Søren Hauberg @ CogSys, DTU Compute

2024

Probabilistic Multiple Sequence Alignment using Spatial Transformations
Sebastián García López, Søren Hauberg, Wouter Boomsma.
Preprint, 2024.
PDF BioRxiv

@article {sebastian:align:2024,
	author = {Garc{\'\i}a L{\'o}pez, Sebasti{\'a}n and Hauberg, S{\o}ren and Boomsma, Wouter},
	title = {Probabilistic Multiple Sequence Alignment using Spatial Transformations},
	elocation-id = {2024.10.12.617969},
	year = {2024},
	doi = {10.1101/2024.10.12.617969},
	publisher = {Cold Spring Harbor Laboratory},
	eprint = {https://www.biorxiv.org/content/early/2024/10/15/2024.10.12.617969.full.pdf},
	journal = {bioRxiv}
}

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

@inproceedings{hadi:cncds:2024,
  title = {Extended Neural Contractive Dynamical Systems: On Multiple Tasks and Riemannian Safety Regions},
  author = {Hadi Beik-Mohammadi and S{\o}ren Hauberg and Georgios Arvanitidis and Gerhard Neumann and Leonel Rozo},
  booktitle = {Preprint},
  year = {2024}
}

A Continuous Relaxation for Discrete Bayesian Optimization
Richard Michael, Simon Bartels, Miguel González-Duque, Yevgen Zainchkovskyy, Jes Frellsen, Søren Hauberg and Wouter Boomsma.
arXiv preprint, 2024.
PDF arXiv

@inproceedings{michael:arxiv:2024,
  title = {A Continuous Relaxation for Discrete Bayesian Optimization},
  author = {Richard Michael and Simon Bartels and Miguel González-Duque and Yevgen Zainchkovskyy and Jes Frellsen and S{\o}ren Hauberg and Wouter Boomsma},
  booktitle = {arXiv preprint},
  year = {2024},
}

Variational Autoencoding of Dental Point Clouds
Johan Ziruo Ye, Thomas Ørkild, Peter Lempel Søndergaard and Søren Hauberg.
Transactions on Machine Learning Research (TMLR) 2024
PDF SPIGM abstract Code

@article{johan:dental:2024,
  title = {Variational Autoencoding of Dental Point Clouds},
  author = {Johan Ziruo Ye and Thomas {\O}rkild and Peter Lempel S{\o}ndergaard and S{\o}ren Hauberg},
  journal = {Transactions on Machine Learning Research (TMLR)},
  year = {2024},
}

Decoder ensembling for learned latent geometries
Stas Syrota, Pablo Moreno-Muñoz and Søren Hauberg.
In ICML Workshop on Geometry-grounded Representation Learning and Generative Modeling, 2024.
PDF

@inproceedings{syrota:gram:2024,
  title = {Decoder ensembling for learned latent geometries},
  author = {Stas Syrota and Pablo Moreno-Muñoz and Søren Hauberg},
  booktitle = {ICML Workshop on Geometry-grounded Representation Learning and Generative Modeling},
  year = {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.
Neural Information Processing Systems (NeurIPS), 2024.
PDF ArXiv Benchmark

@inproceedings{miguel:hdbo:2024,
  title = {A survey and benchmark of high-dimensional Bayesian optimization of discrete sequences},
  author = {Miguel González-Duque and Richard Michael and Simon Bartels and Yevgen Zainchkovskyy and S{\o}ren Hauberg and Wouter Boomsma},
  booktitle = {Neural Information Processing Systems (NeurIPS)},
  year = {2024},
}

Improving Adversarial Energy-Based Model via Diffusion Process
Cong Geng, Tian Han, Peng-Tao Jiang, Hao Zhang, Jinwei Chen, Søren Hauberg and Bo Li.
In International Conference on Machine Learning (ICML), 2024.
PDF arXiv

@inproceedings{conggeng:icml:2024,
  author = {Cong Geng and Tian Han and Peng-Tao Jiang and Hao Zhang and Jinwei Chen and S{\o}ren Hauberg and Bo Li},
  title = {Improving Adversarial Energy-Based Model via Diffusion Process},
  booktitle = {Proceedings of the 41st International Conference on Machine Learning (ICML)},
  series = {Proceedings of Machine Learning Research (PMLR)},
  year = {2024},
}

Accurate Computation of the Logarithm of Modified Bessel Functions on GPUs
Andreas L. Plesner, Hans Henrik Brandenborg Sørensen, and Søren Hauberg.
In International Conference on Supercomputing (ICS), 2024.
PDF Publisher's site Code

@inproceedings{Plesner:ICS:2024,
  title = {Accurate Computation of the Logarithm of Modified Bessel Functions on GPUs},
  author = {Andreas L. Plesner and Hans Henrik Brandenborg S{\o}rensen and S{\o}ren Hauberg},
  booktitle = {International Conference on Supercomputing (ICS)},
  publisher = {ACM},
  year = {2024}
}

Gradients of Functions of Large Matrices
Nicholas Krämer, Pablo Moreno-Muñoz, Hrittik Roy and Søren Hauberg.
Neural Information Processing Systems (NeurIPS), 2024.
PDF arXiv Code

@inproceedings{kramer:lanczos:2024,
  title={Gradients of Functions of Large Matrices}, 
  author={Nicholas Kr{\"a}mer and Pablo Moreno-Mu\~{n}oz and Hrittik Roy and S{\o}ren Hauberg},
  booktitle = {Neural Information Processing Systems (NeurIPS)},
  year = {2024},
}

Laplacian Segmentation Networks: Improved Epistemic Uncertainty from Spatial Aleatoric Uncertainty
Kilian Zepf, Selma Wanna, Marco Miani, Juston Moore, Jes Frellsen, Søren Hauberg, Frederik Warburg, Aasa Feragen.
In MICCAI, 2024.
PDF arXiv

@inproceedings{zepf:lsn:2024,
  title = {Laplacian Segmentation Networks: Improved Epistemic Uncertainty from Spatial Aleatoric Uncertainty},
  author = {Kilian Zepf and Selma Wanna and Marco Miani and Juston Moore and Jes Frellsen and S{\o}ren Hauberg and Frederik Warburg and Aasa Feragen},
  booktitle = {Medical Image Computing and Computer-Assisted Intervention (MICCAI)},
  year = {2024},
}

Neural Contractive Dynamical Systems
Hadi Beik-Mohammadi, Søren Hauberg, Georgios Arvanitidis, Nadia Figueroa, Gerhard Neumann and Leonel Rozo.
In International Conference on Representation Learning (ICLR), 2024.
PDF arXiv

@inproceedings{hadi:iclr:2024,
  title = {Neural Contractive Dynamical Systems},
  author = {Hadi Beik-Mohammadi and S{\o}ren Hauberg and Georgios Arvanitidis and Nadia Figueroa and Gerhard Neumann and Leonel Rozo},
  booktitle = {International Conference on Representation Learning (ICLR)},
  year = {2024}
}

Bayes without Underfitting: Fully Correlated Deep Learning Posteriors via Alternating Projections
Marco Miani, Hrittik Roy and Søren Hauberg.
Preprint, 2024.
PDF ArXiv

@misc{miani2024bayesunderfittingfullycorrelated,
      title={Bayes without Underfitting: Fully Correlated Deep Learning Posteriors via Alternating Projections}, 
      author={Marco Miani and Hrittik Roy and Søren Hauberg},
      year={2024},
      eprint={2410.16901},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2410.16901}, 
}

Reparameterization invariance in approximate Bayesian inference
Hrittik Roy, Marco Miani, Carl Henrik Ek, Philipp Hennig, Marvin Pförtner, Lukas Tatzel and Søren Hauberg.
Neural Information Processing Systems (NeurIPS), 2024.
PDF arXiv Code

@inproceedings{roy:reparam:2024,
  title={Reparameterization invariance in approximate Bayesian inference}, 
  author={Hrittik Roy and Marco Miani and Carl Henrik Ek and Philipp Hennig and Marvin Pförtner and Lukas Tatzel and S{\o}ren Hauberg},
  booktitle = {Neural Information Processing Systems (NeurIPS)},
  year = {2024},
}

Visualizing Riemannian data with Rie-SNE
Andri Bergsson and Søren Hauberg.
Machine Learning Methods in Visualisation (MLVis), 2024.
PDF arXiv Code

@inproceedings{bergsson:mlvis:2024,
  title = {Visualizing Riemannian data with Rie-SNE},
  author = {Andri Bergsson and S{\o}ren Hauberg},
  booktitle = {Machine Learning Methods in Visualisation},
  year = {2024},
}

