Publications by topic 
Unsupervised deep learningReview papers
A. Hyvärinen, I. Khemakhem, and H. Morioka. Nonlinear Independent Component Analysis for Principled Disentanglement in Unsupervised Deep Learning. Patterns,
4(10):100844, 2023.
A. Hyvärinen, I. Khemakhem, and R. Monti. Identifiability of latentvariable and structuralequation models: from linear to nonlinear. Annals of the Institute of Statistical Mathematics, in press.
Nonlinear Independent Component Analysis[Recently we have developed a new framework for a nonlinear version of ICA, which can be seen as a principled approach to "disentanglement".]
Hermanni Hälvä, Sylvain Le Corff, Luc Lehéricy, Jonathan So, Yongjie Zhu, Elisabeth Gassiat, Aapo Hyvarinen. Disentangling Identifiable Features from Noisy Data with Structured Nonlinear ICA. NeurIPS2021.
H. Morioka, H. Hälvä, and A. Hyvärinen. Independent innovation analysis for nonlinear vector autoregressive process.AISTATS2021.
Ilyes Khemakhem, Diederik P. Kingma, Ricardo P. Monti, and Aapo Hyvärinen.
ICEBeeM: Identifiable Conditional EnergyBased Deep Models. NeurIPS 2020.
Luigi Gresele, Giancarlo Fissore, Adrián Javaloy, Bernhard Schölkopf, Aapo Hyvärinen. Relative gradient optimization of the Jacobian term in unsupervised deep learning.
NeurIPS2020.
H. Hälvä and A. Hyvärinen. Hidden Markov Nonlinear ICA: Unsupervised Learning from Nonstationary Time Series.UAI2020.
Hiroaki Sasaki, Takashi Takenouchi, Ricardo Monti, Aapo Hyvärinen. Robust contrastive learning and nonlinear ICA in the presence of outliers.UAI2020.
Ilyes Khemakhem, Diederik P. Kingma, Ricardo P. Monti, and Aapo Hyvärinen.
Variational Autoencoders and Nonlinear ICA: A Unifying Framework. AISTATS2020.
A. Hyvärinen, H. Sasaki, and R.E. Turner. Nonlinear ICA Using Auxiliary Variables and Generalized Contrastive Learning.
AISTATS 2019.
A. Hyvärinen and H. Morioka.
Unsupervised Feature Extraction by TimeContrastive Learning and Nonlinear ICA.
NIPS 2016.
A. Hyvärinen and H. Morioka.
Nonlinear ICA of Temporally Dependent Stationary Sources.
AISTATS 2017.
A. Hyvärinen and P. Pajunen. Nonlinear Independent Component Analysis:
Existence and Uniqueness results. Neural Networks 12(3): 429439, 1999.
Density estimation / Energybased modelling[An alternative goal in unsupervised learning is to model the probability density of data.]
S. Saremi and A. Hyvärinen. Neural Empirical Bayes.
J. Machine Learning Research, (181):123, 2019.
S. Saremi, A. Merjou, B. Schölkopf and A. Hyvärinen. Deep Energy Estimator Networks.
Arxiv, May 2018.
H. Sasaki and A. Hyvärinen. NeuralKernelized Conditional Density Estimation.
Arxiv, June 2018.
Further unsupervised deep learning
J. Hirayama, A. Hyvärinen and M. Kawanabe.
SPLICE: Fully Tractable Hierarchical Extension of ICA with Pooling.
ICML 2017.
T. Matsuda and A. Hyvärinen. Estimation of NonNormalized Mixture Models and Clustering Using Deep Representation.
AISTATS 2019.
