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 | 169 | 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 | 132 | 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 | 80 | 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 | 50 | 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 | 43 | 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 |

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 | 27 | 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 |

Objective Frequentist Uncertainty Quantification for Atmospheric Retrievals P Patil, M Kuusela, J Hobbs SIAM/ASA Journal on Uncertainty Quantification 10 (3), 827-859, 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 |

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 | 6 | 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 | 5 | 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 | 5 | 2023 |

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 | |