Publications by topic 
Estimation theory[These papers propose principles for estimation of statistical models, especially nonnormalized ones, a.k.a. energybased models.]Review on the topic
M. U. Gutmann and A. Hyvärinen. Estimation of unnormalized statistical models without numerical integration. Proc. Int. Workshop on InformationTheoretic Methods in Science and Engineering, Tokyo, Japan, 2013.
Noisecontrastive estimationM. Gutmann and A. Hyvärinen. NoiseContrastive Estimation of Unnormalized Statistical Models, with Applications to Natural Image Statistics, J. Machine Learning Research 13:307361, 2012.pdf Matlab code [The fundamental paper first proposing NCE, based on our AISTATS2010 paper. NCE is one of our two fundamental methods for estimating statistical models when the normalization constant (partition function) is not known.]
O. Chehab, A. Hyvärinen, and A. Risteski.
Provable benefits of annealing for estimating normalizing constants: Importance Sampling, NoiseContrastive Estimation, and beyond. NeurIPS 2023.
O. Chehab, A. Gramfort and A. Hyvärinen.
The Optimal Noise in NoiseContrastive Estimation Is Not What You Think. UAI 2022.
M. Pihlaja, M. Gutmann and A. Hyvärinen. A Family of Computationally Efficient and Simple Estimators for Unnormalized Statistical Models. Proc. UAI2010.
M. Gutmann and A. Hyvärinen.
Learning features by contrasting natural images with noise.
Proc. Int. Conf. on Artificial Neural Networks (ICANN2009), Limassol, Cyprus, 2009.
Score matching
A. Hyvärinen. Estimation of nonnormalized statistical models using score matching.
Journal of Machine Learning Research, 6:695709, 2005.
A. Hyvärinen. Some extensions of score matching.
Computational Statistics & Data Analysis, 51:24992512, 2007.
A. Hyvärinen. Connections between score matching, contrastive divergence, and pseudolikelihood for continuousvalued variables.
IEEE Transactions on Neural Networks, 18(5):15291531, 2007.
A. Hyvärinen. Optimal approximation of signal priors.
Neural Computation, 20:30873110, 2008.
A. Hyvärinen. Estimation theory and information geometry based on denoising.
Proc. Workshop on Information Theory in Science and Engineering, Tampere, Finland, 2008.
General
T. Matsuda, M. Uehara, and A. Hyvärinen. Information criteria for nonnormalized models. JMLR, 22: 133, 2021
