Ryota Tomioka - RankMe

82 papers A* 17A 2B 1Journal 42Unranked 20

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	A generative model for inorganic materials design. Nat. Claudio Zeni, Robert Pinsler, Daniel Zügner, Andrew Fowler, Matthew Horton, Xiang Fu, Zilong Wang, Aliaksandra Shysheya, Jonathan Crabbé, Shoko Ueda, Roberto Sordillo, Lixin Sun, Jake Smith, Bichlien Nguyen, Hannes Schulz, Sarah Lewis, Chin-Wei Huang, Ziheng Lu, Yichi Zhou, Han Yang, Hongxia Hao, Jielan Li, Chunlei Yang, Wenjie Li, Ryota Tomioka, Tian Xie
2024	A*	`conf`	Latent Representation and Simulation of Markov Processes via Time-Lagged Information Bottleneck. ICLR Marco Federici, Patrick Forré, Ryota Tomioka, Bastiaan S. Veeling
2023	J	`jnl`	Latent Representation and Simulation of Markov Processes via Time-Lagged Information Bottleneck. CoRR Marco Federici, Patrick Forré, Ryota Tomioka, Bastiaan S. Veeling
2023	J	`jnl`	MatterGen: a generative model for inorganic materials design. CoRR Claudio Zeni, Robert Pinsler, Daniel Zügner, Andrew Fowler, Matthew Horton, Xiang Fu, Sasha Shysheya, Jonathan Crabbé, Lixin Sun, Jake Smith, Ryota Tomioka, Tian Xie
2023	A*	`conf`	Timewarp: Transferable Acceleration of Molecular Dynamics by Learning Time-Coarsened Dynamics. NeurIPS Leon Klein, Andrew Y. K. Foong, Tor Erlend Fjelde, Bruno Mlodozeniec, Marc Brockschmidt, Sebastian Nowozin, Frank Noé, Ryota Tomioka
2023	J	`jnl`	Timewarp: Transferable Acceleration of Molecular Dynamics by Learning Time-Coarsened Dynamics. CoRR Leon Klein, Andrew Y. K. Foong, Tor Erlend Fjelde, Bruno Mlodozeniec, Marc Brockschmidt, Sebastian Nowozin, Frank Noé, Ryota Tomioka
2021	A*	`conf`	An Information-theoretic Approach to Distribution Shifts. NeurIPS Marco Federici, Ryota Tomioka, Patrick Forré
2021	J	`jnl`	An Information-theoretic Approach to Distribution Shifts. CoRR Marco Federici, Ryota Tomioka, Patrick Forré
2021	J	`jnl`	DistIR: An Intermediate Representation and Simulator for Efficient Neural Network Distribution. CoRR Keshav Santhanam, Siddharth Krishna, Ryota Tomioka, Tim Harris, Matei Zaharia
2021	—	`conf`	DistIR: An Intermediate Representation for Optimizing Distributed Neural Networks. EuroMLSys@EuroSys Keshav Santhanam, Siddharth Krishna, Ryota Tomioka, Andrew W. Fitzgibbon, Tim Harris
2021	A	`conf`	Regularized Policies are Reward Robust. AISTATS Hisham Husain, Kamil Ciosek, Ryota Tomioka
2021	J	`jnl`	Regularized Policies are Reward Robust. CoRR Hisham Husain, Kamil Ciosek, Ryota Tomioka
2020	A*	`conf`	Conservative Uncertainty Estimation By Fitting Prior Networks. ICLR Kamil Ciosek, Vincent Fortuin, Ryota Tomioka, Katja Hofmann, Richard E. Turner
2020	A*	`conf`	On the Loss Landscape of Adversarial Training: Identifying Challenges and How to Overcome Them. NeurIPS Chen Liu, Mathieu Salzmann, Tao Lin, Ryota Tomioka, Sabine Süsstrunk
2020	J	`jnl`	On the Loss Landscape of Adversarial Training: Identifying Challenges and How to Overcome Them. CoRR Chen Liu, Mathieu Salzmann, Tao Lin, Ryota Tomioka, Sabine Süsstrunk
2019	A*	`conf`	Continuous Hierarchical Representations with Poincaré Variational Auto-Encoders. NeurIPS Emile Mathieu, Charline Le Lan, Chris J. Maddison, Ryota Tomioka, Yee Whye Teh
2019	J	`jnl`	Hierarchical Representations with Poincaré Variational Auto-Encoders. CoRR Emile Mathieu, Charline Le Lan, Chris J. Maddison, Ryota Tomioka, Yee Whye Teh
