Søren Hauberg

ORCID

Publication Activity (10 Years)

Years Active: 2008-2024
Publications (10 Years): 91

Top Venues

Publications

Miguel González Duque, Richard Michael, Simon Bartels, Yevgen Zainchkovskyy, Søren Hauberg, Wouter Boomsma
A survey and benchmark of high-dimensional Bayesian optimization of discrete sequences. CoRR (2024)
Hrittik Roy, Marco Miani, Carl Henrik Ek, Philipp Hennig, Marvin Pförtner, Lukas Tatzel, Søren Hauberg
Reparameterization invariance in approximate Bayesian inference. CoRR (2024)
Hadi Beik-Mohammadi, Søren Hauberg, Georgios Arvanitidis, Nadia Figueroa, Gerhard Neumann, Leonel Rozo
Neural Contractive Dynamical Systems. CoRR (2024)
Richard Michael, Simon Bartels, Miguel González Duque, Yevgen Zainchkovskyy, Jes Frellsen, Søren Hauberg, Wouter Boomsma
A Continuous Relaxation for Discrete Bayesian Optimization. CoRR (2024)
Cong Geng, Tian Han, Peng-Tao Jiang, Hao Zhang, Jinwei Chen, Søren Hauberg, Bo Li
Improving Adversarial Energy-Based Model via Diffusion Process. CoRR (2024)
Andreas Plesner, Hans Henrik Brandenborg Sørensen, Søren Hauberg
Accurate Computation of the Logarithm of Modified Bessel Functions on GPUs. ICS (2024)
Hadi Beik-Mohammadi, Søren Hauberg, Georgios Arvanitidis, Nadia Figueroa, Gerhard Neumann, Leonel Rozo
Neural Contractive Dynamical Systems. ICLR (2024)
Nicholas Krämer, Pablo Moreno-Muñoz, Hrittik Roy, Søren Hauberg
Gradients of Functions of Large Matrices. CoRR (2024)
Federico Bergamin, Pablo Moreno-Muñoz, Søren Hauberg, Georgios Arvanitidis
Riemannian Laplace approximations for Bayesian neural networks. NeurIPS (2023)
Hadi Beik-Mohammadi, Søren Hauberg, Georgios Arvanitidis, Gerhard Neumann, Leonel Rozo
Reactive motion generation on learned Riemannian manifolds. Int. J. Robotics Res. 42 (10) (2023)
Pablo Moreno-Muñoz, Pol Garcia Recasens, Søren Hauberg
On Masked Pre-training and the Marginal Likelihood. NeurIPS (2023)
Frederik Warburg, Marco Miani, Silas Brack, Søren Hauberg
Bayesian Metric Learning for Uncertainty Quantification in Image Retrieval. NeurIPS (2023)
Thoranna Bender, Simon Møe Sørensen, Alireza Kashani, Kristjan Eldjarn Hjorleifsson, Grethe Hyldig, Søren Hauberg, Serge J. Belongie, Frederik Warburg
Learning to Taste: A Multimodal Wine Dataset. NeurIPS (2023)
Frederik Warburg, Marco Miani, Silas Brack, Søren Hauberg
Bayesian Metric Learning for Uncertainty Quantification in Image Retrieval. CoRR (2023)
Thoranna Bender, Simon Møe Sørensen, Alireza Kashani, Kristjan Eldjarn Hjorleifsson, Grethe Hyldig, Søren Hauberg, Serge J. Belongie, Frederik Warburg
Learning to Taste: A Multimodal Wine Dataset. CoRR (2023)
Kilian Zepf, Selma Wanna, Marco Miani, Juston Moore, Jes Frellsen, Søren Hauberg, Aasa Feragen, Frederik Warburg
Laplacian Segmentation Networks: Improved Epistemic Uncertainty from Spatial Aleatoric Uncertainty. CoRR (2023)
Simon Bartels, Kristoffer Stensbo-Smidt, Pablo Moreno-Muñoz, Wouter Boomsma, Jes Frellsen, Søren Hauberg
Adaptive Cholesky Gaussian Processes. AISTATS (2023)
Johan Ziruo Ye, Thomas Ørkild, Peter Lempel Søndergaard, Søren Hauberg
Variational Point Encoding Deformation for Dental Modeling. CoRR (2023)
Federico Bergamin, Pablo Moreno-Muñoz, Søren Hauberg, Georgios Arvanitidis
