An Efficient Algorithm for Modelling Duration in Hidden Markov Models, with a Dramatic Application. - Søren Hauberg; Jakob Sloth (J. Math. Imaging Vis., 2008)

Brownian Warps for Non-Rigid Registration. - Mads Nielsen; Peter Johansen; Andrew D. Jackson; Benny Lautrup; Søren Hauberg (J. Math. Imaging Vis., 2008)

Three Dimensional Monocular Human Motion Analysis in End-Effector Space. - Søren Hauberg; Jérôe Lapuyade; Morten Engell-Nørregård; Kenny Erleben; Kim Steenstrup Pedersen (EMMCVPR, 2009)

Interactive Inverse Kinematics for Human Motion Estimation. - Morten Engell-Nørregård; Søren Hauberg; Jérôe Lapuyade; Kenny Erleben; Kim Steenstrup Pedersen (VRIPHYS, 2009)

Stick It! Articulated Tracking Using Spatial Rigid Object Priors. - Søren Hauberg; Kim Steenstrup Pedersen (ACCV (3), 2010)

GPU Accelerated Likelihoods for Stereo-Based Articulated Tracking. - Rune Møllegaard Friborg; Søren Hauberg; Kenny Erleben (ECCV Workshops (1), 2010)

Gaussian-Like Spatial Priors for Articulated Tracking. - Søren Hauberg; Stefan Sommer; Kim Steenstrup Pedersen (ECCV (1), 2010)

Manifold Valued Statistics, Exact Principal Geodesic Analysis and the Effect of Linear Approximations. - Stefan Sommer; François Lauze; Søren Hauberg; Mads Nielsen (ECCV (6), 2010)

Data-Driven Importance Distributions for Articulated Tracking. - Søren Hauberg; Kim Steenstrup Pedersen (EMMCVPR, 2011)

An Empirical Study on the Performance of Spectral Manifold Learning Techniques. - Peter Mysling; Søren Hauberg; Kim Steenstrup Pedersen (ICANN (1), 2011)

Means in spaces of tree-like shapes. - Aasa Feragen; Søren Hauberg; Mads Nielsen; François Lauze (ICCV, 2011)

Unscented Kalman Filtering for Articulated Human Tracking. - Anders Boesen Lindbo Larsen; Søren Hauberg; Kim Steenstrup Pedersen (SCIA, 2011)

Predicting Articulated Human Motion from Spatial Processes. - Søren Hauberg; Kim Steenstrup Pedersen (Int. J. Comput. Vis., 2011)

Spatial Measures between Human Poses for Classification and Understanding. - Søren Hauberg; Kim Steenstrup Pedersen (AMDO, 2012)

A Geometric take on Metric Learning. - Søren Hauberg; Oren Freifeld; Michael J. Black (NIPS, 2012)

Natural metrics and least-committed priors for articulated tracking. - Søren Hauberg; Stefan Sommer; Kim Steenstrup Pedersen (Image Vis. Comput., 2012)

Probabilistic Numerical Analysis in Riemannian Statistics. - Philipp Hennig; Søren Hauberg (CoRR, 2013)

Unscented Kalman Filtering on Riemannian Manifolds. - Søren Hauberg; François Lauze; Kim Steenstrup Pedersen (J. Math. Imaging Vis., 2013)

Probabilistic Solutions to Differential Equations and their Application to Riemannian Statistics. - Philipp Hennig; Søren Hauberg (AISTATS, 2014)

Model Transport: Towards Scalable Transfer Learning on Manifolds. - Oren Freifeld; Søren Hauberg; Michael J. Black (CVPR, 2014)

Grassmann Averages for Scalable Robust PCA. - Søren Hauberg; Aasa Feragen; Michael J. Black (CVPR, 2014)

Probabilistic Shortest Path Tractography in DTI Using Gaussian Process ODE Solvers. - Michael Schober; Niklas Kasenburg; Aasa Feragen; Philipp Hennig; Søren Hauberg (MICCAI (3), 2014)

