Laplacian Autoencoders for Learning Stochastic Representations. - Marco Miani; Frederik Warburg; Pablo Moreno-Muñoz; Nicki Skafte Detlefsen; Søren Hauberg (NeurIPS, 2022)

Bayesian Metric Learning for Uncertainty Quantification in Image Retrieval. - Frederik Warburg; Marco Miani; Silas Brack; Søren Hauberg (NeurIPS, 2023)

Laplacian Segmentation Networks: Improved Epistemic Uncertainty from Spatial Aleatoric Uncertainty. - Kilian Zepf; Selma Wanna; Marco Miani; Juston Moore; Jes Frellsen; Søren Hauberg; Aasa Feragen; Frederik Warburg (CoRR, 2023)

Laplacian Segmentation Networks Improve Epistemic Uncertainty Quantification. - Kilian Zepf; Selma Wanna; Marco Miani; Juston Moore; Jes Frellsen; Søren Hauberg; Frederik Warburg; Aasa Feragen (MICCAI (8), 2024)

Sketched Lanczos uncertainty score: a low-memory summary of the Fisher information. - Marco Miani; Lorenzo Beretta 001; Søren Hauberg (NeurIPS, 2024)

Reparameterization invariance in approximate Bayesian inference. - Hrittik Roy; Marco Miani; Carl Henrik Ek; Philipp Hennig; Marvin Pförtner; Lukas Tatzel; Søren Hauberg (NeurIPS, 2024)

Curious Explorer: A Provable Exploration Strategy in Policy Learning. - Marco Miani; Maurizio Parton; Marco Romito (IEEE Trans. Pattern Anal. Mach. Intell., 2024)

Bayes without Underfitting: Fully Correlated Deep Learning Posteriors via Alternating Projections. - Marco Miani; Hrittik Roy; Søren Hauberg (AISTATS, 2025)

Bayesian generative models can flag performance loss, bias, and out-of-distribution image content. - Miguel López-Pérez; Marco Miani; Valery Naranjo; Søren Hauberg; Aasa Feragen (CoRR, 2025)