Hyvarinen, Aapo

64 publications

TMLR 2025 A Noise-Corrected Langevin Algorithm and Sampling by Half-Denoising Aapo Hyvarinen

ICML 2025 Density Ratio Estimation with Conditional Probability Paths Hanlin Yu, Arto Klami, Aapo Hyvarinen, Anna Korba, Omar Chehab

ICML 2024 Causal Representation Learning Made Identifiable by Grouping of Observational Variables Hiroshi Morioka, Aapo Hyvarinen

AISTATS 2024 Identifiable Feature Learning for Spatial Data with Nonlinear ICA Hermanni Hälvä, Jonathan So, Richard E. Turner, Aapo Hyvärinen

AISTATS 2023 Connectivity-Contrastive Learning: Combining Causal Discovery and Representation Learning for Multimodal Data Hiroshi Morioka, Aapo Hyvarinen

NeurIPS 2023 Provable Benefits of Annealing for Estimating Normalizing Constants: Importance Sampling, Noise-Contrastive Estimation, and Beyond Omar Chehab, Aapo Hyvarinen, Andrej Risteski

UAI 2022 Binary Independent Component Analysis: A Non-Stationarity-Based Approach Antti Hyttinen, Vitória Barin Pacela, Aapo Hyvärinen

UAI 2022 The Optimal Noise in Noise-Contrastive Learning Is Not What You Think Omar Chehab, Alexandre Gramfort, Aapo Hyvärinen

AISTATS 2021 Causal Autoregressive Flows Ilyes Khemakhem, Ricardo Monti, Robert Leech, Aapo Hyvarinen

AISTATS 2021 Independent Innovation Analysis for Nonlinear Vector Autoregressive Process Hiroshi Morioka, Hermanni Hälvä, Aapo Hyvarinen

NeurIPS 2021 Disentangling Identifiable Features from Noisy Data with Structured Nonlinear ICA Hermanni Hälvä, Sylvain Le Corff, Luc Lehéricy, Jonathan So, Yongjie Zhu, Elisabeth Gassiat, Aapo Hyvarinen

JMLR 2021 Information Criteria for Non-Normalized Models Takeru Matsuda, Masatoshi Uehara, Aapo Hyvarinen

NeurIPS 2021 Shared Independent Component Analysis for Multi-Subject Neuroimaging Hugo Richard, Pierre Ablin, Bertrand Thirion, Alexandre Gramfort, Aapo Hyvarinen

UAI 2020 Hidden Markov Nonlinear ICA: Unsupervised Learning from Nonstationary Time Series Hermanni Hälvä, Aapo Hyvarinen

NeurIPS 2020 ICE-BeeM: Identifiable Conditional Energy-Based Deep Models Based on Nonlinear ICA Ilyes Khemakhem, Ricardo Monti, Diederik Kingma, Aapo Hyvarinen

NeurIPS 2020 Modeling Shared Responses in Neuroimaging Studies Through MultiView ICA Hugo Richard, Luigi Gresele, Aapo Hyvarinen, Bertrand Thirion, Alexandre Gramfort, Pierre Ablin

NeurIPS 2020 Relative Gradient Optimization of the Jacobian Term in Unsupervised Deep Learning Luigi Gresele, Giancarlo Fissore, Adrián Javaloy, Bernhard Schölkopf, Aapo Hyvarinen

UAI 2020 Robust Contrastive Learning and Nonlinear ICA in the Presence of Outliers Hiroaki Sasaki, Takashi Takenouchi, Ricardo Monti, Aapo Hyvarinen

AISTATS 2020 Variational Autoencoders and Nonlinear ICA: A Unifying Framework Ilyes Khemakhem, Diederik Kingma, Ricardo Monti, Aapo Hyvarinen

UAI 2019 Causal Discovery with General Non-Linear Relationships Using Non-Linear ICA Ricardo Pio Monti, Kun Zhang, Aapo Hyvärinen

AISTATS 2019 Estimation of Non-Normalized Mixture Models Takeru Matsuda, Aapo Hyvärinen

JMLR 2019 Neural Empirical Bayes Saeed Saremi, Aapo Hyvärinen

AISTATS 2019 Nonlinear ICA Using Auxiliary Variables and Generalized Contrastive Learning Aapo Hyvarinen, Hiroaki Sasaki, Richard Turner

UAI 2018 A Unified Probabilistic Model for Learning Latent Factors and Their Connectivities from High-Dimensional Data Ricardo Pio Monti, Aapo Hyvärinen

JMLR 2017 Density Estimation in Infinite Dimensional Exponential Families Bharath Sriperumbudur, Kenji Fukumizu, Arthur Gretton, Aapo Hyvärinen, Revant Kumar

AISTATS 2017 Nonlinear ICA of Temporally Dependent Stationary Sources Aapo Hyvärinen, Hiroshi Morioka

ICML 2017 SPLICE: Fully Tractable Hierarchical Extension of ICA with Pooling Jun-ichiro Hirayama, Aapo Hyvärinen, Motoaki Kawanabe

MLJ 2016 Sparse and Low-Rank Matrix Regularization for Learning Time-Varying Markov Networks Junichiro Hirayama, Aapo Hyvärinen, Shin Ishii

NeurIPS 2016 Unsupervised Feature Extraction by Time-Contrastive Learning and Nonlinear ICA Aapo Hyvarinen, Hiroshi Morioka

ECML-PKDD 2014 Clustering via Mode Seeking by Direct Estimation of the Gradient of a Log-Density Hiroaki Sasaki, Aapo Hyvärinen, Masashi Sugiyama

AISTATS 2014 Estimating Dependency Structures for Non-Gaussian Components with Linear and Energy Correlations Hiroaki Sasaki, Michael Gutmann, Hayaru Shouno, Aapo Hyvärinen