Metrics for Probabilistic Geometries. - Alessandra Tosi; Søren Hauberg; Alfredo Vellido; Neil D. Lawrence (UAI, 2014)

Geodesic exponential kernels: When curvature and linearity conflict. - Aasa Feragen; François Lauze; Søren Hauberg (CVPR, 2015)

Highly-Expressive Spaces of Well-Behaved Transformations: Keeping it Simple. - Oren Freifeld; Søren Hauberg; Kayhan N. Batmanghelich; John W. Fisher III (ICCV, 2015)

A Random Riemannian Metric for Probabilistic Shortest-Path Tractography. - Søren Hauberg; Michael Schober; Matthew G. Liptrot; Philipp Hennig; Aasa Feragen (MICCAI (1), 2015)

Dreaming More Data: Class-dependent Distributions over Diffeomorphisms for Learned Data Augmentation. - Søren Hauberg; Oren Freifeld; Anders Boesen Lindbo Larsen; John W. Fisher III; Lars Kai Hansen (AISTATS, 2016)

Open Problem: Kernel methods on manifolds and metric spaces. What is the probability of a positive definite geodesic exponential kernel? - Aasa Feragen; Søren Hauberg (COLT, 2016)

A Locally Adaptive Normal Distribution. - Georgios Arvanitidis; Lars Kai Hansen; Søren Hauberg (NIPS, 2016)

Data-driven forward model inference for EEG brain imaging. - Sofie Therese Hansen; Søren Hauberg; Lars Kai Hansen (NeuroImage, 2016)

Principal Curves on Riemannian Manifolds. - Søren Hauberg (IEEE Trans. Pattern Anal. Mach. Intell., 2016)

Scalable Robust Principal Component Analysis Using Grassmann Averages. - Søren Hauberg; Aasa Feragen; Raffi Enficiaud; Michael J. Black (IEEE Trans. Pattern Anal. Mach. Intell., 2016)

Intrinsic Grassmann Averages for Online Linear and Robust Subspace Learning. - Rudrasis Chakraborty; Søren Hauberg; Baba C. Vemuri (CVPR, 2017)

Maximum Likelihood Estimation of Riemannian Metrics from Euclidean Data. - Georgios Arvanitidis; Lars Kai Hansen; Søren Hauberg (GSI, 2017)

Transformations Based on Continuous Piecewise-Affine Velocity Fields. - Oren Freifeld; Søren Hauberg; Kayhan Batmanghelich; John W. Fisher III (IEEE Trans. Pattern Anal. Mach. Intell., 2017)

Deep Diffeomorphic Transformer Networks. - Nicki Skafte Detlefsen; Oren Freifeld; Søren Hauberg (CVPR, 2018)

Directional Statistics with the Spherical Normal Distribution. - Søren Hauberg (FUSION, 2018)

Latent Space Oddity: on the Curvature of Deep Generative Models. - Georgios Arvanitidis; Lars Kai Hansen; Søren Hauberg (ICLR (Poster), 2018)

Only Bayes should learn a manifold (on the estimation of differential geometric structure from data). - Søren Hauberg (CoRR, 2018)

Geodesic Clustering in Deep Generative Models. - Tao Yang 010; Georgios Arvanitidis; Dongmei Fu; Xiaogang Li 002; Søren Hauberg (CoRR, 2018)

Fast and Robust Shortest Paths on Manifolds Learned from Data. - Georgios Arvanitidis; Søren Hauberg; Philipp Hennig; Michael Schober (AISTATS, 2019)

Probabilistic Riemannian submanifold learning with wrapped Gaussian process latent variable models. - Anton Mallasto; Søren Hauberg; Aasa Feragen (AISTATS, 2019)

Explicit Disentanglement of Appearance and Perspective in Generative Models. - Nicki Skafte Detlefsen; Søren Hauberg (NeurIPS, 2019)

Reliable training and estimation of variance networks. - Nicki Skafte Detlefsen; Martin Jørgensen; Søren Hauberg (NeurIPS, 2019)