Riemannian Laplace approximations for Bayesian neural networks. CoRR (2023)
Pablo Moreno-Muñoz, Pol G. Recasens, Søren Hauberg
On Masked Pre-training and the Marginal Likelihood. CoRR (2023)
Alison Pouplin, David Eklund, Carl Henrik Ek, Søren Hauberg
Identifying latent distances with Finslerian geometry. Trans. Mach. Learn. Res. 2023 (2023)
Marco Miani, Frederik Warburg, Pablo Moreno-Muñoz, Nicki Skafte Detlefsen, Søren Hauberg
Laplacian Autoencoders for Learning Stochastic Representations. NeurIPS (2022)
Hadi Beik Mohammadi, Søren Hauberg, Georgios Arvanitidis, Gerhard Neumann, Leonel Dario Rozo
Reactive Motion Generation on Learned Riemannian Manifolds. CoRR (2022)
Georgios Arvanitidis, Miguel González Duque, Alison Pouplin, Dimitrios Kalatzis, Søren Hauberg
Pulling back information geometry. AISTATS (2022)
Simon Bartels, Kristoffer Stensbo-Smidt, Pablo Moreno-Muñoz, Wouter Boomsma, Jes Frellsen, Søren Hauberg
Adaptive Cholesky Gaussian Processes. CoRR (2022)
Pierre Segonne, Yevgen Zainchkovskyy, Søren Hauberg
Robust uncertainty estimates with out-of-distribution pseudo-inputs training. CoRR (2022)
Miguel González Duque, Rasmus Berg Palm, Søren Hauberg, Sebastian Risi
Mario Plays on a Manifold: Generating Functional Content in Latent Space through Differential Geometry. CoRR (2022)
Adele Myers, Saiteja Utpala, Shubham Talbar, Sophia Sanborn, Christian Shewmake, Claire Donnat, Johan Mathe, Umberto Lupo, Rishi Sonthalia, Xinyue Cui, Tom Szwagier, Arthur Pignet, Andri Bergsson, Søren Hauberg, Dmitriy Nielsen, Stefan Sommer, David Klindt, Erik Hermansen, Melvin Vaupel, Benjamin Dunn, Jeffrey Xiong, Noga Aharony, Itsik Pe'er, Felix Ambellan, Martin Hanik, Esfandiar Nava Yazdani, Christoph von Tycowicz, Nina Miolane
ICLR 2022 Challenge for Computational Geometry and Topology: Design and Results. CoRR (2022)
Jakob D. Havtorn, Lasse Borgholt, Søren Hauberg, Jes Frellsen, Lars Maaløe
Benchmarking Generative Latent Variable Models for Speech. CoRR (2022)
Miguel González Duque, Rasmus Berg Palm, Søren Hauberg, Sebastian Risi
Mario Plays on a Manifold: Generating Functional Content in Latent Space through Differential Geometry. CoG (2022)
Pablo Moreno-Muñoz, Cilie W. Feldager, Søren Hauberg
Revisiting Active Sets for Gaussian Process Decoders. CoRR (2022)
Marco Miani, Frederik Warburg, Pablo Moreno-Muñoz, Nicki Skafte Detlefsen, Søren Hauberg
Laplacian Autoencoders for Learning Stochastic Representations. CoRR (2022)
Helene Hauschultz, Rasmus Berg Palm, Pablo Moreno-Muños, Nicki Skafte Detlefsen, Andrew Allan du Plessis, Søren Hauberg
Is an encoder within reach? CoRR (2022)
Andrea Vallone, Frederik Warburg, Hans Hansen, Søren Hauberg, Javier Civera
Danish Airs and Grounds: A Dataset for Aerial-to-Street-Level Place Recognition and Localization. CoRR (2022)
Yevgen Zainchkovskyy, Jesper Ferkinghoff-Borg, Anja Bennett, Thomas Egebjerg, Nikolai Lorenzen, Per Greisen Jr., Søren Hauberg, Carsten Stahlhut
Probabilistic thermal stability prediction through sparsity promoting transformer representation. CoRR (2022)
Federico Bergamin, Pierre-Alexandre Mattei, Jakob D. Havtorn, Hugo Senetaire, Hugo Schmutz, Lars Maaløe, Søren Hauberg, Jes Frellsen
