A gradient-based algorithm competitive with variational Bayesian EM for mixture of Gaussians. - Mikael Kuusela; Tapani Raiko; Antti Honkela; Juha Karhunen (IJCNN, 2009)

Approximate Riemannian Conjugate Gradient Learning for Fixed-Form Variational Bayes. - Antti Honkela; Tapani Raiko; Mikael Kuusela; Matti Tornio; Juha Karhunen (J. Mach. Learn. Res., 2010)

Semi-supervised detection of collective anomalies with an application in high energy particle physics. - Tommi Vatanen; Mikael Kuusela; Eric Malmi; Tapani Raiko; Timo Aaltonen; Yoshikazu Nagai (IJCNN, 2012)

Simulation-Based Inference with WALDO: Perfectly Calibrated Confidence Regions Using Any Prediction or Posterior Estimation Algorithm. - Luca Masserano; Tommaso Dorigo; Rafael Izbicki; Mikael Kuusela; Ann B. Lee (CoRR, 2022)

Objective Frequentist Uncertainty Quantification for Atmospheric ( mathrm{CO}_2 ) Retrievals. - Pratik Patil; Mikael Kuusela; Jonathan Hobbs (SIAM/ASA J. Uncertain. Quantification, 2022)

Simulator-Based Inference with WALDO: Confidence Regions by Leveraging Prediction Algorithms and Posterior Estimators for Inverse Problems. - Luca Masserano; Tommaso Dorigo; Rafael Izbicki; Mikael Kuusela; Ann B. Lee (AISTATS, 2023)

Quantification of Aquarius, SMAP, SMOS and Argo-Based Gridded Sea Surface Salinity Product Sampling Errors. - Severine Fournier; Frederick M. Bingham; Cristina González-Haro; Akiko Hayashi; Karly M. Ulfsax Carlin; Susannah K. Brodnitz; Verónica González-Gambau; Mikael Kuusela (Remote. Sens., 2023)

Toward Model-Agnostic Detection of New Physics Using Data-Driven Signal Regions. - Soheun Yi; John Alison; Mikael Kuusela (CoRR, 2024)