Expected path length on random manifolds. - David Eklund; Søren Hauberg (CoRR, 2019)

Mapillary Street-Level Sequences: A Dataset for Lifelong Place Recognition. - Frederik Warburg; Søren Hauberg; Manuel López-Antequera; Pau Gargallo; Yubin Kuang; Javier Civera 001 (CVPR, 2020)

Variational Autoencoders with Riemannian Brownian Motion Priors. - Dimitrios Kalatzis; David Eklund; Georgios Arvanitidis; Søren Hauberg (ICML, 2020)

Can You Trust Predictive Uncertainty Under Real Dataset Shifts in Digital Pathology? - Jeppe Thagaard; Søren Hauberg; Bert van der Vegt; Thomas Ebstrup; Johan D. Hansen; Anders B. Dahl (MICCAI (1), 2020)

Probabilistic Spatial Transformers for Bayesian Data Augmentation. - Pola Schwöbel; Frederik Warburg; Martin Jørgensen; Kristoffer H. Madsen; Søren Hauberg (CoRR, 2020)

Reparametrization Invariance in non-parametric Causal Discovery. - Martin Jørgensen; Søren Hauberg (CoRR, 2020)

What is a meaningful representation of protein sequences? - Nicki Skafte Detlefsen; Søren Hauberg; Wouter Boomsma (CoRR, 2020)

Parallel QR Factorization of Block-Tridiagonal Matrices. - Alfredo Buttari; Søren Hauberg; Costy Kodsi (SIAM J. Sci. Comput., 2020)

Geometrically Enriched Latent Spaces. - Georgios Arvanitidis; Søren Hauberg; Bernhard Schölkopf (AISTATS, 2021)

Spontaneous Symmetry Breaking in Data Visualization. - Cilie W. Feldager; Søren Hauberg; Lars Kai Hansen (ICANN (2), 2021)

Bayesian Triplet Loss: Uncertainty Quantification in Image Retrieval. - Frederik Warburg; Martin Jørgensen; Javier Civera 001; Søren Hauberg (ICCV, 2021)

Hierarchical VAEs Know What They Don't Know. - Jakob Drachmann Havtorn; Jes Frellsen; Søren Hauberg; Lars Maaløe (ICML, 2021)

Isometric Gaussian Process Latent Variable Model for Dissimilarity Data. - Martin Jørgensen; Søren Hauberg (ICML, 2021)

Bounds all around: training energy-based models with bidirectional bounds. - Cong Geng; Jia Wang; Zhiyong Gao; Jes Frellsen; Søren Hauberg (NeurIPS, 2021)

Learning Riemannian Manifolds for Geodesic Motion Skills. - Hadi Beik-Mohammadi; Søren Hauberg; Georgios Arvanitidis; Gerhard Neumann; Leonel Dario Rozo (Robotics: Science and Systems, 2021)

Multi-chart flows. - Dimitris Kalatzis; Johan Ziruo Ye; Jesper Wohlert; Søren Hauberg (CoRR, 2021)

Intrinsic Grassmann Averages for Online Linear, Robust and Nonlinear Subspace Learning. - Rudrasis Chakraborty; Liu Yang; Søren Hauberg; Baba C. Vemuri (IEEE Trans. Pattern Anal. Mach. Intell., 2021)

Pulling back information geometry. - Georgios Arvanitidis; Miguel González Duque; Alison Pouplin; Dimitrios Kalatzis; Søren Hauberg (AISTATS, 2022)

Model-agnostic out-of-distribution detection using combined statistical tests. - Federico Bergamin; Pierre-Alexandre Mattei; Jakob Drachmann Havtorn; Hugo Sénétaire; Hugo Schmutz; Lars Maaløe; Søren Hauberg; Jes Frellsen (AISTATS, 2022)

Mario Plays on a Manifold: Generating Functional Content in Latent Space through Differential Geometry. - Miguel González Duque; Rasmus Berg Palm; Søren Hauberg; Sebastian Risi (CoG, 2022)