Model-agnostic out-of-distribution detection using combined statistical tests. CoRR (2022)
Andrea Vallone, Frederik Warburg, Hans Hansen, Søren Hauberg, Javier Civera
Danish Airs and Grounds: A Dataset for Aerial-to-Street-Level Place Recognition and Localization. IEEE Robotics Autom. Lett. 7 (4) (2022)
Adele Myers, Saiteja Utpala, Shubham Talbar, Sophia Sanborn, Christian Shewmake, Claire Donnat, Johan Mathe, Rishi Sonthalia, Xinyue Cui, Tom Szwagier, Arthur Pignet, Andri Bergsson, Søren Hauberg, Dmitriy Nielsen, Stefan Sommer, David A. Klindt, Erik Hermansen, Melvin Vaupel, Benjamin A. Dunn, Jeffrey Xiong, Noga Aharony, Itsik Pe'er, Felix Ambellan, Martin Hanik, Esfandiar Nava-Yazdani, Christoph von Tycowicz, Nina Miolane
ICLR 2022 Challenge for Computational Geometry & Topology: Design and Results. TAG-ML (2022)
Federico Bergamin, Pierre-Alexandre Mattei, Jakob Drachmann Havtorn, Hugo Sénétaire, Hugo Schmutz, Lars Maaløe, Søren Hauberg, Jes Frellsen
Model-agnostic out-of-distribution detection using combined statistical tests. AISTATS (2022)
Pola Schwöbel, Frederik Rahbæk Warburg, Martin Jørgensen, Kristoffer Hougaard Madsen, Søren Hauberg
Probabilistic spatial transformer networks. UAI (2022)
Alison Pouplin, David Eklund, Carl Henrik Ek, Søren Hauberg
Identifying latent distances with Finslerian geometry. CoRR (2022)
Andri Bergsson, Søren Hauberg
Visualizing Riemannian data with Rie-SNE. CoRR (2022)
Pablo Moreno-Muñoz, Cilie W. Feldager, Søren Hauberg
Revisiting Active Sets for Gaussian Process Decoders. NeurIPS (2022)
Cong Geng, Jia Wang, Zhiyong Gao, Jes Frellsen, Søren Hauberg
Bounds all around: training energy-based models with bidirectional bounds. NeurIPS (2021)
Dimitris Kalatzis, Johan Ziruo Ye, Jesper Wohlert, Søren Hauberg
Multi-chart flows. CoRR (2021)
Jakob Drachmann Havtorn, Jes Frellsen, Søren Hauberg, Lars Maaløe
Hierarchical VAEs Know What They Don't Know. ICML (2021)
Martin Jørgensen, Søren Hauberg
Isometric Gaussian Process Latent Variable Model for Dissimilarity Data. ICML (2021)
Rudrasis Chakraborty, Liu Yang, Søren Hauberg, Baba C. Vemuri
Intrinsic Grassmann Averages for Online Linear, Robust and Nonlinear Subspace Learning. IEEE Trans. Pattern Anal. Mach. Intell. 43 (11) (2021)
Hadi Beik Mohammadi, Søren Hauberg, Georgios Arvanitidis, Gerhard Neumann, Leonel Dario Rozo
Learning Riemannian Manifolds for Geodesic Motion Skills. Robotics: Science and Systems (2021)
Georgios Arvanitidis, Søren Hauberg, Bernhard Schölkopf
Geometrically Enriched Latent Spaces. AISTATS (2021)
Cilie W. Feldager, Søren Hauberg, Lars Kai Hansen
Spontaneous Symmetry Breaking in Data Visualization. ICANN (2) (2021)
Cong Geng, Jia Wang, Zhiyong Gao, Jes Frellsen, Søren Hauberg
Bounds all around: training energy-based models with bidirectional bounds. CoRR (2021)
Hadi Beik Mohammadi, Søren Hauberg, Georgios Arvanitidis, Gerhard Neumann, Leonel Dario Rozo
Learning Riemannian Manifolds for Geodesic Motion Skills. CoRR (2021)
Jakob D. Havtorn, Jes Frellsen, Søren Hauberg, Lars Maaløe
Hierarchical VAEs Know What They Don't Know. CoRR (2021)
Georgios Arvanitidis, Miguel González Duque, Alison Pouplin, Dimitris Kalatzis, Søren Hauberg
Pulling back information geometry. CoRR (2021)