Sketched Lanczos uncertainty score: a low-memory summary of the Fisher information
Marco Miani, Lorenzo Beretta and Søren Hauberg.
Neural Information Processing Systems (NeurIPS), 2024.
PDF ArXiv Code

@inproceedings{miani:sketching:2024,
  title = {Sketched Lanczos uncertainty score: a low-memory summary of the Fisher information},
  author = {Marco Miani and Lorenzo Beretta and S{\o}ren Hauberg},
  booktitle = {Neural Information Processing Systems (NeurIPS)},
  year = {2024},
}

2023

Identifying latent distances with Finslerian geometry
Alison Pouplin, David Eklund, Carl Henrik Ek, and Søren Hauberg.
Transactions on Machine Learning Research (TMLR), 2023.
PDF ArXiv

@inproceedings{pouplin:finsler:2023,
  author = {Pouplin, Alison and Eklund, David and Ek, Carl Henrik and Hauberg, S{\o}ren},
  title = {Identifying latent distances with Finslerian geometry},
  publisher = {Transactions on Machine Learning Research (TMLR)},
  year = {2023},
}

On Masked Pre-training and the Marginal Likelihood
Pablo Moreno-Muñoz, Pol G. Recasens and Søren Hauberg.
In Neural Information Processing Systems (NeurIPS) 2023
PDF arXiv Code

@inproceedings{morenomunoz2023masked,
  title={On Masked Pre-training and the Marginal Likelihood}, 
  author={Pablo Moreno-Muñoz and Pol G. Recasens and Søren Hauberg},
  booktitle = {Neural Information Processing Systems (NeurIPS)},
  year = {2023},
}

Bayesian Metric Learning for Uncertainty Quantification in Image Retrieval
Frederik Warburg, Marco Miani, Silas Brack and Søren Hauberg.
In Neural Information Processing Systems (NeurIPS) 2023
PDF arXiv

@inproceedings{warburg:metric:2023,
  title = {Bayesian Metric Learning for Uncertainty Quantification in Image Retrieval},
  author = {Frederik Warburg and Marco Miani and Silas Brack and S{\o}ren Hauberg},
  booktitle = {Neural Information Processing Systems (NeurIPS)},
  year = {2023},
}

Pitfalls in machine learning-based assessment of tumor-infiltrating lymphocytes in breast cancer: a report of the international immuno-oncology biomarker working group
Jeppe Thagaard et al. (142 authors)
The Journal of pathology, August 2023.
PDF Publishers site

@article {PMID:37608772,
	Title = {Pitfalls in machine learning-based assessment of tumor-infiltrating lymphocytes in breast cancer: a report of the international immuno-oncology biomarker working group},
	Author = {Thagaard, Jeppe and Broeckx, Glenn and Page, David B and Jahangir, Chowdhury Arif and Verbandt, Sara and Kos, Zuzana and Gupta, Rajarsi and Khiroya, Reena and Abduljabbar, Khalid and Acosta Haab, Gabriela and Acs, Balazs and Akturk, Guray and Almeida, Jonas S and Alvarado-Cabrero, Isabel and Amgad, Mohamed and Azmoudeh-Ardalan, Farid and Badve, Sunil and Baharun, Nurkhairul Bariyah and Balslev, Eva and Bellolio, Enrique R and Bheemaraju, Vydehi and Blenman, Kim Rm and Botinelly Mendonça Fujimoto, Luciana and Bouchmaa, Najat and Burgues, Octavio and Chardas, Alexandros and Chon U Cheang, Maggie and Ciompi, Francesco and Cooper, Lee Ad and Coosemans, An and Corredor, Germán and Dahl, Anders B and Dantas Portela, Flavio Luis and Deman, Frederik and Demaria, Sandra and Doré Hansen, Johan and Dudgeon, Sarah N and Ebstrup, Thomas and Elghazawy, Mahmoud and Fernandez-Martín, Claudio and Fox, Stephen B and Gallagher, William M and Giltnane, Jennifer M and Gnjatic, Sacha and Gonzalez-Ericsson, Paula I and Grigoriadis, Anita and Halama, Niels and Hanna, Matthew G and Harbhajanka, Aparna and Hart, Steven N and Hartman, Johan and Hauberg, Søren and Hewitt, Stephen and Hida, Akira I and Horlings, Hugo M and Husain, Zaheed and Hytopoulos, Evangelos and Irshad, Sheeba and Janssen, Emiel Am and Kahila, Mohamed and Kataoka, Tatsuki R and Kawaguchi, Kosuke and Kharidehal, Durga and Khramtsov, Andrey I and Kiraz, Umay and Kirtani, Pawan and Kodach, Liudmila L and Korski, Konstanty and Kovács, Anikó and Laenkholm, Anne-Vibeke and Lang-Schwarz, Corinna and Larsimont, Denis and Lennerz, Jochen K and Lerousseau, Marvin and Li, Xiaoxian and Ly, Amy and Madabhushi, Anant and Maley, Sai K and Manur Narasimhamurthy, Vidya and Marks, Douglas K and McDonald, Elizabeth S and Mehrotra, Ravi and Michiels, Stefan and Minhas, Fayyaz Ul Amir Afsar and Mittal, Shachi and Moore, David A and Mushtaq, Shamim and Nighat, Hussain and Papathomas, Thomas and Penault-Llorca, Frederique and Perera, Rashindrie D and Pinard, Christopher J and Pinto-Cardenas, Juan Carlos and Pruneri, Giancarlo and Pusztai, Lajos and Rahman, Arman and Rajpoot, Nasir Mahmood and Rapoport, Bernardo Leon and Rau, Tilman T and Reis-Filho, Jorge S and Ribeiro, Joana M and Rimm, David and Roslind, Anne and Vincent-Salomon, Anne and Salto-Tellez, Manuel and Saltz, Joel and Sayed, Shahin and Scott, Ely and Siziopikou, Kalliopi P and Sotiriou, Christos and Stenzinger, Albrecht and Sughayer, Maher A and Sur, Daniel and Fineberg, Susan and Symmans, Fraser and Tanaka, Sunao and Taxter, Timothy and Tejpar, Sabine and Teuwen, Jonas and Thompson, E Aubrey and Tramm, Trine and Tran, William T and van der Laak, Jeroen and van Diest, Paul J and Verghese, Gregory E and Viale, Giuseppe and Vieth, Michael and Wahab, Noorul and Walter, Thomas and Waumans, Yannick and Wen, Hannah Y and Yang, Wentao and Yuan, Yinyin and Zin, Reena Md and Adams, Sylvia and Bartlett, John and Loibl, Sibylle and Denkert, Carsten and Savas, Peter and Loi, Sherene and Salgado, Roberto and Specht Stovgaard, Elisabeth},
	DOI = {10.1002/path.6155},
	Month = {August},
	Year = {2023},
	Journal = {The Journal of pathology},
	ISSN = {0022-3417},
	Abstract = {The clinical significance of the tumor-immune interaction in breast cancer is now established, and tumor-infiltrating lymphocytes (TILs) have emerged as predictive and prognostic biomarkers for patients with triple-negative (estrogen receptor, progesterone receptor, and HER2-negative) breast cancer and HER2-positive breast cancer. How computational assessments of TILs might complement manual TIL assessment in trial and daily practices is currently debated. Recent efforts to use machine learning (ML) to automatically evaluate TILs have shown promising results. We review state-of-the-art approaches and identify pitfalls and challenges of automated TIL evaluation by studying the root cause of ML discordances in comparison to manual TIL quantification. We categorize our findings into four main topics: (1) technical slide issues, (2) ML and image analysis aspects, (3) data challenges, and (4) validation issues. The main reason for discordant assessments is the inclusion of false-positive areas or cells identified by performance on certain tissue patterns or design choices in the computational implementation. To aid the adoption of ML for TIL assessment, we provide an in-depth discussion of ML and image analysis, including validation issues that need to be considered before reliable computational reporting of TILs can be incorporated into the trial and routine clinical management of patients with triple-negative breast cancer. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.},
	URL = {https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/path.6155},
}

Reactive Motion Generation on Learned Riemannian Manifolds
Hadi Beik-Mohammadi, Søren Hauberg, Georgios Arvanitidis, Gerhard Neumann and Leonel Rozo.
International Journal of Robotics Research, 2023.
PDF arXiv Publisher's site Video

