Rätsch, Gunnar

72 publications

WACV 2025 Generalizable Single-Source Cross-Modality Medical Image Segmentation via Invariant Causal Mechanisms Boqi Chen, Yuanzhi Zhu, Yunke Ao, Sebastiano Caprara, Reto Sutter, Gunnar Rätsch, Ender Konukoglu, Anna Susmelj

ICLR 2025 Preference Elicitation for Offline Reinforcement Learning Alizée Pace, Bernhard Schölkopf, Gunnar Ratsch, Giorgia Ramponi

ICLR 2024 Delphic Offline Reinforcement Learning Under Nonidentifiable Hidden Confounding Alizée Pace, Hugo Yèche, Bernhard Schölkopf, Gunnar Ratsch, Guy Tennenholtz

CHIL 2024 Dynamic Survival Analysis for Early Event Prediction Hugo Yèche, Manuel Burger, Dinara Veshchezerova, Gunnar Ratsch

CHIL 2024 FAMEWS: A Fairness Auditing Tool for Medical Early-Warning Systems Marine Hoche, Olga Mineeva, Manuel Burger, Alessandro Blasimme, Gunnar Ratsch

ICMLW 2024 Identifying Biological Priors and Structure in Single-Cell Foundation Models Flavia Pedrocchi, Stefan Stark, Gunnar Ratsch, Amir Joudaki

ICML 2024 Improving Neural Additive Models with Bayesian Principles Kouroche Bouchiat, Alexander Immer, Hugo Yèche, Gunnar Ratsch, Vincent Fortuin

ICMLW 2024 Preference Elicitation for Offline Reinforcement Learning Alizée Pace, Bernhard Schölkopf, Gunnar Ratsch, Giorgia Ramponi

ICMLW 2024 Preference Elicitation for Offline Reinforcement Learning Alizée Pace, Bernhard Schölkopf, Gunnar Ratsch, Giorgia Ramponi

ICMLW 2024 Towards Dynamic Feature Acquisition on Medical Time Series by Maximizing Conditional Mutual Information Fedor Sergeev, Paola Malsot, Gunnar Ratsch, Vincent Fortuin

NeurIPSW 2024 Towards Foundation Models for Critical Care Time Series Manuel Burger, Fedor Sergeev, Malte Londschien, Daphné Chopard, Hugo Yèche, Eike Christian Gerdes, Polina Leshetkina, Alexander Morgenroth, Zeynep Babür, Jasmina Bogojeska, Martin Faltys, Rita Kuznetsova, Gunnar Ratsch

NeurIPSW 2024 Towards Large-Scale Clinical Multi-Variate Time-Series Datasets Manuel Burger, Fedor Sergeev, Malte Londschien, Daphné Chopard, Hugo Yèche, Eike Christian Gerdes, Polina Leshetkina, Alexander Morgenroth, Zeynep Babür, Jasmina Bogojeska, Martin Faltys, Rita Kuznetsova, Gunnar Ratsch

ICLR 2024 Towards Training Without Depth Limits: Batch Normalization Without Gradient Explosion Alexandru Meterez, Amir Joudaki, Francesco Orabona, Alexander Immer, Gunnar Ratsch, Hadi Daneshmand

NeurIPSW 2024 Uncertainty-Penalized Direct Preference Optimization Sam Houliston, Alizée Pace, Alexander Immer, Gunnar Ratsch

NeurIPSW 2024 Uncertainty-Penalized Direct Preference Optimization Sam Houliston, Alizée Pace, Alexander Immer, Gunnar Ratsch

ICLRW 2023 Clinical Trajectory Representations for Clustering Haobo Li, Alizée Pace, Martin Faltys, Gunnar Ratsch

ICMLW 2023 Delphic Offline Reinforcement Learning Under Nonidentifiable Hidden Confounding Alizée Pace, Hugo Yèche, Bernhard Schölkopf, Gunnar Ratsch, Guy Tennenholtz

NeurIPSW 2023 Learning Genomic Sequence Representations Using Graph Neural Networks over De Bruijn Graphs Kacper Kapusniak, Manuel Burger, Gunnar Ratsch, Amir Joudaki

NeurIPSW 2023 Multi-V-Stain: Multiplexed Virtual Staining of Histopathology Whole-Slide Images Sonali Andani, Boqi Chen, Joanna Ficek-Pascual, Simon Heinke, Ruben Casanova, Bettina Sobottka, Bernd Bodenmiller, Viktor Koelzer, Gunnar Ratsch

ICML 2023 Stochastic Marginal Likelihood Gradients Using Neural Tangent Kernels Alexander Immer, Tycho F. A. Van Der Ouderaa, Mark Van Der Wilk, Gunnar Ratsch, Bernhard Schölkopf

