Aapo Hyvärinen

ORCID

Publication Activity (10 Years)

Years Active: 1996-2023
Publications (10 Years): 78

Top Venues

Publications

Omar Chehab, Alexandre Gramfort, Aapo Hyvärinen
Optimizing the Noise in Self-Supervised Learning: from Importance Sampling to Noise-Contrastive Estimation. CoRR (2023)
Aapo Hyvärinen, Ilyes Khemakhem, Hiroshi Morioka
Nonlinear independent component analysis for principled disentanglement in unsupervised deep learning. Patterns 4 (10) (2023)
Omar Chehab, Aapo Hyvärinen, Andrej Risteski
Provable benefits of annealing for estimating normalizing constants: Importance Sampling, Noise-Contrastive Estimation, and beyond. CoRR (2023)
Hiroshi Morioka, Aapo Hyvärinen
Causal Representation Learning Made Identifiable by Grouping of Observational Variables. CoRR (2023)
Yongjie Zhu, Tiina Parviainen, Erkka Heinilä, Lauri Parkkonen, Aapo Hyvärinen
Unsupervised representation learning of spontaneous MEG data with nonlinear ICA. NeuroImage 274 (2023)
Hermanni Hälvä, Jonathan So, Richard E. Turner, Aapo Hyvärinen
Identifiable Feature Learning for Spatial Data with Nonlinear ICA. CoRR (2023)
Aapo Hyvärinen, Ilyes Khemakhem, Hiroshi Morioka
Nonlinear Independent Component Analysis for Principled Disentanglement in Unsupervised Deep Learning. CoRR (2023)
Aapo Hyvärinen, Ilyes Khemakhem, Ricardo Pio Monti
Identifiability of latent-variable and structural-equation models: from linear to nonlinear. CoRR (2023)
Hiroshi Morioka, Aapo Hyvärinen
Connectivity-contrastive learning: Combining causal discovery and representation learning for multimodal data. AISTATS (2023)
Omar Chehab, Aapo Hyvärinen, Andrej Risteski
Provable benefits of annealing for estimating normalizing constants: Importance Sampling, Noise-Contrastive Estimation, and beyond. NeurIPS (2023)
Omar Chehab, Alexandre Gramfort, Aapo Hyvärinen
The Optimal Noise in Noise-Contrastive Learning Is Not What You Think. CoRR (2022)
Omar Chehab, Alexandre Gramfort, Aapo Hyvärinen
The optimal noise in noise-contrastive learning is not what you think. UAI (2022)
Ilmari Kurki, Aapo Hyvärinen, Linda Henriksson
Dynamics of retinotopic spatial attention revealed by multifocal MEG. NeuroImage 263 (2022)
Antti Hyttinen, Vitória Barin Pacela, Aapo Hyvärinen
Binary independent component analysis: a non-stationarity-based approach. UAI (2022)
Aapo Hyvärinen
Painful intelligence: What AI can tell us about human suffering. CoRR (2022)
Ilyes Khemakhem, Ricardo Pio Monti, Robert Leech, Aapo Hyvärinen
Causal Autoregressive Flows. AISTATS (2021)
Wenkai Xu, Gang Niu, Aapo Hyvärinen, Masashi Sugiyama
Direction Matters: On Influence-Preserving Graph Summarization and Max-Cut Principle for Directed Graphs. Neural Comput. 33 (8) (2021)
Antti Hyttinen, Vitória Barin Pacela, Aapo Hyvärinen
Binary Independent Component Analysis via Non-stationarity. CoRR (2021)
Hugo Richard, Pierre Ablin, Bertrand Thirion, Alexandre Gramfort, Aapo Hyvärinen
Shared Independent Component Analysis for Multi-Subject Neuroimaging. NeurIPS (2021)
Takeru Matsuda, Masatoshi Uehara, Aapo Hyvärinen
