← Authors

Søren Hauberg

32 papers · 2012–2025 · 10 conferences · across top CS/AI conferences

Achievements

🐣 Hot Topic Early Bird 🌍 Conference Polyglot (10) 🏃 Academic Marathon (13) 🌉 Interdisciplinary Bridge 🐝 Cross-Pollinator (14) 🗺️ Taxonomy Completionist (10) 🧭 Keyword Pioneer 👑 Triple Crown 🗃️ Keyword Collector (101) ⚡ Prolific Year (7) 🚀 Conference Pioneer 💎 Century Club (32) 🔥 Unstoppable (5) 📈 Trend Setter ❓ The Questioner

Conferences

NIPS (15) AISTATS (5) ICML (4) ICLR (2) COLT (1) CVPR (1) ICCV (1) MICCAI (1) RSS (1) UAI (1)

Top co-authors

Marco Miani (6) Pablo Moreno-Muñoz (5) Georgios Arvanitidis (5) Frederik Warburg (5) Leonel Rozo (4) Nicki Skafte (3) Jes Frellsen (3) Martin Jørgensen (3) Oren Freifeld (3) Hrittik Roy (3)

Keywords

variational inference (5) riemannian manifold (5) uncertainty quantification (5) generative model (4) bayesian inference (4) variational autoencoder (4) laplace approximation (4) gaussian process (4) metric learning (3) bayesian neural network (3) density estimation (3) model calibration (2) out-of-distribution detection (2) representation learning (2) image retrieval (2) riemannian metric (2) manifold learning (2) data augmentation (2) dimensionality reduction (2) image transformation (2)

Papers

Riemann$^2$: Learning Riemannian Submanifolds from Riemannian Data AISTATS 2025
Bayes without Underfitting: Fully Correlated Deep Learning Posteriors via Alternating Projections AISTATS 2025
Geometric Contact Flows: Contactomorphisms for Dynamics and Control ICML 2025
Identifying Metric Structures of Deep Latent Variable Models ICML 2025
Improving Adversarial Energy-Based Model via Diffusion Process ICML 2024
Laplacian Segmentation Networks Improve Epistemic Uncertainty Quantification MICCAI 2024
Reparameterization invariance in approximate Bayesian inference NIPS 2024
Sketched Lanczos uncertainty score: a low-memory summary of the Fisher information NIPS 2024
Gradients of Functions of Large Matrices NIPS 2024
A survey and benchmark of high-dimensional Bayesian optimization of discrete sequences NIPS 2024
Neural Contractive Dynamical Systems ICLR 2024
Learning to Taste: A Multimodal Wine Dataset NIPS 2023
Bayesian Metric Learning for Uncertainty Quantification in Image Retrieval NIPS 2023
Riemannian Laplace approximations for Bayesian neural networks NIPS 2023
On Masked Pre-training and the Marginal Likelihood NIPS 2023
Laplacian Autoencoders for Learning Stochastic Representations NIPS 2022
Revisiting Active Sets for Gaussian Process Decoders NIPS 2022
Probabilistic spatial transformer networks UAI 2022
Bounds all around: training energy-based models with bidirectional bounds NIPS 2021
Hierarchical VAEs Know What They Don’t Know ICML 2021
Learning Riemannian Manifolds for Geodesic Motion Skills RSS 2021
Bayesian Triplet Loss: Uncertainty Quantification in Image Retrieval ICCV 2021
Reliable training and estimation of variance networks NIPS 2019
Probabilistic Riemannian submanifold learning with wrapped Gaussian process latent variable models AISTATS 2019
Explicit Disentanglement of Appearance and Perspective in Generative Models NIPS 2019
Deep Diffeomorphic Transformer Networks CVPR 2018
Latent Space Oddity: on the Curvature of Deep Generative Models ICLR 2018
Dreaming More Data: Class-dependent Distributions over Diffeomorphisms for Learned Data Augmentation AISTATS 2016
A Locally Adaptive Normal Distribution NIPS 2016
Open Problem: Kernel methods on manifolds and metric spaces. What is the probability of a positive definite geodesic exponential kernel? COLT 2016
Probabilistic Solutions to Differential Equations and their Application to Riemannian Statistics AISTATS 2014
A Geometric take on Metric Learning NIPS 2012