ICML 2023 Temporal Label Smoothing for Early Event Prediction Hugo Yèche, Alizée Pace, Gunnar Ratsch, Rita Kuznetsova

AISTATS 2022 Faster One-Sample Stochastic Conditional Gradient Method for Composite Convex Minimization Gideon Dresdner, Maria-Luiza Vladarean, Gunnar Rätsch, Francesco Locatello, Volkan Cevher, Alp Yurtsever

ICLR 2022 Bayesian Neural Network Priors Revisited Vincent Fortuin, Adrià Garriga-Alonso, Sebastian W. Ober, Florian Wenzel, Gunnar Ratsch, Richard E Turner, Mark van der Wilk, Laurence Aitchison

NeurIPS 2022 Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations Alexander Immer, Tycho van der Ouderaa, Gunnar Rätsch, Vincent Fortuin, Mark van der Wilk

NeurIPSW 2022 On the Importance of Clinical Notes in Multi-Modal Learning for EHR Data Severin Husmann, Hugo Yèche, Gunnar Ratsch, Rita Kuznetsova

AISTATS 2021 Scalable Gaussian Process Variational Autoencoders Metod Jazbec, Matt Ashman, Vincent Fortuin, Michael Pearce, Stephan Mandt, Gunnar Rätsch

IJCAI 2021 Boosting Variational Inference with Locally Adaptive Step-Sizes Gideon Dresdner, Saurav Shekhar, Fabian Pedregosa, Francesco Locatello, Gunnar Rätsch

ICML 2021 Neighborhood Contrastive Learning Applied to Online Patient Monitoring Hugo Yèche, Gideon Dresdner, Francesco Locatello, Matthias Hüser, Gunnar Rätsch

ICML 2021 Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning Alexander Immer, Matthias Bauer, Vincent Fortuin, Gunnar Rätsch, Khan Mohammad Emtiyaz

AAAI 2020 A Commentary on the Unsupervised Learning of Disentangled Representations Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem

NeurIPSW 2020 Bayesian Neural Network Priors Revisited Vincent Fortuin, Adrià Garriga-Alonso, Florian Wenzel, Gunnar Ratsch, Richard E Turner, Mark van der Wilk, Laurence Aitchison

ICLR 2020 Disentangling Factors of Variations Using Few Labels Francesco Locatello, Michael Tschannen, Stefan Bauer, Gunnar Rätsch, Bernhard Schölkopf, Olivier Bachem

ICLR 2019 SOM-VAE: Interpretable Discrete Representation Learning on Time Series Vincent Fortuin, Matthias Hüser, Francesco Locatello, Heiko Strathmann, Gunnar Rätsch

AISTATS 2018 Boosting Variational Inference: An Optimization Perspective Francesco Locatello, Rajiv Khanna, Joydeep Ghosh, Gunnar Rätsch

IJCAI 2018 Predicting Circulatory System Deterioration in Intensive Care Unit Patients Stephanie L. Hyland, Matthias Hüser, Xinrui Lyu, Martin Faltys, Tobias Merz, Gunnar Rätsch

AAAI 2017 Learning Unitary Operators with Help from U(n) Stephanie L. Hyland, Gunnar Rätsch

AAAI 2016 A Generative Model of Words and Relationships from Multiple Sources Stephanie L. Hyland, Theofanis Karaletsos, Gunnar Rätsch

ICLR 2016 When Crowds Hold Privileges: Bayesian Unsupervised Representation Learning with Oracle Constraints Theofanis Karaletsos, Serge J. Belongie, Gunnar Rätsch

ECML-PKDD 2015 Opening the Black Box: Revealing Interpretable Sequence Motifs in Kernel-Based Learning Algorithms Marina M.-C. Vidovic, Nico Görnitz, Klaus-Robert Müller, Gunnar Rätsch, Marius Kloft

MLJ 2015 Probabilistic Clustering of Time-Evolving Distance Data Julia E. Vogt, Marius Kloft, Stefan Stark, Sudhir Raman, Sandhya Prabhakaran, Volker Roth, Gunnar Rätsch

ECML-PKDD 2012 Efficient Training of Graph-Regularized Multitask SVMs Christian Widmer, Marius Kloft, Nico Görnitz, Gunnar Rätsch

NeurIPS 2011 Hierarchical Multitask Structured Output Learning for Large-Scale Sequence Segmentation Nico Goernitz, Christian K. Widmer, Georg Zeller, Andre Kahles, Gunnar Rätsch, Sören Sonnenburg

MLOSS 2010 The SHOGUN Machine Learning Toolbox Sören Sonnenburg, Gunnar Rätsch, Sebastian Henschel, Christian Widmer, Jonas Behr, Alexander Zien, Fabio de Bona, Alexander Binder, Christian Gehl, Vojtěch Franc