Information criteria for non-normalized models. J. Mach. Learn. Res. 22 (2021)
Alexandre Gramfort, Hubert J. Banville, Omar Chehab, Aapo Hyvärinen, Denis A. Engemann
Learning with self-supervision on EEG data. BCI (2021)
Hiroshi Morioka, Hermanni Hälvä, Aapo Hyvärinen
Independent Innovation Analysis for Nonlinear Vector Autoregressive Process. AISTATS (2021)
Hermanni Hälvä, Sylvain Le Corff, Luc Lehéricy, Jonathan So, Yongjie Zhu, Elisabeth Gassiat, Aapo Hyvärinen
Disentangling Identifiable Features from Noisy Data with Structured Nonlinear ICA. CoRR (2021)
Hugo Richard, Pierre Ablin, Bertrand Thirion, Alexandre Gramfort, Aapo Hyvärinen
Shared Independent Component Analysis for Multi-Subject Neuroimaging. CoRR (2021)
Hugo Richard, Pierre Ablin, Aapo Hyvärinen, Alexandre Gramfort, Bertrand Thirion
Adaptive Multi-View ICA: Estimation of noise levels for optimal inference. CoRR (2021)
Hermanni Hälvä, Sylvain Le Corff, Luc Lehéricy, Jonathan So, Yongjie Zhu, Elisabeth Gassiat, Aapo Hyvärinen
Disentangling Identifiable Features from Noisy Data with Structured Nonlinear ICA. NeurIPS (2021)
Hiroaki Sasaki, Takashi Takenouchi, Ricardo Pio Monti, Aapo Hyvärinen
Robust contrastive learning and nonlinear ICA in the presence of outliers. UAI (2020)
Thuc Duy Le, Lin Liu, Kun Zhang, Emre Kiciman, Peng Cui, Aapo Hyvärinen
Preface: The 2020 ACM SIGKDD Workshop on Causal Discovery. CD@KDD (2020)
Hubert J. Banville, Omar Chehab, Aapo Hyvärinen, Denis-Alexander Engemann, Alexandre Gramfort
Uncovering the structure of clinical EEG signals with self-supervised learning. CoRR (2020)
Ilyes Khemakhem, Ricardo Pio Monti, Robert Leech, Aapo Hyvärinen
Causal Autoregressive Flows. CoRR (2020)
Hugo Richard, Luigi Gresele, Aapo Hyvärinen, Bertrand Thirion, Alexandre Gramfort, Pierre Ablin
Modeling Shared Responses in Neuroimaging Studies through MultiView ICA. CoRR (2020)
Luigi Gresele, Giancarlo Fissore, Adrián Javaloy, Bernhard Schölkopf, Aapo Hyvärinen
Relative gradient optimization of the Jacobian term in unsupervised deep learning. CoRR (2020)
Hiroshi Morioka, Vince D. Calhoun, Aapo Hyvärinen
Nonlinear ICA of fMRI reveals primitive temporal structures linked to rest, task, and behavioral traits. NeuroImage 218 (2020)
Hugo Richard, Luigi Gresele, Aapo Hyvärinen, Bertrand Thirion, Alexandre Gramfort, Pierre Ablin
Modeling Shared responses in Neuroimaging Studies through MultiView ICA. NeurIPS (2020)
Ilyes Khemakhem, Diederik P. Kingma, Ricardo Pio Monti, Aapo Hyvärinen
Variational Autoencoders and Nonlinear ICA: A Unifying Framework. AISTATS (2020)
Miika Koskinen, Mikko Kurimo, Joachim Gross, Aapo Hyvärinen, Riitta Hari
Brain activity reflects the predictability of word sequences in listened continuous speech. NeuroImage 219 (2020)
Ricardo Pio Monti, Ilyes Khemakhem, Aapo Hyvärinen
Autoregressive flow-based causal discovery and inference. CoRR (2020)
Hermanni Hälvä, Aapo Hyvärinen
Hidden Markov Nonlinear ICA: Unsupervised Learning from Nonstationary Time Series. UAI (2020)
Ilyes Khemakhem, Ricardo Pio Monti, Diederik P. Kingma, Aapo Hyvärinen