@article{beikmohammadi:ijrr:2023,
  author = {Hadi Beik-Mohammadi and Søren Hauberg and Georgios Arvanitidis and Gerhard Neumann and Leonel Rozo},
  title ={Reactive motion generation on learned Riemannian manifolds},
  journal = {The International Journal of Robotics Research},
  year = {2023},
  doi = {10.1177/02783649231193046},
  URL = {https://doi.org/10.1177/02783649231193046},
}

Riemannian Laplace approximations for Bayesian neural networks
Federico Bergamin, Pablo Moreno-Muñoz, Søren Hauberg, and Georgios Arvantidis.
In Neural Information Processing Systems (NeurIPS) 2023
PDF arXiv

@inproceedings{bergamin:laplace:2023,
  title = {Riemannian Laplace approximations for Bayesian neural networks},
  author = {Federico Bergamin and Pablo Moreno-Muñoz and S{\o}ren Hauberg and Georgios Arvantidis},
  booktitle = {Neural Information Processing Systems (NeurIPS)},
  year = {2023},
}

Learning to Taste: A Multimodal Wine Dataset
Thoranna Bender, Simon Møe Sørensen, Alireza Kashani, K. Eldjarn Hjorleifsson, Grethe Hyldig, Søren Hauberg, Serge Belongie and Frederik Warburg.
In Neural Information Processing Systems (NeurIPS) 2023
PDF Dataset ArXiv

@inproceedings{bender2023learning,
      title={Learning to Taste: A Multimodal Wine Dataset}, 
      author={Thoranna Bender and Simon Møe Sørensen and Alireza Kashani and K. Eldjarn Hjorleifsson and Grethe Hyldig and Søren Hauberg and Serge Belongie and Frederik Warburg},
  booktitle = {Neural Information Processing Systems (NeurIPS)},
  year = {2023},
}

2022

Adaptive Cholesky Gaussian Processes
Simon Bartels, Kristoffer Stensbo-Smidt, Pablo Moreno-Munoz, Wouter Boomsma, Jes Frellsen and Søren Hauberg.
Proceedings of The 26th International Conference on Artificial Intelligence and Statistics, 2023.
PDF arXiv

@misc{bartels2022adaptive,
      title={Adaptive Cholesky Gaussian Processes}, 
      author={Simon Bartels and Kristoffer Stensbo-Smidt and Pablo Moreno-Munoz and Wouter Boomsma and Jes Frellsen and S{\o}ren Hauberg},
      year={2022},
      eprint={2202.10769},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

Pulling back information geometry
Georgios Arvantidis, Miguel González-Duque, Alison Pouplin, Dimitris Kalatzis and Søren Hauberg.
Artificial Intelligence and Statistics (AISTATS), 2022.
PDF arXiv

@inproceedings{many:aistats:2022,
  title = {Pulling back information geometry},
  author = {Georgios Arvantidis and Miguel González-Duque and Alison Pouplin and Dimitris Kalatzis and S{\o}ren Hauberg},
  year = {2022},
  booktitle = {Artificial Intelligence and Statistics},
}

Model-agnostic out-of-distribution detection using combined statistical tests
Federico Bergamin, Pierre-Alexandre Mattei, Jakob D. Havtorn, Hugo Senetaire, Hugo Schmutz, Lars Maaløe, Søren Hauberg and Jes Frellsen.
Artificial Intelligence and Statistics (AISTATS), 2022.
PDF arXiv

@inproceedings{bergamin:aistats:2022,
  title = {Model-agnostic out-of-distribution detection using combined statistical tests},
  author = {Federico Bergamin and Pierre-Alexandre Mattei and Jakob D. Havtorn and Hugo Senetaire and Hugo Schmutz and Lars Maaløe and S{\o}ren Hauberg and Jes Frellsen},
  year = {2022},
  booktitle = {Artificial Intelligence and Statistics},
}

Benchmarking Generative Latent Variable Models for Speech
Jakob Drachmann Havtorn, Lasse Borgholt, Søren Hauberg, Jes Frellsen and Lars Maaløe.
Deep Generative Models for Highly Structured Data (ICLR 2022 Workshop).
PDF OpenReview

@inproceedings{havtorn:DGM4HSD:2022,
  title = {Benchmarking Generative Latent Variable Models for Speech},
  author = {Jakob Drachmann Havtorn and Lasse Borgholt and S{\o}ren Hauberg and Jes Frellsen and Lars Maal{\o}e},
  booktitle = {Deep Generative Models for Highly Structured Data (ICLR 2022 Workshop)},
  year = {2022},
}

Danish Airs and Grounds: A Dataset for Aerial-to-Street-Level Place Recognition and Localization
Andrea Vallone, Frederik Warburg, Hans Hansen, Søren Hauberg and Javier Civera.
IEEE Robotics and Automation Letters.
PDF Dataset Publisher's site arXiv

@article{dag2022,
  author={Vallone, Andrea and Warburg, Frederik and Hansen, Hans and Hauberg, S{\o}ren and Civera, Javier},
  journal={IEEE Robotics and Automation Letters}, 
  title={Danish Airs and Grounds: A Dataset for Aerial-to-Street-Level Place Recognition and Localization}, 
  year={2022},
  volume={},
  number={},
  pages={1-8},
}

Laplacian Autoencoders for Learning Stochastic Representations
Marco Miani, Frederik Warburg, Pablo Moreno-Muñoz, Nicke Skafte Detlefsen and Søren Hauberg.
Advances in Neural Information Processing Systems (NeurIPS), 2022.
PDF arXiv

@inproceedings{miani:neurips:2022,
  title = {Laplacian Autoencoders for Learning Stochastic Representations},
  author = {Marco Miani and Frederik Warburg and Pablo Moreno-Muñoz and Nicki Skafte Detlefsen and S{\o}ren Hauberg},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
}

Mario Plays on a Manifold: Generating Functional Content in Latent Space through Differential Geometry
Miguel González-Duque, Rasmus Berg Palm, Søren Hauberg and Sebastian Risi.
Conference on Games, 2022.
PDF arXiv

@inproceedings{gonzalezduque:cog:2022,
  title={Mario Plays on a Manifold: Generating Functional Content in Latent Space through Differential Geometry}, 
  author = {Miguel González-Duque and Rasmus Berg Palm and S{\o}ren Hauberg and Sebastian Risi},
  year = {2022},
  booktitle = {IEEE Conference on Games (CoG)},
}

Optimal latent transport
Hrittik Roy and Søren Hauberg.
NeurIPS Workshop on Symmetry and Geometry in Neural Representations.
PDF OpenReview

@inproceedings{roy:optimal:2022,
  title = {Optimal latent transport},
  author = {Hrittik Roy and S{\o}ren Hauberg},
  booktitle = {NeurIPS Workshop on Symmetry and Geometry in Neural Representations},
  year = {2022},
}

Robust uncertainty estimates with out-of-distribution pseudo-inputs training
Pierre Segonne, Yevgen Zainchkovskyy and Søren Hauberg.
arXiv, 2022.
PDF arXiv

@inproceedings{Segonne:2022,
  title = {Robust uncertainty estimates with out-of-distribution pseudo-inputs training},
  author = {Pierre Segonne, and Yevgen Zainchkovskyy and S{\o}ren Hauberg},
  year={2022},
  eprint={2201.05890},
  archivePrefix={arXiv},
  primaryClass={cs.LG}
}

Learning meaningful representations of protein sequences
Nicki Skafte Detlefsen, Søren Hauberg and Wouter Boomsma.
Nature Communications 13.1 (2022).
PDF arXiv Publisher's site

@article{detlefsens:proteins:2022,
  title={Learning meaningful representations of protein sequences},
  author={Nicki Skafte Detlefsen and S{\o}ren Hauberg and Wouter Boomsma},
  journal={Nature Communications},
  volume={13},
  number={1},
  pages={1--12},
  year={2022},
  publisher={Nature Publishing Group}
}

Is an encoder within reach?
Helene Hauschultz, Rasmus Berg Palm. Pablo Moreno-Muños, Nicki Skafte Detlefsen, Andrew Allan du Plessis, Søren Hauberg.
arXiv preprint, 2022.
PDF arXiv

@misc{hauschultz:reach:2022,
  author = {Helene Hauschultz and Rasmus Berg Palm and Pablo Moreno-Muños and Nicki Skafte Detlefsen and Andrew Allan du Plessis and S{\o}ren Hauberg},
  title = {Is an encoder within reach?},
  publisher = {arXiv},
  year = {2022},
}