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

Revisiting Active Sets for Gaussian Process Decoders. - Pablo Moreno-Muñoz; Cilie W. Feldager; Søren Hauberg (NeurIPS, 2022)

ICLR 2022 Challenge for Computational Geometry & Topology: Design and Results. - Adele Myers; Saiteja Utpala; Shubham Talbar; Sophia Sanborn; Christian Shewmake; Claire Donnat; Johan Mathe; Rishi Sonthalia; Xinyue Cui; Tom Szwagier; Arthur Pignet; Andri Bergsson; et al. (TAG-ML, 2022)

Probabilistic spatial transformer networks. - Pola Schwöbel; Frederik Rahbæk Warburg; Martin Jørgensen; Kristoffer Hougaard Madsen; Søren Hauberg (UAI, 2022)

Robust uncertainty estimates with out-of-distribution pseudo-inputs training. - Pierre Segonne; Yevgen Zainchkovskyy; Søren Hauberg (CoRR, 2022)

Benchmarking Generative Latent Variable Models for Speech. - Jakob D. Havtorn; Lasse Borgholt; Søren Hauberg; Jes Frellsen; Lars Maaløe (CoRR, 2022)

Is an encoder within reach? - Helene Hauschultz; Rasmus Berg Palm; Pablo Moreno-Muños; Nicki Skafte Detlefsen; Andrew Allan du Plessis; Søren Hauberg (CoRR, 2022)

ICLR 2022 Challenge for Computational Geometry and Topology: Design and Results. - Adele Myers; Saiteja Utpala; Shubham Talbar; Sophia Sanborn; Christian Shewmake; Claire Donnat; Johan Mathe; Umberto Lupo; Rishi Sonthalia; Xinyue Cui; Tom Szwagier; Arthur Pignet; et al. (CoRR, 2022)

Probabilistic thermal stability prediction through sparsity promoting transformer representation. - Yevgen Zainchkovskyy; Jesper Ferkinghoff-Borg; Anja Bennett; Thomas Egebjerg; Nikolai Lorenzen; Per Greisen Jr.; Søren Hauberg; Carsten Stahlhut (CoRR, 2022)

Danish Airs and Grounds: A Dataset for Aerial-to-Street-Level Place Recognition and Localization. - Andrea Vallone; Frederik Warburg; Hans Hansen; Søren Hauberg; Javier Civera 001 (IEEE Robotics Autom. Lett., 2022)

Adaptive Cholesky Gaussian Processes. - Simon Bartels; Kristoffer Stensbo-Smidt; Pablo Moreno-Muñoz; Wouter Boomsma; Jes Frellsen; Søren Hauberg (AISTATS, 2023)

Learning to Taste: A Multimodal Wine Dataset. - Thoranna Bender; Simon Møe Sørensen; Alireza Kashani; Kristjan Eldjarn Hjorleifsson; Grethe Hyldig; Søren Hauberg; Serge J. Belongie; Frederik Warburg (NeurIPS, 2023)

Riemannian Laplace approximations for Bayesian neural networks. - Federico Bergamin; Pablo Moreno-Muñoz; Søren Hauberg; Georgios Arvanitidis (NeurIPS, 2023)

On Masked Pre-training and the Marginal Likelihood. - Pablo Moreno-Muñoz; Pol Garcia Recasens; Søren Hauberg (NeurIPS, 2023)

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)

Variational Point Encoding Deformation for Dental Modeling. - Johan Ziruo Ye; Thomas Ørkild; Peter Lempel Søndergaard; Søren Hauberg (CoRR, 2023)

Identifying latent distances with Finslerian geometry. - Alison Pouplin; David Eklund; Carl Henrik Ek; Søren Hauberg (Trans. Mach. Learn. Res., 2023)

Reactive motion generation on learned Riemannian manifolds. - Hadi Beik-Mohammadi; Søren Hauberg; Georgios Arvanitidis; Gerhard Neumann; Leonel Rozo (Int. J. Robotics Res., 2023)

