Mikael Kuusela
Cited by
Cited by
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
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
Heat stored in the Earth system 1960–2020: where does the energy go?
K Von Schuckmann, A Minière, F Gues, FJ Cuesta-Valero, G Kirchengast, ...
Earth System Science Data 15 (4), 1675-1709, 2023
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
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
Model-independent detection of new physics signals using interpretable SemiSupervised classifier tests
P Chakravarti, M Kuusela, J Lei, L Wasserman
The Annals of Applied Statistics 17 (4), 2759-2795, 2023
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
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
Uncertainty quantification for wide-bin unfolding: one-at-a-time strict bounds and prior-optimized confidence intervals
M Stanley, P Patil, M Kuusela
Journal of Instrumentation 17 (10), P10013, 2022
Objective Frequentist Uncertainty Quantification for Atmospheric Retrievals
P Patil, M Kuusela, J Hobbs
SIAM/ASA Journal on Uncertainty Quantification 10 (3), 827-859, 2022
Uncertainty quantification in unfolding elementary particle spectra at the Large Hadron Collider
MJ Kuusela
EPFL, 2016
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
Simulation-based inference with waldo: Perfectly calibrated confidence regions using any prediction or posterior estimation algorithm
L Masserano, T Dorigo, R Izbicki, M Kuusela, AB Lee
arXiv preprint arXiv:2205.15680, 2022
Neural likelihood surfaces for spatial processes with computationally intensive or intractable likelihoods
J Walchessen, A Lenzi, M Kuusela
arXiv preprint arXiv:2305.04634, 2023
Quantification of Aquarius, SMAP, SMOS and Argo-based gridded sea surface salinity product sampling errors
S Fournier, FM Bingham, C González-Haro, A Hayashi, KM Ulfsax Carlin, ...
Remote Sensing 15 (2), 422, 2023
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, 2 (2021)
P Chakravarti, M Kuusela, J Lei, L Wasserman
arXiv preprint arXiv:2102.07679, 0
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Articles 1–20