Revisiting Active Sets for Gaussian Process Decoders
Pablo Moreno-Muñoz, Cilie W. Feldager and Søren Hauberg.
Advances in Neural Information Processing Systems (NeurIPS), 2022.
PDF arXiv

@inproceedings{pablo:neurips:2022,
  title = {Revisiting Active Sets for Gaussian Process Decoders},
  author = {Pablo Moreno-Muñoz and Cilie Werner Feldager and S{\o}ren Hauberg},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
}

Probabilistic Spatial Transformer Networks
Pola Schwöbel, Frederik Warburg, Martin Jørgensen, Kristoffer H. Madsen and Søren Hauberg.
In The Conference on Uncertainty in Artificial Intelligence (UAI), 2022.
PDF arXiv

@inproceedings{schwobel:UAI:2022,
  title = {Probabilistic Spatial Transformer Networks},
  author = {Pola Schwöbel and Frederik Warburg and Martin Jørgensen and Kristoffer H. Madsen and S{\o}ren Hauberg},
  booktitle = {The Conference on Uncertainty in Artificial Intelligence (UAI)},
  year = {2022}
}

2021

Bayesian Triplet Loss: Uncertainty Quantification in Image Retrieval
Frederik Warburg, Martin Jørgensen, Javier Civera and Søren Hauberg.
International Conference on Computer Vision (ICCV), 2021.
PDF arXiv

@inproceedings{warburg:iccv:2021,
  title = {Bayesian Triplet Loss: Uncertainty Quantification in Image Retrieval},
  author = {Frederik Warburg and Martin J{\o}rgensen and Javier Civera and S{\o}ren Hauberg},
  booktitle = {International Conference on Computer Vision (ICCV)},
  year = {2021},
}

Automated Quantification of sTIL Density with H&E-Based Digital Image Analysis Has Prognostic Potential in Triple-Negative Breast Cancers
Jeppe Thagaard, Elisabeth S. Stovgaard, Line G. Vognsen, Søren Hauberg, Anders Dahl, Thomas Ebstrup, Johan Doré, Rikke E. Vincentz, Rikke K. Jepsen, Anne Roslind, Iben Kümler, Dorte Nielsen and Eva Balslev.
Cancers, 13(12), June 2021.
PDF Publishers site

@article{thagaard:cancers:2021,
  title = {Automated Quantification of sTIL Density with H&E-Based Digital Image Analysis Has Prognostic Potential in Triple-Negative Breast Cancers},
  author = {Jeppe Thagaard and Elisabeth S. Stovgaard and Line G. Vognsen and Søren Hauberg and Anders Dahl and Thomas Ebstrup and Johan Doré and Rikke E. Vincentz and Rikke K. Jepsen and Anne Roslind and Iben Kümler and Dorte Nielsen and Eva Balslev},
  publisher = {MDPI},
  month = June,
  number = {12},
  volume = {13},
  journal = {Cancers},
  year = {2021}
}

Geometrically Enriched Latent Spaces
Georgios Arvanitidis, Søren Hauberg, and Bernhard Schölkopf.
Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS), 2021.
PDF arXiv

@inproceedings{arvanitidis:aistats:2021,
  title = {Geometrically Enriched Latent Spaces},
  author = {Georgios Arvanitidis and S{\o}ren Hauberg and Bernhard Sch{\"o}lkopf},
  booktitle = {{Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS)}},
  series = {Journal of Machine Learning Research W\&CP},
  year = {2021},
}

Hierarchical VAEs Know What They Don’t Know
Jakob D. Havtorn, Jes Frellsen, Søren Hauberg and Lars Maaløe.
International Conference on Machine Learning (ICML), 2021.
PDF arXiv

@inproceedings{havtorn:icml:2021,
  author = {Jakob D. Havtorn and Jes Frellsen and S{\o}ren Hauberg and Lars Maal{\o}e},
  title = {Hierarchical VAEs Know What They Don’t Know},
  booktitle = {Proceedings of the 38th International Conference on Machine Learning (ICML)},
  series = {Proceedings of Machine Learning Research (PMLR)},
  year = {2021},
}

Spontaneous Symmetry Breaking in Data Visualization
Cilie W. Feldager, Søren Hauberg, and Lars Kai Hansen.
In International Conference on Artificial Neural Networks (ICANN), 2021.
PDF

@inproceedings{feldager:icann:2021,
  title = {Spontaneous Symmetry Breakingin Data Visualization},
  author = {Cilie W. Feldager and S{\o}ren Hauberg and Lars Kai Hansen},
  booktitle = {International Conference on Artificial Neural Networks (ICANN)},
  year = {2021}
}

Isometric Gaussian Process Latent Variable Model for Dissimilarity Data
Martin Jørgensen and Søren Hauberg.
International Conference on Machine Learning (ICML), 2021.
PDF arXiv

@inproceedings{jorgensen:icml:2021,
  author = {Martin J{\o}rgensen and S{\o}ren Hauberg},
  title = {Isometric Gaussian Process Latent Variable Model for Dissimilarity Data},
  booktitle = {Proceedings of the 38th International Conference on Machine Learning (ICML)},
  series = {Proceedings of Machine Learning Research (PMLR)},
  year = {2021},
}

Multi-chart flows
Dimitris Kalatzis, Johan Ziruo Ye, Jesper Wohlert and Søren Hauberg.
arXiv preprint, 2021.
PDF arXiv

@misc{kalatzis:arxiv:2021,
  title = {Multi-chart flows},
  author = {Dimitris Kalatzis and Johan Ziruo Ye and Jesper Wohlert and S{\o}ren Hauberg},
  year = {2021},
  eprint={2106.03500},
  archivePrefix={arXiv},
  primaryClass={cs.LG}
}

Bounds all around: training energy-based models with bidirectional bounds
Cong Geng, Jia Wang, Zhiyong Gao, Jes Frellsen, and Søren Hauberg.
In Advances in Neural Information Processing Systems (NeurIPS) 34, 2021.
PDF arXiv

@inproceedings{conggeng:neurips:2021,
  title = {Bounds all around: training energy-based models with bidirectional bounds},
  author = {Cong Geng and Jia Wang and Zhiyong Gao and Jes Frellsen and S{\o}ren Hauberg},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS) 34},
  year = {2021}
}

Learning Riemannian Manifolds for Geodesic Motion Skills
Hadi Beik-Mohammadi, Søren Hauberg, Georgios Arvanitidis, Gerhard Neumann and Leonel Rozo.
Robotics: Science and Systems (RSS), 2021.
PDF arXiv Best student paper award Code

@inproceedings{hadi:rss:2021,
  author = {Hadi Beik-Mohammadi and S{\o}ren Hauberg and Georgios Arvanitidis and Gerhard Neumann and Leonel Rozo},
  title = {Learning Riemannian Manifolds for Geodesic Motion Skills},
  booktitle = {Robotics: Science and Systems (RSS)},
  year = {2021},
}

2020

Reparametrization Invariance in non-parametric Causal Discovery
Martin Jørgensen and Søren Hauberg.
arXiv preprint, 2020.
PDF arXiv

@inproceedings{jorgensen:arxiv:2020b,
  title = {Reparametrization Invariance in non-parametric Causal Discovery},
  author = {Martin J{\o}rgensen and S{\o}ren Hauberg},
  booktitle = {arXiv preprint},
  year = {2020},
}

Mapillary Street-Level Sequences: A Dataset for Lifelong Place Recognition
Frederik Warburg, Søren Hauberg, Manuel López-Antequera, Pau Gargallo, Yubin Kuang, and Javier Civera.
In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020
PDF Mapillary blog Dataset

@inproceedings{warburg:cvpr:2020,
  title = {Mapillary Street-Level Sequences: A Dataset for Lifelong Place Recognition},
  author = {Frederik Warburg and S{\o}ren Hauberg and Manuel Lopez-Antequera and Pau Gargallo and
Yubin Kuang and Javier Civera},
  booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2020}
}

Variational Autoencoders with Riemannian Brownian Motion Priors
Dimitris Kalatzis, David Eklund, Georgios Arvanitidis and Søren Hauberg.
In International Conference on Machine Learning (ICML), 2020.
PDF arXiv

@inproceedings{kalatzis:icml:2020,
  author = {Dimitris Kalatzis and David Eklund and Georgios Arvanitidis and S{\o}ren Hauberg},
  title = {Variational Autoencoders with Riemannian Brownian Motion Priors},
  booktitle = {Proceedings of the 37th International Conference on Machine Learning (ICML)},
  series = {Proceedings of Machine Learning Research (PMLR)},
  volume = {119},
  year = {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, and Anders B. Dahl.
In Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2020, Lima, Peru, October 2020.
PDF