Frederik Warburg, Martin Jørgensen, Javier Civera, Søren Hauberg
Bayesian Triplet Loss: Uncertainty Quantification in Image Retrieval. ICCV (2021)
Jeppe Thagaard, Søren Hauberg, Bert van der Vegt, Thomas Ebstrup, Johan D. Hansen, Anders B. Dahl
Can You Trust Predictive Uncertainty Under Real Dataset Shifts in Digital Pathology? MICCAI (1) (2020)
Martin Jørgensen, Søren Hauberg
Isometric Gaussian Process Latent Variable Model for Dissimilarity Data. CoRR (2020)
Alfredo Buttari, Søren Hauberg, Costy Kodsi
Factorization of Block-Tridiagonal Matrices. SIAM J. Sci. Comput. 42 (6) (2020)
Frederik Warburg, Søren Hauberg, Manuel López-Antequera, Pau Gargallo, Yubin Kuang, Javier Civera
Mapillary Street-Level Sequences: A Dataset for Lifelong Place Recognition. CVPR (2020)
Dimitrios Kalatzis, David Eklund, Georgios Arvanitidis, Søren Hauberg
Variational Autoencoders with Riemannian Brownian Motion Priors. ICML (2020)
Frederik Warburg, Martin Jørgensen, Javier Civera, Søren Hauberg
Bayesian Triplet Loss: Uncertainty Quantification in Image Retrieval. CoRR (2020)
Dimitris Kalatzis, David Eklund, Georgios Arvanitidis, Søren Hauberg
Variational Autoencoders with Riemannian Brownian Motion Priors. CoRR (2020)
Pola Schwöbel, Frederik Warburg, Martin Jørgensen, Kristoffer H. Madsen, Søren Hauberg
Probabilistic Spatial Transformers for Bayesian Data Augmentation. CoRR (2020)
Martin Jørgensen, Søren Hauberg
Reparametrization Invariance in non-parametric Causal Discovery. CoRR (2020)
Nicki Skafte Detlefsen, Søren Hauberg, Wouter Boomsma
What is a meaningful representation of protein sequences? CoRR (2020)
Georgios Arvanitidis, Søren Hauberg, Bernhard Schölkopf
Geometrically Enriched Latent Spaces. CoRR (2020)
Georgios Arvanitidis, Søren Hauberg, Philipp Hennig, Michael Schober
Fast and Robust Shortest Paths on Manifolds Learned from Data. CoRR (2019)
Georgios Arvanitidis, Søren Hauberg, Philipp Hennig, Michael Schober
Fast and Robust Shortest Paths on Manifolds Learned from Data. AISTATS (2019)
Nicki Skafte Detlefsen, Søren Hauberg
Explicit Disentanglement of Appearance and Perspective in Generative Models. NeurIPS (2019)
David Eklund, Søren Hauberg
Expected path length on random manifolds. CoRR (2019)
Anton Mallasto, Søren Hauberg, Aasa Feragen
Probabilistic Riemannian submanifold learning with wrapped Gaussian process latent variable models. AISTATS (2019)
Nicki Skafte Detlefsen, Søren Hauberg
Explicit Disentanglement of Appearance and Perspective in Generative Models. CoRR (2019)
Nicki Skafte Detlefsen, Martin Jørgensen, Søren Hauberg
Reliable training and estimation of variance networks. NeurIPS (2019)
Nicki Skafte Detlefsen, Martin Jørgensen, Søren Hauberg
Reliable training and estimation of variance networks. CoRR (2019)
Tao Yang, Georgios Arvanitidis, Dongmei Fu, Xiaogang Li, Søren Hauberg
Geodesic Clustering in Deep Generative Models. CoRR (2018)
Søren Hauberg
Directional Statistics with the Spherical Normal Distribution. FUSION (2018)
Søren Hauberg
Only Bayes should learn a manifold (on the estimation of differential geometric structure from data). CoRR (2018)
Anton Mallasto, Søren Hauberg, Aasa Feragen
Probabilistic Riemannian submanifold learning with wrapped Gaussian process latent variable models. CoRR (2018)
