| 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 | 206 | 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 | 134 | 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 | 89 | 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 | 82 | 2023 |
| 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 | 46* | 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 | 46 | 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 | 41 | 2012 |
| 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 | 33 | 2023 |
| Heat stored in the Earth system 1960–2020: where does the energy go?, Earth Syst. Sci. Data, 15, 1675–1709 K Von Schuckmann, A Minière, F Gues, FJ Cuesta-Valero, G Kirchengast, ... | 22 | 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 | 21 | 2009 |
| Statistical issues in unfolding methods for high energy physics M Kuusela | 17 | 2012 |
| Shape-constrained uncertainty quantification in unfolding steeply falling elementary particle spectra M Kuusela, PB Stark | 15 | 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 | 12 | 2022 |
| Uncertainty quantification in unfolding elementary particle spectra at the Large Hadron Collider MJ Kuusela EPFL, 2016 | 11 | 2016 |
| Neural likelihood surfaces for spatial processes with computationally intensive or intractable likelihoods J Walchessen, A Lenzi, M Kuusela Spatial Statistics 62, 100848, 2024 | 10 | 2024 |
| Objective Frequentist Uncertainty Quantification for Atmospheric Retrievals P Patil, M Kuusela, J Hobbs SIAM/ASA Journal on Uncertainty Quantification 10 (3), 827-859, 2022 | 9 | 2022 |
| 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 |
| Simulator-based inference with WALDO: Confidence regions by leveraging prediction algorithms and posterior estimators for inverse problems L Masserano, T Dorigo, R Izbicki, M Kuusela, AB Lee Proceedings of Machine Learning Research 206, 2023 | 7 | 2023 |
| Background modeling for double Higgs boson production: Density ratios and optimal transport T Manole, P Bryant, J Alison, M Kuusela, L Wasserman The Annals of Applied Statistics 18 (4), 2950-2978, 2024 | 6 | 2024 |
| 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 | 6 | 2023 |
