Søren Hauberg

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

Achievements

+10 more ↓

🐣 Hot Topic Early Bird 🧭 Keyword Pioneer 🗺️ Taxonomy Completionist (10) 🌉 Interdisciplinary Bridge 🌍 Conference Polyglot (10)

🗺️ 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