2019	A*	`conf`	On Certifying Non-Uniform Bounds against Adversarial Attacks. ICML Chen Liu, Ryota Tomioka, Volkan Cevher
2019	J	`jnl`	On Certifying Non-uniform Bound against Adversarial Attacks. CoRR Chen Liu, Ryota Tomioka, Volkan Cevher
2018	J	`jnl`	Depth and nonlinearity induce implicit exploration for RL. CoRR Justas Dauparas, Ryota Tomioka, Katja Hofmann
2018	A*	`conf`	Multi-Level Variational Autoencoder: Learning Disentangled Representations From Grouped Observations. AAAI Diane Bouchacourt, Ryota Tomioka, Sebastian Nowozin
2017	J	`jnl`	AMPNet: Asynchronous Model-Parallel Training for Dynamic Neural Networks. CoRR Alex Gaunt, Matthew Johnson, Maik Riechert, Daniel Tarlow, Ryota Tomioka, Dimitrios Vytiniotis, Sam Webster
2017	—	`conf`	Batch Policy Gradient Methods for Improving Neural Conversation Models. ICLR (Poster) Kirthevasan Kandasamy, Yoram Bachrach, Ryota Tomioka, Daniel Tarlow, David Carter
2017	J	`jnl`	Batch Policy Gradient Methods for Improving Neural Conversation Models. CoRR Kirthevasan Kandasamy, Yoram Bachrach, Ryota Tomioka, Daniel Tarlow, David Carter
2017	J	`jnl`	Geometry of Optimization and Implicit Regularization in Deep Learning. CoRR Behnam Neyshabur, Ryota Tomioka, Ruslan Salakhutdinov, Nathan Srebro
2017	J	`jnl`	Multi-Level Variational Autoencoder: Learning Disentangled Representations from Grouped Observations. CoRR Diane Bouchacourt, Ryota Tomioka, Sebastian Nowozin
2017	—	`conf`	QSGD: Communication-Efficient SGD via Gradient Quantization and Encoding. NIPS Dan Alistarh, Demjan Grubic, Jerry Li, Ryota Tomioka, Milan Vojnovic
2016	A*	`conf`	Data-Dependent Path Normalization in Neural Networks. ICLR (Poster) Behnam Neyshabur, Ryota Tomioka, Ruslan Salakhutdinov, Nathan Srebro
2016	J	`jnl`	Gaussian Attention Model and Its Application to Knowledge Base Embedding and Question Answering. CoRR Liwen Zhang, John M. Winn, Ryota Tomioka
2016	J	`jnl`	QSGD: Randomized Quantization for Communication-Optimal Stochastic Gradient Descent. CoRR Dan Alistarh, Jerry Li, Ryota Tomioka, Milan Vojnovic
2016	J	`jnl`	Theoretical and Experimental Analyses of Tensor-Based Regression and Classification. Neural Comput. Kishan Wimalawarne, Ryota Tomioka, Masashi Sugiyama
2016	—	`conf`	f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization. NIPS Sebastian Nowozin, Botond Cseke, Ryota Tomioka
2016	J	`jnl`	f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization. CoRR Sebastian Nowozin, Botond Cseke, Ryota Tomioka
2015	J	`jnl`	Condition for perfect dimensionality recovery by variational Bayesian PCA. J. Mach. Learn. Res. Shinichi Nakajima, Ryota Tomioka, Masashi Sugiyama, S. Derin Babacan
2015	J	`jnl`	Early detection of persistent topics in social networks. Soc. Netw. Anal. Min. Shota Saito, Ryota Tomioka, Kenji Yamanishi
2015	—	`conf`	In Search of the Real Inductive Bias: On the Role of Implicit Regularization in Deep Learning. ICLR (Workshop) Behnam Neyshabur, Ryota Tomioka, Nathan Srebro
2015	—	`conf`	Interpolating Convex and Non-Convex Tensor Decompositions via the Subspace Norm. NIPS Qinqing Zheng, Ryota Tomioka
2015	J	`jnl`	Interpolating Convex and Non-Convex Tensor Decompositions via the Subspace Norm. CoRR Qinqing Zheng, Ryota Tomioka
2015	J	`jnl`	Jointly Learning Multiple Perceptual Similarities. CoRR Liwen Zhang, Subhransu Maji, Ryota Tomioka
