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 | 105 | 2010 |

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 | 29 | 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 | 24* | 2015 |

Locally stationary spatio-temporal interpolation of Argo profiling float data M Kuusela, ML Stein Proceedings of the Royal Society A 474 (2220), 20180400, 2018 | 22 | 2018 |

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 | 15 | 2009 |

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 | 13 | 2012 |

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 |

Shape-constrained uncertainty quantification in unfolding steeply falling elementary particle spectra M Kuusela, PB Stark The Annals of Applied Statistics 11 (3), 1671-1710, 2017 | 5 | 2017 |

Statistical issues in unfolding methods for high energy physics M Kuusela | 4 | 2012 |

Uncertainty quantification in unfolding elementary particle spectra at the Large Hadron Collider MJ Kuusela EPFL, 2016 | 3 | 2016 |

Introduction to unfolding in high energy physics M Kuusela Lecture at Advanced Scientific Computing Workshop, ETH Zurich (July 15, 2014 …, 2014 | 3 | 2014 |

Unfolding: A statistician’s perspective M Kuusela Conference Slides, Phystatν 9, 2016 | 2 | 2016 |

Soft classification of diffractive interactions at the LHC M Kuusela, E Malmi, R Orava, T Vatanen AIP Conference Proceedings 1350 (1), 111–114, 2011 | 2 | 2011 |

Objective frequentist uncertainty quantification for atmospheric CO retrievals P Patil, M Kuusela, J Hobbs arXiv preprint arXiv:2007.14975, 2020 | | 2020 |

Data Science for Modern Oceanography: Statistics, Machine Learning, Visualization, and More A Gray Ocean Sciences Meeting 2020, 2020 | | 2020 |

Statistics for Mapping Ocean Heat Content with Argo Floats: Modeling and Uncertainty Quantification M Kuusela Ocean Sciences Meeting 2020, 2020 | | 2020 |

Locally stationary spatio-temporal interpolation of Argo float data M Kuusela, M Stein 2018 Ocean Sciences Meeting, 2018 | | 2018 |

Shape-Constrained Uncertainty Quantification in Unfolding Elementary Particle Spectra at the Large Hadron Collider M Kuusela | | 2015 |

Fixed background EM algorithm for semi-supervised anomaly detection T Vatanen, M Kuusela, E Malmi, T Raiko, T Aaltonen, Y Nagai Aalto University, 2011 | | 2011 |

Algorithms for Variational Learning of Mixture of Gaussians M Kuusela | | 2008 |