@inproceedings{Thagaard:MICCAI:2020,
  title = {Can you trust predictive uncertainty under real dataset shifts in digital pathology?},
  author = {Jeppe Thagaard and S{\o}ren Hauberg and Bert van der Vegt and Thomas Ebstrup and Johan D. Hansen and Anders B. Dahl},
  booktitle = {Medical Image Computing and Computer-Assisted Intervention (MICCAI)},
  address = {Lima, Peru},
  month = oct,
  year = {2020}
}

Intrinsic Grassmann Averages for Online Linear Robust and Nonlinear Subspace Learning
Rudrasis Chakraborty, Liu Yang, Søren Hauberg and Baba C. Vemuri.
In IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI).
PDF

@article{chakraborty:pami:2020,
  title = {Intrinsic Grassmann Averages for Online Linear Robust and Nonlinear Subspace Learning},
  author = {Rudrasis Chakraborty and Liu Yang and S{\o}ren Hauberg and Baba C. Vemuri},
  journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)},
  year = {2020},
}

Parallel QR factorization of block-tridiagonal matrices
Alfredo Buttari, Søren Hauberg and Costy Kodsi.
SIAM Journal on Scientific Computing, 2020.
PDF Publishers site HAL

@article{buttari2020parallel,
  title={Parallel QR factorization of block-tridiagonal matrices},
  author={Buttari, Alfredo and Hauberg, S{\o}ren and Kodsi, Costy},
  journal={SIAM Journal on Scientific Computing},
  volume={42},
  number={6},
  pages={C313--C334},
  year={2020},
  publisher={SIAM}
}

2019

Fast and Robust Shortest Paths on Manifolds Learned from Data
Georgios Arvanitidis, Søren Hauberg, Philipp Hennig, and Michael Schober.
In Proceedings of the 22nd international Conference on Artificial Intelligence and Statistics (AISTATS), 2019.
PDF Supplements Code

@inproceedings{arvanitidis:aistats:2019,
  title = {Fast and Robust Shortest Paths on Manifolds Learned from Data},
  author = {Georgios Arvanitidis and S{\o}ren Hauberg and Philipp Hennig and Michael Schober},
  booktitle = {Proceedings of the 19th international Conference on Artificial Intelligence and Statistics (AISTATS)},
  journal = {Journal of Machine Learning Research, W\&CP},
  year = {2019}
}

Expected path length on random manifolds
David Eklund and Søren Hauberg.
arXiv preprint, 2019.
PDF arXiv

@inproceedings{eklund:arxiv:2019,
  title = {Expected path length on random manifolds},
  author = {David Eklund and S{\o}ren Hauberg},
  booktitle = {arXiv preprint},
  year = {2019},
}

Reliable training and estimation of variance networks
Nicki Skafte Detlefsen, Martin Jørgensen, and Søren Hauberg.
In Advances in Neural Information Processing Systems (NeurIPS), 2019.
PDF arXiv

@inproceedings{detlefsen2019reliable,
  title={Reliable training and estimation of variance networks},
  author={Detlefsen, Nicki S and J{\o}rgensen, Martin and Hauberg, S{\o}ren},
  year={2019},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
}

Explicit Disentanglement of Appearance and Perspective in Generative Models
Nicki Skafte Detlefsen, and Søren Hauberg.
In Advances in Neural Information Processing Systems (NeurIPS), 2019.
PDF arXiv

@inproceedings{detlefsen2019explicit,
  title={Explicit Disentanglement of Appearance and Perspective in Generative Models},
  author={Nicki Skafte Detlefsen and S{\o}ren Hauberg},
  year={2019},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
}

Probabilistic Riemannian submanifold learning with wrapped Gaussian process latent variable models
Anton Mallasto, Søren Hauberg, and Aasa Feragen.
In Proceddings of 22nd international Conference on Artificial Intelligence and Statistics (AISTATS), 2019.
PDF ArXiv

@inproceedings{mallasto:2018,
  title = {Probabilistic Riemannian submanifold learning with wrapped Gaussian process latent variable models},
  author = {Anton Mallasto and S{\o}ren Hauberg and Aasa Feragen},
  booktitle = {Proceedings of the 19th international Conference on Artificial Intelligence and Statistics (AISTATS)},
  journal = {Journal of Machine Learning Research, W\&CP},
  year = {2018}
}

2018

Geodesic Clustering in Deep Generative Models
Tao Yang, Georgios Arvanitidis, Dongmei Fu, Xiaogang Li, and Søren Hauberg.
arXiv preprint, 2018.
PDF arXiv

@inproceedings{yang:arxiv:2018,
  title = {Geodesic Clustering in Deep Generative Models},
  author = {Tao Yang and Georgios Arvanitidis and Dongmei Fu and Xiaogang Li and S{\o}ren Hauberg},
  booktitle = {arXiv preprint},
  year = {2018},
}

Deep Diffeomorphic Transformer Networks
Nicki Skafte Detlefsen, Oren Freifeld and Søren Hauberg.
In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, USA. July 2018.
PDF Code

@inproceedings{detlefsen:cvpr:2018,
  title = {Deep Diffeomorphic Transformer Networks},
  author = {Nicki Skafte Detlefsen and Oren Freifeld and S{\o}ren Hauberg},
  booktitle = {Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR)},
  month = {July},
  year = {2018}
}

Directional Statistics with the Spherical Normal Distribution
Søren Hauberg.
In Proceedings of FUSION 2018.
PDF Supplements

@inproceedings{hauberg:SN:2018,
  title = {Directional Statistics with the Spherical Normal Distribution},
  author = {S{\o}ren Hauberg},
  booktitle = {Proceedings of FUSION 2018},
  year = {2018}
}

Latent Space Oddity: on the Curvature of Deep Generative Models
Georgios Arvanitidis, Lars Kai Hansen and Søren Hauberg.
In International Conference on Learning Representations (ICLR), 2018.
PDF

@inproceedings{arvanitidis:iclr:2018,
  title = {Latent Space Oddity: on the Curvature of Deep Generative Models},
  author = {Arvanitidis, Georgios and Hansen, Lars Kai and Hauberg, S{\o}ren},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2018}
}

The non-central Nakagami distribution
Søren Hauberg.
Unpublished technical note, 2018.
PDF

@article{hauberg:nakagami:2018,
  title = {The non-central Nakagami distribution},
  author = {S{\o}ren Hauberg},
  year = {2018}
}

Only Bayes should learn a manifold (on the estimation of differential geometric structure from data)
Søren Hauberg.
Unpublished manuscript, 2018.
PDF arXiv

@article{hauberg:only:2018,
  title = {Only Bayes should learn a manifold},
  author = {S{\o}ren Hauberg},
  year = {2018}
}

On the Geometry of Latent Variable Models
Søren Hauberg.
Oberwolfach abstract from Statistics for Data with Geometric Structure, 2018 (3).
PDF Workshop and complete report

@article{hauberg:owr:2018,
  title = {On the Geometry of Latent Variable Models},
  author = {Hauberg, S{\o}ren},
  journal = {Oberwolfach reports : OWR},
  year = {2018},
  number = {3},
  publisher = {Mathematisches Forschungsinstitut [u.a.]},
}

2017

Intrinsic Grassmann Averages for Online Linear and Robust Subspace Learning
Rudrasis Chakraborty, Søren Hauberg, and Baba C. Vemuri.
In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), Honolulu, USA. July 2017.
PDF arXiv

@inproceedings{chakraborty:cvpr:2017,
  title = {Intrinsic Grassmann Averages for Online Linear and Robust Subspace Learning},
  author = {Rudrasis Chakraborty and S{\o}ren Hauberg and Baba C. Vemuri},
  booktitle = {Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR)},
  month = {July},
  year = {2017}
}

Maximum likelihood estimation of Riemannian metrics from Euclidean data
Georgios Arvanitidis, Lars Kai Hansen and Søren Hauberg.
In Geometric Science of Information (GSI), 2017.
PDF

@inproceedings{arvanitidis:gsi:2017,
  title = {Maximum likelihood estimation of Riemannian metrics from Euclidean data},
  author = {Georgios Arvanitidis and Lars Kai Hansen and S{\o}ren Hauberg},
  booktitle = {Geometric Science of Information (GSI)},
  year = {2017}
}

