Mikael Kuusela
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Geciteerd door
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
Locally stationary spatio-temporal interpolation of Argo profiling float data
M Kuusela, ML Stein
Proceedings of the Royal Society A 474 (2220), 20180400, 2018
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
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
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
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
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
Statistical issues in unfolding methods for high energy physics
M Kuusela
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
Uncertainty quantification in unfolding elementary particle spectra at the Large Hadron Collider
MJ Kuusela
EPFL, 2016
Introduction to unfolding in high energy physics
M Kuusela
Lecture at Advanced Scientific Computing Workshop, ETH Zurich (July 15, 2014 …, 2014
Model-Independent Detection of New Physics Signals Using Interpretable Semi-Supervised Classifier Tests
P Chakravarti, M Kuusela, J Lei, L Wasserman
arXiv preprint arXiv:2102.07679, 2021
Unfolding: A statistician’s perspective
M Kuusela
Conference Slides, Phystatν 9, 2016
Soft classification of diffractive interactions at the LHC
M Kuusela, E Malmi, R Orava, T Vatanen
AIP Conference Proceedings 1350 (1), 111–114, 2011
Summertime increases in upper-ocean stratification and mixed-layer depth
JB Sallée, V Pellichero, C Akhoudas, E Pauthenet, L Vignes, S Schmidtko, ...
Nature 591 (7851), 592-598, 2021
Spatio-temporal Local Interpolation of Global Ocean Heat Transport using Argo Floats: A Debiased Latent Gaussian Process Approach
B Park, M Kuusela, D Giglio, A Gray
arXiv preprint arXiv:2105.09707, 2021
Spatio-temporal methods for estimating subsurface ocean thermal response to tropical cyclones
AJ Hu, M Kuusela, AB Lee, D Giglio, KM Wood
arXiv preprint arXiv:2012.15130, 2020
Objective frequentist uncertainty quantification for atmospheric CO retrievals
P Patil, M Kuusela, J Hobbs
arXiv preprint arXiv:2007.14975, 2020
Data Science for Modern Oceanography: Statistics, Machine Learning, Visualization, and More
A Gray
Ocean Sciences Meeting 2020, 2020
Statistics for Mapping Ocean Heat Content with Argo Floats: Modeling and Uncertainty Quantification
M Kuusela
Ocean Sciences Meeting 2020, 2020
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Artikelen 1–20