Mikael Kuusela
Title
Cited by
Cited by
Year
Approximate Riemannian conjugate gradient learning for fixed-form variational Bayes
A Honkela, T Raiko, M Kuusela, M Tornio, J Karhunen
The Journal of Machine Learning Research 11, 3235-3268, 2010
1052010
Semi-supervised detection of collective anomalies with an application in high energy particle physics
T Vatanen, M Kuusela, E Malmi, T Raiko, T Aaltonen, Y Nagai
The 2012 International Joint Conference on Neural Networks (IJCNN), 1-8, 2012
292012
Statistical unfolding of elementary particle spectra: Empirical Bayes estimation and bias-corrected uncertainty quantification
M Kuusela, VM Panaretos
The Annals of Applied Statistics 9 (3), 1671–1705, 2015
24*2015
Locally stationary spatio-temporal interpolation of Argo profiling float data
M Kuusela, ML Stein
Proceedings of the Royal Society A 474 (2220), 20180400, 2018
222018
A gradient-based algorithm competitive with variational Bayesian EM for mixture of Gaussians
M Kuusela, T Raiko, A Honkela, J Karhunen
2009 International Joint Conference on Neural Networks, 1688-1695, 2009
152009
Semi-supervised anomaly detection–towards model-independent searches of new physics
M Kuusela, T Vatanen, E Malmi, T Raiko, T Aaltonen, Y Nagai
Journal of Physics: Conference Series 368 (1), 012032, 2012
132012
Multivariate techniques for identifying diffractive interactions at the LHC
M Kuusela, JW Lämsä, E Malmi, P Mehtälä, R Orava
International Journal of Modern Physics A 25 (08), 1615-1647, 2010
82010
Shape-constrained uncertainty quantification in unfolding steeply falling elementary particle spectra
M Kuusela, PB Stark
The Annals of Applied Statistics 11 (3), 1671-1710, 2017
52017
Statistical issues in unfolding methods for high energy physics
M Kuusela
42012
Uncertainty quantification in unfolding elementary particle spectra at the Large Hadron Collider
MJ Kuusela
EPFL, 2016
32016
Introduction to unfolding in high energy physics
M Kuusela
Lecture at Advanced Scientific Computing Workshop, ETH Zurich (July 15, 2014 …, 2014
32014
Unfolding: A statistician’s perspective
M Kuusela
Conference Slides, Phystatν 9, 2016
22016
Soft classification of diffractive interactions at the LHC
M Kuusela, E Malmi, R Orava, T Vatanen
AIP Conference Proceedings 1350 (1), 111–114, 2011
22011
Objective frequentist uncertainty quantification for atmospheric CO retrievals
P Patil, M Kuusela, J Hobbs
arXiv preprint arXiv:2007.14975, 2020
2020
Data Science for Modern Oceanography: Statistics, Machine Learning, Visualization, and More
A Gray
Ocean Sciences Meeting 2020, 2020
2020
Statistics for Mapping Ocean Heat Content with Argo Floats: Modeling and Uncertainty Quantification
M Kuusela
Ocean Sciences Meeting 2020, 2020
2020
Locally stationary spatio-temporal interpolation of Argo float data
M Kuusela, M Stein
2018 Ocean Sciences Meeting, 2018
2018
Shape-Constrained Uncertainty Quantification in Unfolding Elementary Particle Spectra at the Large Hadron Collider
M Kuusela
2015
Fixed background EM algorithm for semi-supervised anomaly detection
T Vatanen, M Kuusela, E Malmi, T Raiko, T Aaltonen, Y Nagai
Aalto University, 2011
2011
Algorithms for Variational Learning of Mixture of Gaussians
M Kuusela
2008
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Articles 1–20