Transformations Based on Continuous Piecewise-Affine Velocity Fields
Oren Freifeld, Søren Hauberg, Kayhan Batmanghelich and John W. Fisher III.
In IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI).
PDF Supplements Derivative of gradient Code

@article{freifeld:preprint:2016,
  title = {Transformations Based on Continuous Piecewise-Affine Velocity Fields},
  author = {Oren Freifeld and S{\o}ren Hauberg and Kayhan Batmanghelich and John W. Fisher III},
  journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)},
}

2016

Dreaming More Data: Class-dependent Distributions over Diffeomorphisms for Learned Data Augmentation
Søren Hauberg, Oren Freifeld, Anders Boesen Lindbo Larsen, John W. Fisher III, and Lars Kai Hansen.
In Proceedings of the 19th international Conference on Artificial Intelligence and Statistics (AISTATS), volume 51, 2016.
PDF Publisher's site arXiv Animation Supplements

@inproceedings{hauberg:aistats:2016,
  title = {Dreaming More Data: Class-dependent Distributions over Diffeomorphisms for Learned Data Augmentation},
  author = {S{\o}ren Hauberg and Oren Freifeld and Anders Boesen Lindbo Larsen and John W. Fisher III and Lars Kai Hansen},
  booktitle = {Proceedings of the 19th international Conference on Artificial Intelligence and Statistics (AISTATS)},
  journal = {Journal of Machine Learning Research, W\&CP},
  volume = {51},
  pages = {342--350},
  year = {2016}
}

A Locally Adaptive Normal Distribution
Georgios Arvanitidis, Lars Kai Hansen, and Søren Hauberg.
Neural Information Processing Systems (NeurIPS), 2016.
PDF Errata arXiv YouTube

@inproceedings{arvanitidis:nips:2016,
  title = {A Locally Adaptive Normal Distribution},
  author = {Georgios Arvanitidis and Lars Kai Hansen and S{\o}ren Hauberg},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2016},
  month = {jun},
}

Open Problem: Kernel methods on manifolds and metric spaces. What is the probability of a positive definite geodesic exponential kernel?
Aasa Feragen and Søren Hauberg.
In Conference on Learning Theory (COLT), 2016.
PDF Publisher's site

@inproceedings{feragen:colt:2016,
  author = {Feragen, Aasa and Hauberg, S{\o}ren},
  title = {Open Problem: Kernel methods on manifolds and metric spaces. What is the probability of a positive definite geodesic exponential kernel?},
  booktitle = {29th Annual Conference on Learning Theory},
  series = {Journal of Machine Learning Research W\&CP},
  volume = {49},
  year = {2016},
  pages = {1647--1650},
}

Data-driven forward model inference for EEG brain imaging
Sofie Therese Hansen, Søren Hauberg, and Lars Kai Hansen.
In NeuroImage, 2016.
Publisher's site Preprint

@article{hansen:neuroimage:2016,
  title = {{Data-driven forward model inference for EEG brain imaging}},
  author = {Sofie Therese Hansen and S{\o}ren Hauberg and Lars Kai Hansen},
  journal = "NeuroImage",
  volume = "139",
  number = "",
  pages = "249--258",
  year = "2016",
}

2015

Geodesic Exponential Kernels: When Curvature and Linearity Conflict
Aasa Feragen,Françous Lauze, and Søren Hauberg.
In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), Boston, USA. June 2015.
PDF Extended abstract (PDF) SIMBAD extended abstract (PDF) arXiv

@inproceedings{feragen:cvpr:2015,
  title = {Geodesic Exponential Kernels: When Curvature and Linearity Conflict},
  author = {Aasa Feragen and Fran\c{c}ous Lauze and S{\o}ren Hauberg},
  booktitle = { Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR)},
  address = {Boston, USA},
  month = jun,
  year = {2015}
}

Highly-Expressive Spaces of Well-Behaved Transformations: Keeping It Simple
Oren Freifeld, Søren Hauberg, Kayhan Batmanghelich and John W. Fisher III.
In International Conference on Computer Vision (ICCV), Santiago, Chile. December 2015.
PDF Code

@inproceedings{freifeld:iccv:2015,
  title = {Highly-Expressive Spaces of Well-Behaved Transformations: Keeping It Simple},
  author = {Oren Freifeld and S{\o}ren Hauberg and Kayhan Batmanghelich and John W. Fisher III},
  booktitle = {International Conference on Computer Vision (ICCV)},
  address = {Santiago, Chile},
  month = Dec,
  year = {2015}
}

A Random Riemannian Metric for Probabilistic Shortest-Path Tractography
Søren Hauberg, Michael Schober, Matthew Liptrot, Philipp Hennig, and Aasa Feragen.
In Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2015, Munich, Germany, September 2015.
PDF Youtube

@inproceedings{Hauberg:MICCAI:2015,
  title = {A Random Riemannian Metric for Probabilistic Shortest-Path Tractography},
  author = {S{\o}ren Hauberg and Michael Schober and Matthew Liptrot and Philipp Hennig and Aasa Feragen},
  booktitle = {Medical Image Computing and Computer-Assisted Intervention (MICCAI)},
  address = {Munich, Germany},
  month = sep,
  year = {2015}
}

Principal Curves on Riemannian Manifolds
Søren Hauberg.
In IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI).
PDF Publishers site Supplements Animation

@article{hauberg:tpami:princurve,
  title = {Principal Curves on Riemannian Manifolds},
  author = {Hauberg, S{\o}ren},
  journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)},
}

Scalable Robust Principal Component Analysis using Grassmann Averages
Søren Hauberg, Aasa Feragen, Raffi Enficiaud and Michael J. Black.
In IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI).
PDF Publishers site Supplements Paper site w. code

@article{hauberg:tpami:grassmann,
  title = {Scalable Robust Principal Component Analysis using Grassmann Averages},
  author = {S{\o}ren Hauberg and Aasa Feragen and Raffi Enficiaud and Michael J. Black},
  journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)},
}

2014

Probabilistic Solutions to Differential Equations and their Application to Riemannian Statistics
Philipp Hennig and Søren Hauberg.
In Proceedings of the 17th international Conference on Artificial Intelligence and Statistics (AISTATS), volume 33, 2014.
PDF Paper site w. code Youtube Supplements

@inproceedings{hennig:aistats:2014,
  title = {Probabilistic Solutions to Differential Equations and their Application to Riemannian Statistics},
  author = {Hennig, Philipp and Hauberg, S{\o}ren},
  booktitle = {Proceedings of the 17th international Conference on Artificial Intelligence and Statistics (AISTATS)},
  volume = {33},
  journal = {Journal of Machine Learning Research, W\&CP},
  year = {2014}
}

Grassmann Averages for Scalable Robust PCA
Søren Hauberg, Aasa Feragen, and Michael J. Black.
In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), Columbus, Ohio, USA. June 2014.
PDF Paper site w. code Tutorial video Results video Supplementary materials CVPR talk (video)

@inproceedings{Hauberg:CVPR:2014,
  title = {Grassmann Averages for Scalable Robust {PCA}},
  author = {Hauberg, S{\o}ren and Feragen, Aasa and Black, Michael J.},
  booktitle = { Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR)},
  address = {Columbus, Ohio, USA},
  month = jun,
  year = {2014}
}

Model Transport: Towards Scalable Transfer Learning on Manifolds
Oren Freifeld, Søren Hauberg, and Michael J. Black.
In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), Columbus, Ohio, USA. June 2014.
PDF Supplementary material

@inproceedings{Freifeld:CVPR:2014,
  title = {Model Transport: Towards Scalable Transfer Learning on Manifolds},
  author = {Freifeld, Oren and Hauberg, S{\o}ren and Black, Michael J.},
  booktitle = { Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR)},
  address = {Columbus, Ohio, USA},
  month = jun,
  year = {2014}
}

Probabilistic shortest path tractography in DTI using Gaussian Process ODE solvers
Michael Schober, Niklas Kasenburg, Aasa Feragen, Philipp Hennig, and Søren Hauberg.
In Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2014, Boston, USA, September 2014.
PDF Supplementary material Paper site w. code Youtube 1 Youtube 2

@inproceedings{Schober:MICCAI:2014,
  title = {Probabilistic shortest path tractography in DTI using Gaussian Process ODE solvers},
  author = {Michael Schober and Niklas Kasenburg and Aasa Feragen and Philipp Hennig and S{\o}ren Hauberg},
  booktitle = {Medical Image Computing and Computer-Assisted Intervention (MICCAI)},
  address = {Boston, USA},
  month = sep,
  year = {2014}
}

