← Authors

Gunnar Rätsch

26 papers · 2005–2025 · 8 conferences · across top CS/AI conferences

Achievements

🏃 Academic Marathon (20) 🐝 Cross-Pollinator (13) 🌍 Conference Polyglot (8) 🗺️ Taxonomy Completionist (14) 🌈 Renaissance Researcher (7) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🌉 Interdisciplinary Bridge 🌱 Topic Pioneer 🏆 Keyword Champion 🗃️ Keyword Collector (90) 📈 Trend Setter 💎 Century Club (26) 🔥 Unstoppable (8) 🚀 Conference Pioneer

Conferences

ICLR (6) ICML (5) NIPS (5) JMLR (4) AISTATS (3) IJCAI (1) MICCAI (1) WACV (1)

Top co-authors

Bernhard Schölkopf (6) Vincent Fortuin (6) Francesco Locatello (6) Alexander Immer (5) Hugo Yèche (4) Sören Sonnenburg (4) Alizée Pace (3) Manfred K. Warmuth (3) Gideon Dresdner (3) Mark van der Wilk (3)

Research topics

Optimization & Theory (1)

Keywords

marginal likelihood (3) laplace approximation (3) bayesian inference (3) support vector machine (3) model selection (2) stochastic gradient (2) convex optimization (2) kernel methods (2) binary classification (2) bayesian model selection (2) multiple kernel learning (2) hyperparameter optimization (2) ensemble learning (2) domain adaptation (1) transfer learning (1) online learning (1) semidefinite programming (1) convergence analysis (1) variational inference (1) contrastive learning (1)

Papers

Generalizable Single-Source Cross-Modality Medical Image Segmentation via Invariant Causal Mechanisms WACV 2025
Preference Elicitation for Offline Reinforcement Learning ICLR 2025
Revisiting Automatic Data Curation for Vision Foundation Models in Digital Pathology MICCAI 2025
Towards Training Without Depth Limits: Batch Normalization Without Gradient Explosion ICLR 2024
Improving Neural Additive Models with Bayesian Principles ICML 2024
Delphic Offline Reinforcement Learning under Nonidentifiable Hidden Confounding ICLR 2024
Temporal Label Smoothing for Early Event Prediction ICML 2023
Stochastic Marginal Likelihood Gradients using Neural Tangent Kernels ICML 2023
Faster One-Sample Stochastic Conditional Gradient Method for Composite Convex Minimization AISTATS 2022
Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations NIPS 2022
Bayesian Neural Network Priors Revisited ICLR 2022
Boosting Variational Inference With Locally Adaptive Step-Sizes IJCAI 2021
Scalable Gaussian Process Variational Autoencoders AISTATS 2021
Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning ICML 2021
Neighborhood Contrastive Learning Applied to Online Patient Monitoring ICML 2021
Disentangling Factors of Variations Using Few Labels ICLR 2020
SOM-VAE: Interpretable Discrete Representation Learning on Time Series ICLR 2019
Boosting Variational Inference: an Optimization Perspective AISTATS 2018
Hierarchical Multitask Structured Output Learning for Large-scale Sequence Segmentation NIPS 2011
The SHOGUN Machine Learning Toolbox JMLR 2010
An Empirical Analysis of Domain Adaptation Algorithms for Genomic Sequence Analysis NIPS 2008
Boosting Algorithms for Maximizing the Soft Margin NIPS 2007
Large Scale Multiple Kernel Learning JMLR 2006
Large Scale Hidden Semi-Markov SVMs NIPS 2006
Efficient Margin Maximizing with Boosting JMLR 2005
Matrix Exponentiated Gradient Updates for On-line Learning and Bregman Projection JMLR 2005