ICE-BeeM: Identifiable Conditional Energy-Based Deep Models Based on Nonlinear ICA. NeurIPS (2020)
Luigi Gresele, Giancarlo Fissore, Adrián Javaloy, Bernhard Schölkopf, Aapo Hyvärinen
Relative gradient optimization of the Jacobian term in unsupervised deep learning. NeurIPS (2020)
Hermanni Hälvä, Aapo Hyvärinen
Hidden Markov Nonlinear ICA: Unsupervised Learning from Nonstationary Time Series. CoRR (2020)
Ilyes Khemakhem, Ricardo Pio Monti, Diederik P. Kingma, Aapo Hyvärinen
ICE-BeeM: Identifiable Conditional Energy-Based Deep Models. CoRR (2020)
Hiroshi Morioka, Aapo Hyvärinen
Independent innovation analysis for nonlinear vector autoregressive process. CoRR (2020)
Alexander Y. Zhigalov, Erkka Heinilä, Tiina Parviainen, Lauri Parkkonen, Aapo Hyvärinen
Decoding attentional states for neurofeedback: Mindfulness vs. wandering thoughts. NeuroImage 185 (2019)
Takeru Matsuda, Masatoshi Uehara, Aapo Hyvärinen
Information criteria for non-normalized models. CoRR (2019)
Hiroaki Sasaki, Takashi Takenouchi, Ricardo Pio Monti, Aapo Hyvärinen
Robust contrastive learning and nonlinear ICA in the presence of outliers. CoRR (2019)
Takeru Matsuda, Aapo Hyvärinen
Estimation of Non-Normalized Mixture Models. AISTATS (2019)
Thuc Duy Le, Jiuyong Li, Kun Zhang, Emre Kiciman, Peng Cui, Aapo Hyvärinen
Preface: The 2019 ACM SIGKDD Workshop on Causal Discovery. CD@KDD (2019)
Hubert J. Banville, Isabela Albuquerque, Aapo Hyvärinen, Graeme Moffat, Denis-Alexander Engemann, Alexandre Gramfort
Self-supervised representation learning from electroencephalography signals. CoRR (2019)
Aapo Hyvärinen, Hiroaki Sasaki, Richard E. Turner
Nonlinear ICA Using Auxiliary Variables and Generalized Contrastive Learning. AISTATS (2019)
Ricardo Pio Monti, Kun Zhang, Aapo Hyvärinen
Causal Discovery with General Non-Linear Relationships using Non-Linear ICA. UAI (2019)
Ricardo Pio Monti, Kun Zhang, Aapo Hyvärinen
Causal Discovery with General Non-Linear Relationships Using Non-Linear ICA. CoRR (2019)
Hubert J. Banville, Graeme Moffat, Isabela Albuquerque, Denis-Alexander Engemann, Aapo Hyvärinen, Alexandre Gramfort
Self-Supervised Representation Learning from Electroencephalography Signals. MLSP (2019)
Ilyes Khemakhem, Diederik P. Kingma, Aapo Hyvärinen
Variational Autoencoders and Nonlinear ICA: A Unifying Framework. CoRR (2019)
Saeed Saremi, Aapo Hyvärinen
Neural Empirical Bayes. CoRR (2019)
Wenkai Xu, Gang Niu, Aapo Hyvärinen, Masashi Sugiyama
Direction Matters: On Influence-Preserving Graph Summarization and Max-cut Principle for Directed Graphs. CoRR (2019)
Saeed Saremi, Aapo Hyvärinen
Neural Empirical Bayes. J. Mach. Learn. Res. 20 (2019)
Ricardo Pio Monti, Aapo Hyvärinen
A Unified Probabilistic Model for Learning Latent Factors and Their Connectivities from High-Dimensional Data. CoRR (2018)
Takeru Matsuda, Aapo Hyvärinen
Estimation of Non-Normalized Mixture Models and Clustering Using Deep Representation. CoRR (2018)
Saeed Saremi, Arash Mehrjou, Bernhard Schölkopf, Aapo Hyvärinen
Deep Energy Estimator Networks. CoRR (2018)
Aapo Hyvärinen, Hiroaki Sasaki, Richard E. Turner