Metrics for Probabilistic Geometries
Alessandra Tosi, Søren Hauberg, Alfredo Vellido, and Neil D. Lawrence.
In The Conference on Uncertainty in Artificial Intelligence (UAI), Quebec, Canada. July 2014.
PDF arXiv

@inproceedings{Tosi:UAI:2014,
  title = {Metrics for Probabilistic Geometries},
  author = {Tosi, Alessandra and Hauberg, S{\o}ren and Vellido, Alfredo and Lawrence, Neil D.},
  booktitle = {The Conference on Uncertainty in Artificial Intelligence (UAI)},
  address = {Quebec, Canada},
  month = jul,
  year = {2014}
}

2013

Unscented Kalman Filtering on Riemannian Manifolds
Søren Hauberg, Françous Lauze, and Kim S. Pedersen.
Journal of Mathematical Imaging and Vision, 46(1):103-120, May 2013.
PDF Publishers site Errata

@article{hauberg:jmiv:2013,
  title = {Unscented Kalman Filtering on Riemannian Manifolds},
  author = {Hauberg, S{\o}ren and Lauze, Francois and Pedersen, Kim S.},
  publisher = {Springer Netherlands},
  month = may,
  number = {1},
  volume = {46},
  pages = {103--120},
  journal = {Journal of Mathematical Imaging and Vision},
  year = {2013}
}

2012

Spatial Measures between Human Poses for Classification and Understanding
Søren Hauberg and Kim S. Pedersen.
In In Articulated Motion and Deformable Objects, Springer Berlin Heidelberg, volume 7378, LNCS, pages 26-36, 2012.
Publishers site

@inproceedings{hauberg:amdo:2012,
  title = {Spatial Measures between Human Poses for Classification and Understanding},
  author = {Hauberg, S{\o}ren and Pedersen, Kim S.},
  booktitle = {Articulated Motion and Deformable Objects},
  editor = {Perales, Francisco J. and Fisher, Robert B. and Moeslund, Thomas B.},
  publisher = {Springer Berlin Heidelberg},
  series = {LNCS},
  volume = {7378},
  pages = {26-36},
  year = {2012}
}

A geometric framework for statistics on trees
Aasa Feragen, Mads Nielsen, Søren Hauberg, Pechin Lo, Marleen de Bruijne, and Françous Lauze.
Technical Report 11/02, Department of Computer Science, University of Copenhagen, January 2012.
PDF

@techreport{feragen:tech:2012,
  title = {A geometric framework for statistics on trees},
  author = {Feragen, Aasa and Nielsen, Mads and Hauberg, S{\o}ren and Lo, Pechin and de Bruijne, Marleen and Lauze, Francois},
  month = jan,
  number = {11/02},
  institution = {Department of Computer Science, University of Copenhagen},
  year = {2012}
}

HUMIM Software for Articulated Tracking
Søren Hauberg and Kim S. Pedersen.
Technical Report 01/2012, Department of Computer Science, University of Copenhagen, January 2012.
PDF

@techreport{hauberg:diku:2012,
  title = {HUMIM Software for Articulated Tracking},
  author = {Hauberg, S{\o}ren and Pedersen, Kim S.},
  month = jan,
  number = {01/2012},
  institution = {Department of Computer Science, University of Copenhagen},
  year = {2012}
}

Natural Metrics and Least-Committed Priors for Articulated Tracking
Søren Hauberg, Stefan Sommer, and Kim S. Pedersen.
Image and Vision Computing, 30(6-7):453-461, 2012.
PDF Code Publishers site

@article{hauberg:imavis:2011,
  title = {Natural Metrics and Least-Committed Priors for Articulated Tracking},
  author = {Hauberg, S{\o}ren and Sommer, Stefan and Pedersen, Kim S.},
  publisher = {Elsevier},
  number = {6-7},
  volume = {30},
  pages = {453--461},
  journal = {Image and Vision Computing},
  year = {2012}
}

A Geometric Take on Metric Learning
Søren Hauberg, Oren Freifeld, and Michael J. Black.
In Advances in Neural Information Processing Systems (NeurIPS) 25, MIT Press, pages 2033-2041, 2012.
PDF Supplementary material Code Poster

@inproceedings{hauberg:nips:2012,
  title = {A Geometric Take on Metric Learning},
  author = {Hauberg, S{\o}ren and Freifeld, Oren and Black, Michael J.},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS) 25},
  editor = {Bartlett, P. and Pereira, F.C.N. and Burges, C.J.C. and Bottou, L. and Weinberger, K.Q.},
  publisher = {MIT Press},
  pages = {2033--2041},
  year = {2012}
}

2011

Data-Driven Importance Distributions for Articulated Tracking
Søren Hauberg and Kim S. Pedersen.
In Energy Minimization Methods in Computer Vision and Pattern Recognition, Springer Berlin Heidelberg, volume 6819, Lecture Notes in Computer Science, pages 287-299, 2011.
PDF Errata Code

@inproceedings{hauberg:emmcvpr:2011,
  title = {Data-Driven Importance Distributions for Articulated Tracking},
  author = {Hauberg, S{\o}ren and Pedersen, Kim S.},
  booktitle = {Energy Minimization Methods in Computer Vision and Pattern Recognition},
  editor = {Boykov, Yuri and Kahl, Fredrik and Lempitsky, Victor and Schmidt, Frank},
  publisher = {Springer Berlin Heidelberg},
  series = {Lecture Notes in Computer Science},
  volume = {6819},
  pages = {287-299},
  year = {2011}
}

An Empirical Study on the Performance of Spectral Manifold Learning Techniques
Peter Mysling, Søren Hauberg and Kim S. Pedersen.
In Artificial Neural Networks and Machine Learning - ICANN 2011, Springer Berlin Heidelberg, volume 6791, Lecture Notes in Computer Science, pages 347-354, 2011.
PDF

@inproceedings{mysling:icann:2011,
  title = {An Empirical Study on the Performance of Spectral Manifold Learning Techniques},
  author = {Mysling, Peter and Hauberg, S{\o}ren and Pedersen, Kim S.},
  booktitle = {Artificial Neural Networks and Machine Learning – ICANN 2011},
  editor = {Honkela, Timo and Duch, Włodzisław and Girolami, Mark and Kaski, Samuel},
  publisher = {Springer Berlin Heidelberg},
  series = {Lecture Notes in Computer Science},
  volume = {6791},
  pages = {347-354},
  year = {2011}
}

Means in spaces of tree-like shapes
Aasa Feragen, Søren Hauberg, Mads Nielsen, and Françous Lauze.
In IEEE International Conference on Computer Vision (ICCV), pages 736 -746, 2011.
PDF Supplementary material

@inproceedings{feragen:iccv:2011,
  title = {Means in spaces of tree-like shapes},
  author = {Feragen, Aasa and Hauberg, S{\o}ren and Nielsen, Mads and Lauze, Francois},
  booktitle = {Computer Vision (ICCV), 2011 IEEE International Conference on},
  publisher = {IEEE},
  month = nov,
  pages = {736 -746},
  year = {2011}
}

Predicting Articulated Human Motion from Spatial Processes
Søren Hauberg and Kim S. Pedersen.
International Journal of Computer Vision (IJCV), 2011.
PDF Paper site Code Publishers site

@article{hauberg:ijcv:2011,
  title = {Predicting Articulated Human Motion from Spatial Processes},
  author = {Hauberg, S{\o}ren and Pedersen, Kim S.},
  publisher = {Springer Netherlands},
  volume = {94},
  pages = {317-334},
  journal = {International Journal of Computer Vision},
  year = {2011}
}

A Physically Natural Metric for Human Motion and the Associated Brownian Motion Model
Søren Hauberg and Kim S. Pedersen.
In 1st IEEE Workshop on Kernels and Distances for Computer Vision (ICCV workshop), 2011.
Workshop site

@inproceedings{hauberg:kdcv:2011,
  title = {A Physically Natural Metric for Human Motion and the Associated Brownian Motion Model},
  author = {Hauberg, S{\o}ren and Pedersen, Kim Steenstrup},
  booktitle = {1st IEEE Workshop on Kernels and Distances for Computer Vision (ICCV workshop)},
  year = {2011}
}

Unscented Kalman Filtering for Articulated Human Tracking
Anders B.L. Larsen, Søren Hauberg, and Kim S. Pedersen.
In Image Analysis, Springer Berlin Heidelberg, volume 6688, Lecture Notes in Computer Science, pages 228-237, 2011.
PDF Publishers site

@inproceedings{larsen:scia:2011,
  title = {Unscented Kalman Filtering for Articulated Human Tracking},
  author = {Larsen, Anders Boesen Lindbo and Hauberg, S{\o}ren and Pedersen, Kim S.},
  booktitle = {Image Analysis},
  editor = {Heyden, Anders and Kahl, Fredrik},
  publisher = {Springer Berlin Heidelberg},
  series = {Lecture Notes in Computer Science},
  volume = {6688},
  pages = {228-237},
  year = {2011}
}

Spatial Models of Human Motion
Søren Hauberg.
PhD thesis. University of Copenhagen, 2011.