2015	A*	`conf`	Norm-Based Capacity Control in Neural Networks. COLT Behnam Neyshabur, Ryota Tomioka, Nathan Srebro
2015	J	`jnl`	Norm-Based Capacity Control in Neural Networks. CoRR Behnam Neyshabur, Ryota Tomioka, Nathan Srebro
2015	J	`jnl`	The algebraic combinatorial approach for low-rank matrix completion. J. Mach. Learn. Res. Franz J. Király, Louis Theran, Ryota Tomioka
2015	J	`jnl`	Theoretical and Experimental Analyses of Tensor-Based Regression and Classification. CoRR Kishan Wimalawarne, Ryota Tomioka, Masashi Sugiyama
2014	J	`jnl`	Discovering Emerging Topics in Social Streams via Link-Anomaly Detection. IEEE Trans. Knowl. Data Eng. Toshimitsu Takahashi, Ryota Tomioka, Kenji Yamanishi
2014	B	`conf`	Early detection of persistent topics in social networks. ASONAM Shota Saito, Ryota Tomioka, Kenji Yamanishi
2014	—	`conf`	Multitask learning meets tensor factorization: task imputation via convex optimization. NIPS Kishan Wimalawarne, Masashi Sugiyama, Ryota Tomioka
2013	—	`conf`	Beyond NMF: Time-Domain Audio Source Separation without Phase Reconstruction. ISMIR Kazuyoshi Yoshii, Ryota Tomioka, Daichi Mochihashi, Masataka Goto
2013	J	`jnl`	Convex Tensor Decomposition via Structured Schatten Norm Regularization CoRR Ryota Tomioka, Taiji Suzuki
2013	—	`conf`	Convex Tensor Decomposition via Structured Schatten Norm Regularization. NIPS Ryota Tomioka, Taiji Suzuki
2013	J	`jnl`	Global analytic solution of fully-observed variational Bayesian matrix factorization. J. Mach. Learn. Res. Shinichi Nakajima, Masashi Sugiyama, S. Derin Babacan, Ryota Tomioka
2013	—	`conf`	Infinite Positive Semidefinite Tensor Factorization for Source Separation of Mixture Signals. ICML (3) Kazuyoshi Yoshii, Ryota Tomioka, Daichi Mochihashi, Masataka Goto
2013	A*	`conf`	Non-negative Multiple Tensor Factorization. ICDM Koh Takeuchi, Ryota Tomioka, Katsuhiko Ishiguro, Akisato Kimura, Hiroshi Sawada
2013	A*	`conf`	Quantitative Prediction of Glaucomatous Visual Field Loss from Few Measurements. ICDM Zenghan Liang, Ryota Tomioka, Hiroshi Murata, Ryo Asaoka, Kenji Yamanishi
2012	A	`conf`	A Bayesian Analysis of the Radioactive Releases of Fukushima. AISTATS Ryota Tomioka, Morten Mørup
2012	A*	`conf`	A Combinatorial Algebraic Approach for the Identifiability of Low-Rank Matrix Completion. ICML Franz J. Király, Ryota Tomioka
2012	—	`conf`	Perfect Dimensionality Recovery by Variational Bayesian PCA. NIPS Shinichi Nakajima, Ryota Tomioka, Masashi Sugiyama, S. Derin Babacan
2012	—	`conf`	Self-measuring Similarity for Multi-task Gaussian Process. ICML Unsupervised and Transfer Learning Kohei Hayashi, Takashi Takenouchi, Ryota Tomioka, Hisashi Kashima
2012	J	`jnl`	Tensor factorization using auxiliary information. Data Min. Knowl. Discov. Atsuhiro Narita, Kohei Hayashi, Ryota Tomioka, Hisashi Kashima
2012	J	`jnl`	The Algebraic Combinatorial Approach for Low-Rank Matrix Completion CoRR Franz J. Király, Louis Theran, Ryota Tomioka, Takeaki Uno
2011	A*	`conf`	A New Multi-task Learning Method for Personalized Activity Recognition. ICDM Xu Sun, Hisashi Kashima, Ryota Tomioka, Naonori Ueda, Ping Li
2011	J	`jnl`	Discovering Emerging Topics in Social Streams via Link Anomaly Detection CoRR Toshimitsu Takahashi, Ryota Tomioka, Kenji Yamanishi