Nonlinear ICA Using Auxiliary Variables and Generalized Contrastive Learning. CoRR (2018)
Hiroaki Sasaki, Aapo Hyvärinen
Neural-Kernelized Conditional Density Estimation. CoRR (2018)
Thuc Duy Le, Kun Zhang, Emre Kiciman, Aapo Hyvärinen, Lin Liu
Preface: The 2018 ACM SIGKDD Workshop on Causal Discovery. CD@KDD (2018)
Ricardo Pio Monti, Aapo Hyvärinen
A unified probabilistic model for learning latent factors and their connectivities from high-dimensional data . UAI (2018)
Aapo Hyvärinen, Hiroshi Morioka
Nonlinear ICA of Temporally Dependent Stationary Sources. AISTATS (2017)
Junichiro Hirayama, Aapo Hyvärinen, Motoaki Kawanabe
SPLICE: Fully Tractable Hierarchical Extension of ICA with Pooling. ICML (2017)
Hande Çelikkanat, Hiroki Moriya, Takeshi Ogawa, Jukka-Pekka Kauppi, Motoaki Kawanabe, Aapo Hyvärinen
Decoding emotional valence from electroencephalographic rhythmic activity. EMBC (2017)
Bharath K. Sriperumbudur, Kenji Fukumizu, Arthur Gretton, Aapo Hyvärinen, Revant Kumar
Density Estimation in Infinite Dimensional Exponential Families. J. Mach. Learn. Res. 18 (2017)
Hiroaki Sasaki, Michael Gutmann, Hayaru Shouno, Aapo Hyvärinen
Simultaneous Estimation of Nongaussian Components and Their Correlation Structure. Neural Comput. 29 (11) (2017)
Haruo Hosoya, Aapo Hyvärinen
A mixture of sparse coding models explaining properties of face neurons related to holistic and parts-based processing. PLoS Comput. Biol. 13 (7) (2017)
Hiroaki Sasaki, Takafumi Kanamori, Aapo Hyvärinen, Gang Niu, Masashi Sugiyama
Mode-Seeking Clustering and Density Ridge Estimation via Direct Estimation of Density-Derivative-Ratios. J. Mach. Learn. Res. 18 (2017)
Junichiro Hirayama, Aapo Hyvärinen, Shin Ishii
Sparse and low-rank matrix regularization for learning time-varying Markov networks. Mach. Learn. 105 (3) (2016)
Aapo Hyvärinen, Hiroshi Morioka
Unsupervised Feature Extraction by Time-Contrastive Learning and Nonlinear ICA. NIPS (2016)
Haruo Hosoya, Aapo Hyvärinen
Learning Visual Spatial Pooling by Strong PCA Dimension Reduction. Neural Comput. 28 (7) (2016)
Aapo Hyvärinen, Hiroshi Morioka
Unsupervised Feature Extraction by Time-Contrastive Learning and Nonlinear ICA. CoRR (2016)
Aapo Hyvärinen, Junichiro Hirayama, Vesa Kiviniemi, Motoaki Kawanabe
Orthogonal Connectivity Factorization: Interpretable Decomposition of Variability in Correlation Matrices. Neural Comput. 28 (3) (2016)
Jouni Puuronen, Aapo Hyvärinen
Independent component analysis with an inverse problem motivated penalty term. IJCNN (2015)
Junichiro Hirayama, Takeshi Ogawa, Aapo Hyvärinen
Unifying Blind Separation and Clustering for Resting-State EEG/MEG Functional Connectivity Analysis. Neural Comput. 27 (7) (2015)
Tatsuya Tashiro, Shohei Shimizu, Aapo Hyvärinen, Takashi Washio
ParceLiNGAM: A Causal Ordering Method Robust Against Latent Confounders. Neural Comput. 26 (1) (2014)
Aapo Hyvärinen, Junichiro Hirayama, Motoaki Kawanabe
Dynamic connectivity factorization: Interpretable decompositions of non-stationarity. PRNI (2014)
Hiroaki Sasaki, Aapo Hyvärinen, Masashi Sugiyama