Neural Contractive Dynamical Systems. - Hadi Beik-Mohammadi; Søren Hauberg; Georgios Arvanitidis; Nadia Figueroa; Gerhard Neumann; Leonel Rozo (ICLR, 2024)

Improving Adversarial Energy-Based Model via Diffusion Process. - Cong Geng; Tian Han 001; Peng-Tao Jiang; Hao Zhang 063; Jinwei Chen; Søren Hauberg; Bo Li 115 (ICML, 2024)

Accurate Computation of the Logarithm of Modified Bessel Functions on GPUs. - Andreas Plesner; Hans Henrik Brandenborg Sørensen; Søren Hauberg (ICS, 2024)

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)

Visualizing Riemannian data with Rie-SNE. - Andri Bergsson; Søren Hauberg (MLVis@EuroVis, 2024)

A survey and benchmark of high-dimensional Bayesian optimization of discrete sequences. - Miguel González Duque; Richard Michael; Simon Bartels; Yevgen Zainchkovskyy; Søren Hauberg; Wouter Boomsma (NeurIPS, 2024)

Gradients of Functions of Large Matrices. - Nicholas Krämer; Pablo Moreno-Muñoz; Hrittik Roy; Søren Hauberg (NeurIPS, 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)

A Continuous Relaxation for Discrete Bayesian Optimization. - Richard Michael; Simon Bartels; Miguel González Duque; Yevgen Zainchkovskyy; Jes Frellsen; Søren Hauberg; Wouter Boomsma (CoRR, 2024)

Decoder ensembling for learned latent geometries. - Stas Syrota; Pablo Moreno-Muñoz; Søren Hauberg (CoRR, 2024)

Extended Neural Contractive Dynamical Systems: On Multiple Tasks and Riemannian Safety Regions. - Hadi Beik-Mohammadi; Søren Hauberg; Georgios Arvanitidis; Gerhard Neumann; Leonel Rozo (CoRR, 2024)

Variational Autoencoding of Dental Point Clouds. - Johan Ziruo Ye; Thomas Ørkild; Peter Lempel Søndergard; Søren Hauberg (Trans. Mach. Learn. Res., 2024)

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

Riemann2: Learning Riemannian Submanifolds from Riemannian Data. - Leonel Rozo; Miguel González Duque; Noémie Jaquier; Søren Hauberg (AISTATS, 2025)

Are Generative Models Fair? A Study of Racial Bias in Dermatological Image Generation. - Miguel López-Pérez; Søren Hauberg; Aasa Feragen (SCIA (2), 2025)

Identifying metric structures of deep latent variable models. - Stas Syrota; Yevgen Zainchkovskyy; Johnny Xi; Benjamin Bloem-Reddy; Søren Hauberg (CoRR, 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)

Spacetime Geometry of Denoising in Diffusion Models. - Rafal Karczewski; Markus Heinonen; Alison Pouplin; Søren Hauberg; Vikas Garg 001 (CoRR, 2025)

Learning geometry and topology via multi-chart flows. - Hanlin Yu; Søren Hauberg; Marcelo Hartmann; Arto Klami; Georgios Arvanitidis (CoRR, 2025)

Connecting Neural Models Latent Geometries with Relative Geodesic Representations. - Hanlin Yu; Berfin Inal; Georgios Arvanitidis; Søren Hauberg; Francesco Locatello; Marco Fumero (CoRR, 2025)

Exploring bidirectional bounds for minimax-training of Energy-based models. - Cong Geng; Jia Wang; Li Chen; Zhiyong Gao; Jes Frellsen; Søren Hauberg (CoRR, 2025)

Geometric Contact Flows: Contactomorphisms for Dynamics and Control. - Andrea Testa; Søren Hauberg; Tamim Asfour; Leonel Rozo (CoRR, 2025)

Riemannian generative decoder. - Andreas Bjerregaard; Søren Hauberg; Anders Krogh (CoRR, 2025)