2011	A*	`conf`	Discovering Emerging Topics in Social Streams via Link Anomaly Detection. ICDM Toshimitsu Takahashi, Ryota Tomioka, Kenji Yamanishi
2011	—	`conf`	Large Scale Real-Life Action Recognition Using Conditional Random Fields with Stochastic Training. PAKDD (2) Xu Sun, Hisashi Kashima, Ryota Tomioka, Naonori Ueda
2011	J	`jnl`	Large-scale EEG/MEG source localization with spatial flexibility. NeuroImage Stefan Haufe, Ryota Tomioka, Thorsten Dickhaus, Claudia Sannelli, Benjamin Blankertz, Guido Nolte, Klaus-Robert Müller
2011	—	`conf`	Real-Time Change-Point Detection Using Sequentially Discounting Normalized Maximum Likelihood Coding. PAKDD (2) Yasuhiro Urabe, Kenji Yamanishi, Ryota Tomioka, Hiroki Iwai
2011	J	`jnl`	SpicyMKL: a fast algorithm for Multiple Kernel Learning with thousands of kernels. Mach. Learn. Taiji Suzuki, Ryota Tomioka
2011	—	`conf`	Statistical Performance of Convex Tensor Decomposition. NIPS Ryota Tomioka, Taiji Suzuki, Kohei Hayashi, Hisashi Kashima
2011	J	`jnl`	Super-Linear Convergence of Dual Augmented Lagrangian Algorithm for Sparsity Regularized Estimation. J. Mach. Learn. Res. Ryota Tomioka, Taiji Suzuki, Masashi Sugiyama
2011	—	`conf`	Tensor Factorization Using Auxiliary Information. ECML/PKDD (2) Atsuhiro Narita, Kohei Hayashi, Ryota Tomioka, Hisashi Kashima
2010	A*	`conf`	A Fast Augmented Lagrangian Algorithm for Learning Low-Rank Matrices. ICML Ryota Tomioka, Taiji Suzuki, Masashi Sugiyama, Hisashi Kashima
2010	J	`jnl`	A regularized discriminative framework for EEG analysis with application to brain-computer interface. NeuroImage Ryota Tomioka, Klaus-Robert Müller
2010	—	`conf`	Global Analytic Solution for Variational Bayesian Matrix Factorization. NIPS Shinichi Nakajima, Masashi Sugiyama, Ryota Tomioka
2010	J	`jnl`	Modeling Sparse Connectivity Between Underlying Brain Sources for EEG/MEG. IEEE Trans. Biomed. Eng. Stefan Haufe, Ryota Tomioka, Guido Nolte, Klaus-Robert Müller, Motoaki Kawanabe
2010	J	`jnl`	Output Divergence Criterion for Active Learning in Collaborative Settings. Inf. Media Technol. Neil Rubens, Ryota Tomioka, Masashi Sugiyama
2010	J	`jnl`	Regularization Strategies and Empirical Bayesian Learning for MKL. CoRR Ryota Tomioka, Taiji Suzuki
2009	J	`jnl`	Dual-Augmented Lagrangian Method for Efficient Sparse Reconstruction. IEEE Signal Process. Lett. Ryota Tomioka, Masashi Sugiyama
2009	J	`jnl`	Super-Linear Convergence of Dual Augmented-Lagrangian Algorithm for Sparsity Regularized Estimation. CoRR Ryota Tomioka, Taiji Suzuki, Masashi Sugiyama
2008	J	`jnl`	Optimizing Spatial filters for Robust EEG Single-Trial Analysis. IEEE Signal Process. Mag. Benjamin Blankertz, Ryota Tomioka, Steven Lemm, Motoaki Kawanabe, Klaus-Robert Müller
2007	A*	`conf`	Classifying matrices with a spectral regularization. ICML Ryota Tomioka, Kazuyuki Aihara
2007	—	`conf`	Invariant Common Spatial Patterns: Alleviating Nonstationarities in Brain-Computer Interfacing. NIPS Benjamin Blankertz, Motoaki Kawanabe, Ryota Tomioka, Friederike U. Hohlefeld, Vadim V. Nikulin, Klaus-Robert Müller
2006	—	`conf`	Logistic Regression for Single Trial EEG Classification. NIPS Ryota Tomioka, Kazuyuki Aihara, Klaus-Robert Müller
2006	—	`conf`	Optimizing Spectral Filters for Single Trial EEG Classification. DAGM-Symposium Ryota Tomioka, Guido Dornhege, Guido Nolte, Kazuyuki Aihara, Klaus-Robert Müller