Clustering via Mode Seeking by Direct Estimation of the Gradient of a Log-Density. ECML/PKDD (3) (2014)
Jouni Puuronen, Aapo Hyvärinen
A Bayesian inverse solution using independent component analysis. Neural Networks 50 (2014)
Shohei Shimizu, Aapo Hyvärinen, Yoshinobu Kawahara
A direct method for estimating a causal ordering in a linear non-Gaussian acyclic model. CoRR (2014)
Hiroaki Sasaki, Michael Gutmann, Hayaru Shouno, Aapo Hyvärinen
Estimating Dependency Structures for non-Gaussian Components with Linear and Energy Correlations. AISTATS (2014)
Stephen M. Smith, Aapo Hyvärinen, Gaël Varoquaux, Karla L. Miller, Christian F. Beckmann
Group-PCA for very large fMRI datasets. NeuroImage 101 (2014)
Pavan Ramkumar, Lauri Parkkonen, Aapo Hyvärinen
Group-level spatial independent component analysis of Fourier envelopes of resting-state MEG data. NeuroImage 86 (2014)
Junichiro Hirayama, Takeshi Ogawa, Aapo Hyvärinen
Simultaneous blind separation and clustering of coactivated EEG/MEG sources for analyzing spontaneous brain activity. EMBC (2014)
Jukka-Pekka Kauppi, Lauri Parkkonen, Riitta Hari, Aapo Hyvärinen
Decoding magnetoencephalographic rhythmic activity using spectrospatial information. NeuroImage 83 (2013)
Hiroaki Sasaki, Michael Gutmann, Hayaru Shouno, Aapo Hyvärinen
Correlated topographic analysis: estimating an ordering of correlated components. Mach. Learn. 92 (2-3) (2013)
Kun Zhang, Heng Peng, Laiwan Chan, Aapo Hyvärinen
Bridging Information Criteria and Parameter Shrinkage for Model Selection. CoRR (2013)
Aapo Hyvärinen, Stephen M. Smith
Pairwise likelihood ratios for estimation of non-Gaussian structural equation models. J. Mach. Learn. Res. 14 (1) (2013)
Hiroaki Sasaki, Michael Gutmann, Hayaru Shouno, Aapo Hyvärinen
Topographic Analysis of Correlated Components. ACML (2012)
Michael Gutmann, Aapo Hyvärinen
Learning a selectivity-invariance-selectivity feature extraction architecture for images. ICPR (2012)
Tatsuya Tashiro, Shohei Shimizu, Aapo Hyvärinen, Takashi Washio
Estimation of Causal Orders in a Linear Non-Gaussian Acyclic Model: A Method Robust against Latent Confounders. ICANN (1) (2012)
Shohei Shimizu, Aapo Hyvärinen, Yutaka Kano, Patrik O. Hoyer
Discovery of non-gaussian linear causal models using ICA CoRR (2012)
Kun Zhang, Aapo Hyvärinen
Source Separation and Higher-Order Causal Analysis of MEG and EEG CoRR (2012)
Kun Zhang, Aapo Hyvärinen
On the Identifiability of the Post-Nonlinear Causal Model CoRR (2012)
Patrik O. Hoyer, Aapo Hyvärinen, Richard Scheines, Peter Spirtes, Joseph D. Ramsey, Gustavo Lacerda, Shohei Shimizu
Causal discovery of linear acyclic models with arbitrary distributions CoRR (2012)
Miika Pihlaja, Michael Gutmann, Aapo Hyvärinen
A Family of Computationally Efficient and Simple Estimators for Unnormalized Statistical Models CoRR (2012)
Michael Gutmann, Aapo Hyvärinen
Noise-Contrastive Estimation of Unnormalized Statistical Models, with Applications to Natural Image Statistics. J. Mach. Learn. Res. 13 (2012)

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Publication Activity (10 Years)

Top Topics

Top Venues

Aapo Hyvärinen