Nicki Skafte Detlefsen, Oren Freifeld, Søren Hauberg
Deep Diffeomorphic Transformer Networks. CVPR (2018)
Georgios Arvanitidis, Lars Kai Hansen, Søren Hauberg
Latent Space Oddity: on the Curvature of Deep Generative Models. ICLR (Poster) (2018)
Oren Freifeld, Søren Hauberg, Kayhan Batmanghelich, Jonn W. Fisher
Transformations Based on Continuous Piecewise-Affine Velocity Fields. IEEE Trans. Pattern Anal. Mach. Intell. 39 (12) (2017)
Rudrasis Chakraborty, Søren Hauberg, Baba C. Vemuri
Intrinsic Grassmann Averages for Online Linear and Robust Subspace Learning. CoRR (2017)
Rudrasis Chakraborty, Søren Hauberg, Baba C. Vemuri
Intrinsic Grassmann Averages for Online Linear and Robust Subspace Learning. CVPR (2017)
Georgios Arvanitidis, Lars Kai Hansen, Søren Hauberg
Maximum Likelihood Estimation of Riemannian Metrics from Euclidean Data. GSI (2017)
Søren Hauberg
Principal Curves on Riemannian Manifolds. IEEE Trans. Pattern Anal. Mach. Intell. 38 (9) (2016)
Georgios Arvanitidis, Lars Kai Hansen, Søren Hauberg
A Locally Adaptive Normal Distribution. NIPS (2016)
Aasa Feragen, Søren Hauberg
Open Problem: Kernel methods on manifolds and metric spaces. What is the probability of a positive definite geodesic exponential kernel? COLT (2016)
Søren Hauberg, Oren Freifeld, Anders Boesen Lindbo Larsen, John W. Fisher III, Lars Kai Hansen
Dreaming More Data: Class-dependent Distributions over Diffeomorphisms for Learned Data Augmentation. AISTATS (2016)
Søren Hauberg, Aasa Feragen, Raffi Enficiaud, Michael J. Black
Scalable Robust Principal Component Analysis Using Grassmann Averages. IEEE Trans. Pattern Anal. Mach. Intell. 38 (11) (2016)
Sofie Therese Hansen, Søren Hauberg, Lars Kai Hansen
Data-driven forward model inference for EEG brain imaging. NeuroImage 139 (2016)
Aasa Feragen, François Lauze, Søren Hauberg
Geodesic exponential kernels: When curvature and linearity conflict. CVPR (2015)
Søren Hauberg, Oren Freifeld, Anders Boesen Lindbo Larsen, John W. Fisher III, Lars Kai Hansen
Dreaming More Data: Class-dependent Distributions over Diffeomorphisms for Learned Data Augmentation. CoRR (2015)
Søren Hauberg, Michael Schober, Matthew G. Liptrot, Philipp Hennig, Aasa Feragen
A Random Riemannian Metric for Probabilistic Shortest-Path Tractography. MICCAI (1) (2015)
Oren Freifeld, Søren Hauberg, Kayhan N. Batmanghelich, John W. Fisher III
Highly-Expressive Spaces of Well-Behaved Transformations: Keeping it Simple. ICCV (2015)
Søren Hauberg, Aasa Feragen, Michael J. Black
Grassmann Averages for Scalable Robust PCA. CVPR (2014)
Michael Schober, Niklas Kasenburg, Aasa Feragen, Philipp Hennig, Søren Hauberg
Probabilistic Shortest Path Tractography in DTI Using Gaussian Process ODE Solvers. MICCAI (3) (2014)
Alessandra Tosi, Søren Hauberg, Alfredo Vellido, Neil D. Lawrence
Metrics for Probabilistic Geometries. CoRR (2014)
Aasa Feragen, François Lauze, Søren Hauberg
Geodesic Exponential Kernels: When Curvature and Linearity Conflict. CoRR (2014)
Alessandra Tosi, Søren Hauberg, Alfredo Vellido, Neil D. Lawrence
Metrics for Probabilistic Geometries. UAI (2014)

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Publication Activity (10 Years)

Top Topics

Top Venues

Søren Hauberg