Gunnar Rätsch

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

Authors are matched by name, so researchers who share a name ("Yang Liu", "Wei Wang") appear as a single profile and their paper counts, venues and achievements are combined. How identity is handled.

Achievements

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🗺️ Taxonomy Completionist (51) 🏃 Academic Marathon · Ultra 🌍 Conference Polyglot · Silver 🌉 Interdisciplinary Bridge · Silver 🐣 Hot Topic Early Bird

🌱 Topic Pioneer 🌟 Keyword Trendsetter Combo (3) 🔬 Deep Specialist (10) 🏆 Keyword Champion 🧭 Keyword Pioneer 🚀 Conference Pioneer 🔥 Unstoppable · Bronze 💎 Century Club · Bronze 🗃️ Keyword Collector (130) 🌈 Renaissance Researcher (6) 📈 Trend Setter

Venues

ICLR (6) ICML (5) NeurIPS (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

Machine Learning (25) Optimization & Theory (14) Learning Types · Machine Learning (11) Core Methods (10) Deep Learning (8) Application Areas (5) Healthcare & Medicine (4) Learning Types · Deep Learning (3) Models (3) Research (3) Artificial Intelligence (2) Interdisciplinary (2) Mathematics & Optimization (2) Reinforcement Learning (2) Architectures (2)

Keywords

marginal likelihood (4) variational autoencoder (3) bayesian inference (3) support vector machine (3) laplace approximation (3) binary classification (2) model selection (2) neural network (2) offline reinforcement learning (2) ensemble learning (2) stochastic gradient (2) kernel methods (2) bayesian model selection (2) convex optimization (2) hyperparameter optimization (2) multiple kernel learning (2) domain generalization (1) contrastive learning (1) online learning (1) neural tangent kernel (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 Delphic Offline Reinforcement Learning under Nonidentifiable Hidden Confounding ICLR 2024 Improving Neural Additive Models with Bayesian Principles ICML 2024 Towards Training Without Depth Limits: Batch Normalization Without Gradient Explosion ICLR 2024 Stochastic Marginal Likelihood Gradients using Neural Tangent Kernels ICML 2023 Temporal Label Smoothing for Early Event Prediction ICML 2023 Bayesian Neural Network Priors Revisited ICLR 2022 Faster One-Sample Stochastic Conditional Gradient Method for Composite Convex Minimization AISTATS 2022 Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations NeurIPS 2022 Boosting Variational Inference With Locally Adaptive Step-Sizes IJCAI 2021 Neighborhood Contrastive Learning Applied to Online Patient Monitoring ICML 2021 Scalable Gaussian Process Variational Autoencoders AISTATS 2021 Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning 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 NeurIPS 2011 The SHOGUN Machine Learning Toolbox JMLR 2010 An Empirical Analysis of Domain Adaptation Algorithms for Genomic Sequence Analysis NeurIPS 2008 Boosting Algorithms for Maximizing the Soft Margin NeurIPS 2007 Large Scale Hidden Semi-Markov SVMs NeurIPS 2006 Large Scale Multiple Kernel Learning JMLR 2006 Efficient Margin Maximizing with Boosting JMLR 2005 Matrix Exponentiated Gradient Updates for On-line Learning and Bregman Projection JMLR 2005