Publications by topic 
Estimation theory[These papers propose two principles for estimation of statistical models, especially nonnormalized ones: noisecontrastive estimation and score matching.]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 estimation
M. Gutmann and A. Hyvärinen.
NoiseContrastive Estimation of Unnormalized Statistical Models, with
Applications to Natural Image Statistics, J. Machine Learning Research 13:307361, 2012.
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. Optimal approximation of signal priors.
Neural Computation, 20:30873110, 2008.
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. Estimation theory and information geometry based on denoising.
Proc. Workshop on Information Theory in Science and Engineering, Tampere, Finland, 2008.