@phdthesis{hauberg:thesis:2011,
  title = {Spatial Models of Human Motion},
  author = {Hauberg, S{\o}ren},
  school = {University of Copenhagen},
  year = {2011}
}

2010

Stick It! Articulated Tracking using Spatial Rigid Object Priors
Søren Hauberg and Kim S. Pedersen.
In Computer Vision - ACCV 2010, Springer Berlin Heidelberg, volume 6494, Lecture Notes in Computer Science, pages 758-769, 2010
PDF Errata Paper site Code Publishers site

@inproceedings{hauberg:accv:2010,
  title = {Stick It! Articulated Tracking using Spatial Rigid Object Priors},
  author = {Hauberg, S{\o}ren and Pedersen, Kim S.},
  booktitle = {Computer Vision - ACCV 2010},
  editor = {Kimmel, Ron and Klette, Reinhard and Sugimoto, Akihiro},
  publisher = {Springer Berlin Heidelberg},
  series = {Lecture Notes in Computer Science},
  volume = {6494},
  pages = {758-769},
  year = {2010}
}

GPU Accelerated Likelihoods for Stereo-Based Articulated Tracking
Rune M. Friborg, Søren Hauberg, and Kenny Erleben.
In The CVGPU workshop at European Conference on Computer Vision (ECCV) 2010.
PDF

@inproceedings{friborg:eccv:2010,
  title = {GPU Accelerated Likelihoods for Stereo-Based Articulated Tracking},
  author = {Friborg, Rune M{\o}llegaard and Hauberg, S{\o}ren and Erleben, Kenny},
  booktitle = {The CVGPU workshop at European Conference on Computer Vision (ECCV) 2010},
  year = {2010}
}

Gaussian-like Spatial Priors for Articulated Tracking
Søren Hauberg, Stefan Sommer, and Kim S. Pedersen.
In Computer Vision - ECCV 2010, Springer Berlin Heidelberg, volume 6311, Lecture Notes in Computer Science, pages 425-437, 2010.
PDF Errata Paper site Code Publishers site

@inproceedings{hauberg:eccv:2010,
  title = {Gaussian-like Spatial Priors for Articulated Tracking},
  author = {Hauberg, S{\o}ren and Sommer, Stefan and Pedersen, Kim S.},
  booktitle = {Computer Vision - ECCV 2010},
  editor = {Daniilidis, Kostas and Maragos, Petros and Paragios, Nikos},
  publisher = {Springer Berlin Heidelberg},
  series = {Lecture Notes in Computer Science},
  volume = {6311},
  pages = {425--437},
  year = {2010}
}

Manifold Valued Statistics, Exact Principal Geodesic Analysis and the Effect of Linear Approximations
Stefan Sommer, Françous Lauze, Søren Hauberg, and Mads Nielsen.
In Computer Vision - ECCV 2010, Springer Berlin Heidelberg, volume 6316, pages 43-56, 2010.
PDF Publishers site

@inproceedings{sommer:eccv:2010,
  title = {Manifold Valued Statistics, Exact Principal Geodesic Analysis and the Effect of Linear Approximations},
  author = {Sommer, Stefan and Lauze, Francois and Hauberg, S{\o}ren and Nielsen, Mads},
  booktitle = {Computer Vision - ECCV 2010},
  editor = {Daniilidis, Kostas and Maragos, Petros and Paragios, Nikos},
  publisher = {Springer Berlin Heidelberg},
  volume = {6316},
  pages = {43--56},
  year = {2010}
}

Dense Marker-less Three Dimensional Motion Capture
Søren Hauberg, Bente R. Jensen, Morten Engell-Nørregård, Kenny Erleben, and Kim S. Pedersen.
In Virtual Vistas; Eleventh International Symposium on the 3D Analysis of Human Movement, 2010.

@inproceedings{hauberg:vista:2010,
  title = {Dense Marker-less Three Dimensional Motion Capture},
  author = {Hauberg, S{\o}ren and Jensen, Bente Rona and Engell-N{\o}rreg{\aa}rd, Morten and Erleben, Kenny and Pedersen, Kim S.},
  booktitle = {Virtual Vistas; Eleventh International Symposium on the 3D Analysis of Human Movement},
  year = {2010}
}

2009

Three Dimensional Monocular Human Motion Analysis in End-Effector Space
Søren Hauberg, Jerome Lapuyade, Morten Engell-Nørregård, Kenny Erleben, and Kim S. Pedersen.
In Energy Minimization Methods in Computer Vision and Pattern Recognition, Springer Berlin Heidelberg, volume 5681, Lecture Notes in Computer Science, pages 235-248, 2009.
PDF Paper site Publishers site

@inproceedings{hauberg:emmcvpr:2009,
  title = {Three Dimensional Monocular Human Motion Analysis in End-Effector Space},
  author = {Hauberg, S{\o}ren and Lapuyade, Jerome and Engell-N{\o}rreg{\aa}rd, Morten and Erleben, Kenny and Pedersen, Kim S.},
  booktitle = {Energy Minimization Methods in Computer Vision and Pattern Recognition},
  editor = {Cremers, Daniel and Boykov, Yuri and Blake, Andrew and Schmidt, Frank},
  publisher = {Springer Berlin Heidelberg},
  series = {Lecture Notes in Computer Science},
  volume = {5681},
  pages = {235-248},
  year = {2009}
}

Interactive Inverse Kinematics for Monocular Motion Estimation
Morten Engell-Nørregård, Søren Hauberg, Jerome Lapuyade,Kenny Erleben, and Kim S. Pedersen.
In The 6th Workshop on Virtual Reality Interaction and Physical Simulation (VRIPHYS), 2009.
Conference site Paper site

@inproceedings{norregard:vriphys:2009,
  title = {Interactive Inverse Kinematics for Monocular Motion Estimation},
  author = {Engell-N{\o}rreg{\aa}rd, Morten and Hauberg, S{\o}ren and Lapuyade, Jerome and Erleben, Kenny and Pedersen, Kim S.},
  booktitle = {The 6th Workshop on Virtual Reality Interaction and Physical Simulation (VRIPHYS)},
  year = {2009}
}

2008

An Efficient Algorithm for Modelling Duration in Hidden Markov Models, with a Dramatic Application
Søren Hauberg and Jakob Sloth.
Journal of Mathematical Imaging and Vision, 2008.
PDF Paper site Publishers site

@article{hauberg:jmiv:2008,
  title = { An Efficient Algorithm for Modelling Duration in Hidden Markov Models, with a Dramatic Application},
  author = {Hauberg, S{\o}ren and Sloth, Jakob},
  publisher = {Springer Netherlands},
  volume = {31},
  pages = {165--170},
  journal = {Journal of Mathematical Imaging and Vision},
  year = {2008}
}

Brownian Warps for Non-Rigid Registration
Mads Nielsen, Peter Johansen, Andrew Jackson, Benny Lautrup, and Søren Hauberg.
Journal of Mathematical Imaging and Vision, 2008.
PDF Publishers site

@article{nielsen:jmiv:2008,
  title = {Brownian Warps for Non-Rigid Registration},
  author = {Nielsen, Mads and Johansen, Peter and Jackson, Andrew and Lautrup, Benny and Hauberg, S{\o}ren},
  publisher = {Springer Netherlands},
  volume = {31},
  pages = {221--231},
  journal = {Journal of Mathematical Imaging and Vision},
  year = {2008}
}

GNU Octave Manual Version 3
John W. Eaton, David Bateman, and Søren Hauberg.
Network Theory Ltd., 2008.
Publishers site GNU Octave

@book{eaton:octave:2008,
  title = {GNU Octave Manual Version 3},
  author = {Eaton, John W. and Bateman, David and Hauberg, S{\o}ren},
  publisher = {Network Theory Ltd.},
  month = oct,
  year = {2008}
}

Publications