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
153 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
131 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
77 2018 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
43 2012 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
41 2022 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
41 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
40 * 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
25 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
19 2009 Statistical issues in unfolding methods for high energy physics M Kuusela
16 2012 Shape-constrained uncertainty quantification in unfolding steeply falling elementary particle spectra M Kuusela, PB Stark
13 2017 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
8 2022 Uncertainty quantification in unfolding elementary particle spectra at the Large Hadron Collider MJ Kuusela
EPFL, 2016
8 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
8 2010 Objective Frequentist Uncertainty Quantification for Atmospheric Retrievals P Patil, M Kuusela, J Hobbs
SIAM/ASA Journal on Uncertainty Quantification 10 (3), 827-859, 2022
6 2022 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
5 2022 Introduction to unfolding in high energy physics M Kuusela
Lecture at Advanced Scientific Computing Workshop, ETH Zurich (July 15, 2014 …, 2014
5 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
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
4 2023 Neural likelihood surfaces for spatial processes with computationally intensive or intractable likelihoods J Walchessen, A Lenzi, M Kuusela
arXiv preprint arXiv:2305.04634, 2023
4 2023