Follow
Mikael Kuusela
Title
Cited by
Cited by
Year
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
1422021
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
1312010
Locally stationary spatio-temporal interpolation of Argo profiling float data
M Kuusela, ML Stein
Proceedings of the Royal Society A 474 (2220), 20180400, 2018
772018
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
422012
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
412012
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
40*2015
Heat stored in the Earth system 1960–2020: where does the energy go?
K Von Schuckmann, A Minère, F Gues, FJ Cuesta-Valero, G Kirchengast, ...
Earth System Science Data Discussions 2022, 1-55, 2022
342022
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
222023
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
192009
Statistical issues in unfolding methods for high energy physics
M Kuusela
162012
Shape-constrained uncertainty quantification in unfolding steeply falling elementary particle spectra
M Kuusela, PB Stark
132017
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
82022
Uncertainty quantification in unfolding elementary particle spectra at the Large Hadron Collider
MJ Kuusela
EPFL, 2016
82016
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
Objective Frequentist Uncertainty Quantification for Atmospheric Retrievals
P Patil, M Kuusela, J Hobbs
SIAM/ASA Journal on Uncertainty Quantification 10 (3), 827-859, 2022
62022
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
52022
Introduction to unfolding in high energy physics
M Kuusela
Lecture at Advanced Scientific Computing Workshop, ETH Zurich (July 15, 2014 …, 2014
52014
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
5
Spatiotemporal local interpolation of global ocean heat transport using Argo floats: A debiased latent Gaussian process approach
B Park, M Kuusela, D Giglio, A Gray
The Annals of Applied Statistics 17 (2), 1491-1520, 2023
42023
Neural likelihood surfaces for spatial processes with computationally intensive or intractable likelihoods
J Walchessen, A Lenzi, M Kuusela
arXiv preprint arXiv:2305.04634, 2023
42023
The system can't perform the operation now. Try again later.
Articles 1–20