruno Mlodozeniec, Marc Brockschmidt, Sebastian Nowozin, Frank Noé, Ryota Tomioka:
Timewarp: Transferable Acceleration of Molecular Dynamics by Learning Time-Coarsened Dynamics. NeurIPS 2023 - [i26]Leon Klein, Andrew Y. K. Foong, Tor Erlend Fjelde, Bruno Mlodozeniec, Marc Brockschmidt, Sebastian Nowozin, Frank Noé, Ryota Tomioka:
Timewarp: Transferable Acceleration of Molecular Dynamics by Learning Time-Coarsened Dynamics. CoRR abs/2302.01170 (2023) - [i25]Marco Federici, Patrick Forré, Ryota Tomioka, Bastiaan S. Veeling:
Latent Representation and Simulation of Markov Processes via Time-Lagged Information Bottleneck. CoRR abs/2309.07200 (2023) - [i24]Claudio Zeni, Robert Pinsler, Daniel Zügner, Andrew Fowler, Matthew Horton, Xiang Fu, Sasha Shysheya, Jonathan Crabbé, Lixin Sun, Jake Smith, Ryota Tomioka, Tian Xie:
MatterGen: a generative model for inorganic materials design. CoRR abs/2312.03687 (2023) - 2021
- [c38]Hisham Husain, Kamil Ciosek, Ryota Tomioka:
Regularized Policies are Reward Robust. AISTATS 2021: 64-72 - [c37]Keshav Santhanam, Siddharth Krishna, Ryota Tomioka, Andrew W. Fitzgibbon, Tim Harris:
DistIR: An Intermediate Representation for Optimizing Distributed Neural Networks. EuroMLSys@EuroSys 2021: 15-23 - [c36]Marco Federici, Ryota Tomioka, Patrick Forré:
An Information-theoretic Approach to Distribution Shifts. NeurIPS 2021: 17628-17641 - [i23]Hisham Husain, Kamil Ciosek, Ryota Tomioka:
Regularized Policies are Reward Robust. CoRR abs/2101.07012 (2021) - [i22]Marco Federici, Ryota Tomioka, Patrick Forré:
An Information-theoretic Approach to Distribution Shifts. CoRR abs/2106.03783 (2021) - [i21]Keshav Santhanam, Siddharth Krishna, Ryota Tomioka, Tim Harris, Matei Zaharia:
DistIR: An Intermediate Representation and Simulator for Efficient Neural Network Distribution. CoRR abs/2111.05426 (2021) - 2020
- [c35]Kamil Ciosek, Vincent Fortuin, Ryota Tomioka, Katja Hofmann, Richard E. Turner:
Conservative Uncertainty Estimation By Fitting Prior Networks. ICLR 2020 - [c34]Chen Liu, Mathieu Salzmann, Tao Lin, Ryota Tomioka, Sabine Süsstrunk:
On the Loss Landscape of Adversarial Training: Identifying Challenges and How to Overcome Them. NeurIPS 2020 - [i20]Chen Liu, Mathieu Salzmann, Tao Lin, Ryota Tomioka, Sabine Süsstrunk:
On the Loss Landscape of Adversarial Training: Identifying Challenges and How to Overcome Them. CoRR abs/2006.08403 (2020)
2010 – 2019
- 2019
- [c33]Chen Liu, Ryota Tomioka, Volkan Cevher:
On Certifying Non-Uniform Bounds against Adversarial Attacks. ICML 2019: 4072-4081 - [c32]Emile Mathieu, Charline Le Lan, Chris J. Maddison, Ryota Tomioka, Yee Whye Teh:
Continuous Hierarchical Representations with Poincaré Variational Auto-Encoders. NeurIPS 2019: 12544-12555 - [i19]Emile Mathieu, Charline Le Lan, Chris J. Maddison, Ryota Tomioka, Yee Whye Teh:
Hierarchical Representations with Poincaré Variational Auto-Encoders. CoRR abs/1901.06033 (2019) - [i18]Chen Liu, Ryota Tomioka, Volkan Cevher:
On Certifying Non-uniform Bound against Adversarial Attacks. CoRR abs/1903.06603 (2019) - 2018
- [c31]Diane Bouchacourt, Ryota Tomioka, Sebastian Nowozin:
Multi-Level Variational Autoencoder: Learning Disentangled Representations From Grouped Observations. AAAI 2018: 2095-2102 - [i17]Justas Dauparas, Ryota Tomioka, Katja Hofmann:
Depth and nonlinearity induce implicit exploration for RL. CoRR abs/1805.11711 (2018) - 2017
- [c30]Kirthevasan Kandasamy, Yoram Bachrach, Ryota Tomioka, Daniel Tarlow, David Carter:
Batch Policy Gradient Methods for Improving Neural Conversation Models. ICLR (Poster) 2017 - [c29]Dan Alistarh, Demjan Grubic, Jerry Li, Ryota Tomioka, Milan Vojnovic:
QSGD: Communication-Efficient SGD via Gradient Quantization and Encoding. NIPS 2017: 1709-1720 - [i16]Kirthevasan Kandasamy, Yoram Bachrach, Ryota Tomioka, Daniel Tarlow, David Carter:
Batch Policy Gradient Methods for Improving Neural Conversation Models. CoRR abs/1702.03334 (2017) - [i15]Behnam Neyshabur, Ryota Tomioka, Ruslan Salakhutdinov, Nathan Srebro:
Geometry of Optimization and Implicit Regularization in Deep Learning. CoRR abs/1705.03071 (2017) - [i14]Diane Bouchacourt, Ryota Tomioka, Sebastian Nowozin:
Multi-Level Variational Autoencoder: Learning Disentangled Representations from Grouped Observations. CoRR abs/1705.08841 (2017) - [i13]Alex Gaunt, Matthew Johnson, Maik Riechert, Daniel Tarlow, Ryota Tomioka, Dimitrios Vytiniotis, Sam Webster:
AMPNet: Asynchronous Model-Parallel Training for Dynamic Neural Networks. CoRR abs/1705.09786 (2017) - 2016
- [j15]Kishan Wimalawarne, Ryota Tomioka, Masashi Sugiyama:
Theoretical and Experimental Analyses of Tensor-Based Regression and Classification. Neural Comput. 28(4): 686-715 (2016) - [c28]Sebastian Nowozin, Botond Cseke, Ryota Tomioka:
f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization. NIPS 2016: 271-279 - [c27]Behnam Neyshabur, Ryota Tomioka, Ruslan Salakhutdinov, Nathan Srebro:
Data-Dependent Path Normalization in Neural Networks. ICLR (Poster) 2016 - [i12]Sebastian Nowozin, Botond Cseke, Ryota Tomioka:
f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization. CoRR abs/1606.00709 (2016) - [i11]Dan Alistarh, Jerry Li, Ryota Tomioka, Milan Vojnovic:
QSGD: Randomized Quantization for Communication-Optimal Stochastic Gradient Descent. CoRR abs/1610.02132 (2016) - [i10]Liwen Zhang, John M. Winn, Ryota Tomioka:
Gaussian Attention Model and Its Application to Knowledge Base Embedding and Question Answering. CoRR abs/1611.02266 (2016) - 2015
- [j14]Franz J. Király, Louis Theran, Ryota Tomioka:
The algebraic combinatorial approach for low-rank matrix completion. J. Mach. Learn. Res. 16: 1391-1436 (2015) - [j13]Shinichi Nakajima, Ryota Tomioka, Masashi Sugiyama, S. Derin Babacan:
Condition for perfect dimensionality recovery by variational Bayesian PCA. J. Mach. Learn. Res. 16: 3757-3811 (2015) - [j12]Shota Saito, Ryota Tomioka, Kenji Yamanishi:
Early detection of persistent topics in social networks. Soc. Netw. Anal. Min. 5(1): 19:1-19:15 (2015) - [c26]Behnam Neyshabur, Ryota Tomioka, Nathan Srebro:
Norm-Based Capacity Control in Neural Networks. COLT 2015: 1376-1401 - [c25]Qinqing Zheng, Ryota Tomioka:
Interpolating Convex and Non-Convex Tensor Decompositions via the Subspace Norm. NIPS 2015: 3106-3113 - [c24]Behnam Neyshabur, Ryota Tomioka, Nathan Srebro:
In Search of the Real Inductive Bias: On the Role of Implicit Regularization in Deep Learning. ICLR (Workshop) 2015 - [i9]Behnam Neyshabur, Ryota Tomioka, Nathan Srebro:
Norm-Based Capacity Control in Neural Networks. CoRR abs/1503.00036 (2015) - [i8]Liwen Zhang, Subhransu Maji, Ryota Tomioka:
Jointly Learning Multiple Perceptual Similarities. CoRR abs/1503.01521 (2015) - [i7]Qinqing Zheng, Ryota Tomioka:
Interpolating Convex and Non-Convex Tensor Decompositions via the Subspace Norm. CoRR abs/1503.05479 (2015) - [i6]Kishan Wimalawarne, Ryota Tomioka, Masashi Sugiyama:
Theoretical and Experimental Analyses of Tensor-Based Regression and Classification. CoRR abs/1509.01770 (2015) - 2014
- [j11]Toshimitsu Takahashi, Ryota Tomioka, Kenji Yamanishi:
Discovering Emerging Topics in Social Streams via Link-Anomaly Detection. IEEE Trans. Knowl. Data Eng. 26(1): 120-130 (2014) - [c23]Shota Saito, Ryota Tomioka, Kenji Yamanishi:
Early detection of persistent topics in social networks. ASONAM 2014: 417-424 - [c22]Kishan Wimalawarne, Masashi Sugiyama, Ryota Tomioka:
Multitask learning meets tensor factorization: task imputation via convex optimization. NIPS 2014: 2825-2833 - 2013
- [j10]Shinichi Nakajima, Masashi Sugiyama, S. Derin Babacan, Ryota Tomioka:
Global analytic solution of fully-observed variational Bayesian matrix factorization. J. Mach. Learn. Res. 14(1): 1-37 (2013) - [c21]Zenghan Liang, Ryota Tomioka, Hiroshi Murata, Ryo Asaoka, Kenji Yamanishi:
Quantitative Prediction of Glaucomatous Visual Field Loss from Few Measurements. ICDM 2013: 1121-1126 - [c20]Koh Takeuchi, Ryota Tomioka, Katsuhiko Ishiguro, Akisato Kimura, Hiroshi Sawada:
Non-negative Multiple Tensor Factorization. ICDM 2013: 1199-1204 - [c19]Kazuyoshi Yoshii, Ryota Tomioka, Daichi Mochihashi, Masataka Goto:
Infinite Positive Semidefinite Tensor Factorization for Source Separation of Mixture Signals. ICML (3) 2013: 576-584 - [c18]Kazuyoshi Yoshii, Ryota Tomioka, Daichi Mochihashi, Masataka Goto:
Beyond NMF: Time-Domain Audio Source Separation without Phase Reconstruction. ISMIR 2013: 369-374 - [c17]Ryota Tomioka, Taiji Suzuki:
Convex Tensor Decomposition via Structured Schatten Norm Regularization. NIPS 2013: 1331-1339 - [i5]Ryota Tomioka, Taiji Suzuki:
Convex Tensor Decomposition via Structured Schatten Norm Regularization. CoRR abs/1303.6370 (2013) - 2012
- [j9]Atsuhiro Narita, Kohei Hayashi, Ryota Tomioka, Hisashi Kashima:
Tensor factorization using auxiliary information. Data Min. Knowl. Discov. 25(2): 298-324 (2012) - [c16]Franz J. Király, Ryota Tomioka:
A Combinatorial Algebraic Approach for the Identifiability of Low-Rank Matrix Completion. ICML 2012 - [c15]Shinichi Nakajima, Ryota Tomioka, Masashi Sugiyama, S. Derin Babacan:
Perfect Dimensionality Recovery by Variational Bayesian PCA. NIPS 2012: 980-988 - [c14]Kohei Hayashi, Takashi Takenouchi, Ryota Tomioka, Hisashi Kashima:
Self-measuring Similarity for Multi-task Gaussian Process. ICML Unsupervised and Transfer Learning 2012: 145-154 - [c13]Ryota Tomioka, Morten Mørup:
A Bayesian Analysis of the Radioactive Releases of Fukushima. AISTATS 2012: 1243-1251 - [i4]Franz J. Király, Louis Theran, Ryota Tomioka, Takeaki Uno:
The Algebraic Combinatorial Approach for Low-Rank Matrix Completion. CoRR abs/1211.4116 (2012) - 2011
- [j8]Ryota Tomioka, Taiji Suzuki, Masashi Sugiyama:
Super-Linear Convergence of Dual Augmented Lagrangian Algorithm for Sparsity Regularized Estimation. J. Mach. Learn. Res. 12: 1537-1586 (2011) - [j7]Taiji Suzuki, Ryota Tomioka:
SpicyMKL: a fast algorithm for Multiple Kernel Learning with thousands of kernels. Mach. Learn. 85(1-2): 77-108 (2011) - [j6]Stefan Haufe, Ryota Tomioka, Thorsten Dickhaus, Claudia Sannelli, Benjamin Blankertz, Guido Nolte, Klaus-Robert Müller:
Large-scale EEG/MEG source localization with spatial flexibility. NeuroImage 54(2): 851-859 (2011) - [c12]Xu Sun, Hisashi Kashima, Ryota Tomioka, Naonori Ueda, Ping Li:
A New Multi-task Learning Method for Personalized Activity Recognition. ICDM 2011: 1218-1223 - [c11]Toshimitsu Takahashi, Ryota Tomioka, Kenji Yamanishi:
Discovering Emerging Topics in Social Streams via Link Anomaly Detection. ICDM 2011: 1230-1235 - [c10]Ryota Tomioka, Taiji Suzuki, Kohei Hayashi, Hisashi Kashima:
Statistical Performance of Convex Tensor Decomposition. NIPS 2011: 972-980 - [c9]Yasuhiro Urabe, Kenji Yamanishi, Ryota Tomioka, Hiroki Iwai:
Real-Time Change-Point Detection Using Sequentially Discounting Normalized Maximum Likelihood Coding. PAKDD (2) 2011: 185-197 - [c8]Xu Sun, Hisashi Kashima, Ryota Tomioka, Naonori Ueda:
Large Scale Real-Life Action Recognition Using Conditional Random Fields with Stochastic Training. PAKDD (2) 2011: 222-233 - [c7]Atsuhiro Narita, Kohei Hayashi, Ryota Tomioka, Hisashi Kashima:
Tensor Factorization Using Auxiliary Information. ECML/PKDD (2) 2011: 501-516 - [i3]Toshimitsu Takahashi, Ryota Tomioka, Kenji Yamanishi:
Discovering Emerging Topics in Social Streams via Link Anomaly Detection. CoRR abs/1110.2899 (2011) - 2010
- [j5]Neil Rubens, Ryota Tomioka, Masashi Sugiyama:
Output Divergence Criterion for Active Learning in Collaborative Settings. Inf. Media Technol. 5(1): 119-128 (2010) - [j4]Ryota Tomioka, Klaus-Robert Müller:
A regularized discriminative framework for EEG analysis with application to brain-computer interface. NeuroImage 49(1): 415-432 (2010) - [j3]Stefan Haufe, Ryota Tomioka, Guido Nolte, Klaus-Robert Müller, Motoaki Kawanabe:
Modeling Sparse Connectivity Between Underlying Brain Sources for EEG/MEG. IEEE Trans. Biomed. Eng. 57(8): 1954-1963 (2010) - [c6]Ryota Tomioka, Taiji Suzuki, Masashi Sugiyama, Hisashi Kashima:
A Fast Augmented Lagrangian Algorithm for Learning Low-Rank Matrices. ICML 2010: 1087-1094 - [c5]Shinichi Nakajima, Masashi Sugiyama, Ryota Tomioka:
Global Analytic Solution for Variational Bayesian Matrix Factorization. NIPS 2010: 1768-1776 - [i2]Ryota Tomioka, Taiji Suzuki:
Regularization Strategies and Empirical Bayesian Learning for MKL. CoRR abs/1011.3090 (2010)
2000 – 2009
- 2009
- [j2]Ryota Tomioka, Masashi Sugiyama:
Dual-Augmented Lagrangian Method for Efficient Sparse Reconstruction. IEEE Signal Process. Lett. 16(12): 1067-1070 (2009) - [i1]Ryota Tomioka, Taiji Suzuki, Masashi Sugiyama:
Super-Linear Convergence of Dual Augmented-Lagrangian Algorithm for Sparsity Regularized Estimation. CoRR abs/0911.4046 (2009) - 2008
- [j1]Benjamin Blankertz, Ryota Tomioka, Steven Lemm, Motoaki Kawanabe, Klaus-Robert Müller:
Optimizing Spatial filters for Robust EEG Single-Trial Analysis. IEEE Signal Process. Mag. 25(1): 41-56 (2008) - 2007
- [c4]Ryota Tomioka, Kazuyuki Aihara:
Classifying matrices with a spectral regularization. ICML 2007: 895-902 - [c3]Benjamin Blankertz, Motoaki Kawanabe, Ryota Tomioka, Friederike U. Hohlefeld, Vadim V. Nikulin, Klaus-Robert Müller:
Invariant Common Spatial Patterns: Alleviating Nonstationarities in Brain-Computer Interfacing. NIPS 2007: 113-120 - 2006
- [c2]Ryota Tomioka, Guido Dornhege, Guido Nolte, Kazuyuki Aihara, Klaus-Robert Müller:
Optimizing Spectral Filters for Single Trial EEG Classification. DAGM-Symposium 2006: 414-423 - [c1]Ryota Tomioka, Kazuyuki Aihara, Klaus-Robert Müller:
Logistic Regression for Single Trial EEG Classification. NIPS 2006: 1377-1384
Coauthor Index
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last updated on 2024-09-05 23:40 CEST by the dblp team
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