MLJ 2013 Correlated Topographic Analysis: Estimating an Ordering of Correlated Components Hiroaki Sasaki, Michael Gutmann, Hayaru Shouno, Aapo Hyvärinen

JMLR 2013 Pairwise Likelihood Ratios for Estimation of Non-Gaussian Structural Equation Models Aapo Hyvärinen, Stephen M. Smith

JMLR 2012 Noise-Contrastive Estimation of Unnormalized Statistical Models, with Applications to Natural Image Statistics Michael U. Gutmann, Aapo Hyvärinen

ACML 2012 Topographic Analysis of Correlated Components Hiroaki Sasaki, Michael U. Gutmann, Hayaru Shouno, Aapo Hyvärinen

ACML 2011 A General Linear Non-Gaussian State-Space Model: Identifiability, Identification, and Applications Kun Zhang, Aapo Hyvärinen

JMLR 2011 DirectLiNGAM: A Direct Method for Learning a Linear Non-Gaussian Structural Equation Model Shohei Shimizu, Takanori Inazumi, Yasuhiro Sogawa, Aapo Hyvärinen, Yoshinobu Kawahara, Takashi Washio, Patrik O. Hoyer, Kenneth Bollen

NeurIPS 2011 Structural Equations and Divisive Normalization for Energy-Dependent Component Analysis Jun-ichiro Hirayama, Aapo Hyvärinen

UAI 2010 A Family of Computationally E Cient and Simple Estimators for Unnormalized Statistical Models Miika Pihlaja, Michael Gutmann, Aapo Hyvärinen

JMLR 2010 Estimation of a Structural Vector Autoregression Model Using Non-Gaussianity Aapo Hyvärinen, Kun Zhang, Shohei Shimizu, Patrik O. Hoyer

AISTATS 2010 Noise-Contrastive Estimation: A New Estimation Principle for Unnormalized Statistical Models Michael Gutmann, Aapo Hyvärinen

ACML 2010 Pairwise Measures of Causal Direction in Linear Non-Gaussian Acyclic Models Aapo Hyvarinen

UAI 2010 Source Separation and Higher-Order Causal Analysis of MEG and EEG Kun Zhang, Aapo Hyvärinen

UAI 2009 A Direct Method for Estimating a Causal Ordering in a Linear Non-Gaussian Acyclic Model Shohei Shimizu, Aapo Hyvärinen, Yoshinobu Kawahara

ECML-PKDD 2009 Causality Discovery with Additive Disturbances: An Information-Theoretical Perspective Kun Zhang, Aapo Hyvärinen

UAI 2009 On the Identifiability of the Post-Nonlinear Causal Model Kun Zhang, Aapo Hyvärinen

UAI 2008 Causal Discovery of Linear Acyclic Models with Arbitrary Distributions Patrik O. Hoyer, Aapo Hyvärinen, Richard Scheines, Peter Spirtes, Joseph D. Ramsey, Gustavo Lacerda, Shohei Shimizu

ICML 2008 Causal Modelling Combining Instantaneous and Lagged Effects: An Identifiable Model Based on Non-Gaussianity Aapo Hyvärinen, Shohei Shimizu, Patrik O. Hoyer

JMLR 2006 A Linear Non-Gaussian Acyclic Model for Causal Discovery Shohei Shimizu, Patrik O. Hoyer, Aapo Hyvärinen, Antti Kerminen

NeurIPS 2006 Emergence of Conjunctive Visual Features by Quadratic Independent Component Analysis J.t. Lindgren, Aapo Hyvärinen

UAI 2005 Discovery of Non-Gaussian Linear Causal Models Using ICA Shohei Shimizu, Aapo Hyvärinen, Yutaka Kano, Patrik O. Hoyer

JMLR 2005 Estimation of Non-Normalized Statistical Models by Score Matching Aapo Hyvärinen

NeCo 2003 Simple-Cell-like Receptive Fields Maximize Temporal Coherence in Natural Video Jarmo Hurri, Aapo Hyvärinen

NeurIPS 2002 Interpreting Neural Response Variability as Monte Carlo Sampling of the Posterior Patrik O. Hoyer, Aapo Hyvärinen

NeurIPS 2002 Temporal Coherence, Natural Image Sequences, and the Visual Cortex Jarmo Hurri, Aapo Hyvärinen

NeCo 2001 Complexity Pursuit: Separating Interesting Components from Time Series Aapo Hyvärinen

NeCo 2001 Topographic Independent Component Analysis Aapo Hyvärinen, Patrik O. Hoyer, Mika Inki

NeCo 2000 Emergence of Phase- and Shift-Invariant Features by Decomposition of Natural Images into Independent Feature Subspaces Aapo Hyvärinen, Patrik O. Hoyer

NeurIPS 1999 Emergence of Topography and Complex Cell Properties from Natural Images Using Extensions of ICA Aapo Hyvärinen, Patrik O. Hoyer

NeCo 1999 Sparse Code Shrinkage: Denoising of Nongaussian Data by Maximum Likelihood Estimation Aapo Hyvärinen

NeurIPS 1998 Sparse Code Shrinkage: Denoising by Nonlinear Maximum Likelihood Estimation Aapo Hyvärinen, Patrik O. Hoyer, Erkki Oja

NeCo 1997 A Fast Fixed-Point Algorithm for Independent Component Analysis Aapo Hyvärinen, Erkki Oja

NeurIPS 1997 New Approximations of Differential Entropy for Independent Component Analysis and Projection Pursuit Aapo Hyvärinen

NeurIPS 1996 One-Unit Learning Rules for Independent Component Analysis Aapo Hyvärinen, Erkki Oja