ECML-PKDD 2009 The Feature Importance Ranking Measure Alexander Zien, Nicole Krämer, Sören Sonnenburg, Gunnar Rätsch

NeurIPS 2008 An Empirical Analysis of Domain Adaptation Algorithms for Genomic Sequence Analysis Gabriele Schweikert, Gunnar Rätsch, Christian K. Widmer, Bernhard Schölkopf

NeurIPS 2007 Boosting Algorithms for Maximizing the Soft Margin Gunnar Rätsch, Manfred K. Warmuth, Karen A. Glocer

JMLR 2007 The Need for Open Source Software in Machine Learning Sören Sonnenburg, Mikio L. Braun, Cheng Soon Ong, Samy Bengio, Leon Bottou, Geoffrey Holmes, Yann LeCun, Klaus-Robert Müller, Fernando Pereira, Carl Edward Rasmussen, Gunnar Rätsch, Bernhard Schölkopf, Alexander Smola, Pascal Vincent, Jason Weston, Robert Williamson

ECML-PKDD 2006 Graph Based Semi-Supervised Learning with Sharper Edges Hyunjung Shin, N. Jeremy Hill, Gunnar Rätsch

NeurIPS 2006 Large Scale Hidden Semi-Markov SVMs Gunnar Rätsch, Sören Sonnenburg

JMLR 2006 Large Scale Multiple Kernel Learning Sören Sonnenburg, Gunnar Rätsch, Christin Schäfer, Bernhard Schölkopf

ALT 2006 Solving Semi-Infinite Linear Programs Using Boosting-like Methods Gunnar Rätsch

ICML 2006 Totally Corrective Boosting Algorithms That Maximize the Margin Manfred K. Warmuth, Jun Liao, Gunnar Rätsch

NeurIPS 2005 A General and Efficient Multiple Kernel Learning Algorithm Sören Sonnenburg, Gunnar Rätsch, Christin Schäfer

JMLR 2005 Efficient Margin Maximizing with Boosting Gunnar Rätsch, Manfred K. Warmuth

ICML 2005 Large Scale Genomic Sequence SVM Classifiers Sören Sonnenburg, Gunnar Rätsch, Bernhard Schölkopf

JMLR 2005 Matrix Exponentiated Gradient Updates for On-Line Learning and Bregman Projection Koji Tsuda, Gunnar Rätsch, Manfred K. Warmuth

NeurIPS 2004 Matrix Exponential Gradient Updates for On-Line Learning and Bregman Projection Koji Tsuda, Gunnar Rätsch, Manfred K. Warmuth

NeurIPS 2003 Image Reconstruction by Linear Programming Koji Tsuda, Gunnar Rätsch

NeCo 2002 A New Discriminative Kernel from Probabilistic Models Koji Tsuda, Motoaki Kawanabe, Gunnar Rätsch, Sören Sonnenburg, Klaus-Robert Müller

NeurIPS 2002 Adapting Codes and Embeddings for Polychotomies Gunnar Rätsch, Sebastian Mika, Alex J. Smola

COLT 2002 Maximizing the Margin with Boosting Gunnar Rätsch, Manfred K. Warmuth

MLJ 2002 Sparse Regression Ensembles in Infinite and Finite Hypothesis Spaces Gunnar Rätsch, Ayhan Demiriz, Kristin P. Bennett

NeurIPS 2001 A New Discriminative Kernel from Probabilistic Models Koji Tsuda, Motoaki Kawanabe, Gunnar Rätsch, Sören Sonnenburg, Klaus-Robert Müller

NeurIPS 2001 Active Learning in the Drug Discovery Process Manfred K. Warmuth, Gunnar Rätsch, Michael Mathieson, Jun Liao, Christian Lemmen

NeurIPS 2001 On the Convergence of Leveraging Gunnar Rätsch, Sebastian Mika, Manfred K. Warmuth

MLJ 2001 Soft Margins for AdaBoost Gunnar Rätsch, Takashi Onoda, Klaus-Robert Müller

NeurIPS 2000 A Mathematical Programming Approach to the Kernel Fisher Algorithm Sebastian Mika, Gunnar Rätsch, Klaus-Robert Müller

COLT 2000 Barrier Boosting Gunnar Rätsch, Manfred K. Warmuth, Sebastian Mika, Takashi Onoda, Steven Lemm, Klaus-Robert Müller

NeurIPS 1999 Invariant Feature Extraction and Classification in Kernel Spaces Sebastian Mika, Gunnar Rätsch, Jason Weston, Bernhard Schölkopf, Alex J. Smola, Klaus-Robert Müller

NeurIPS 1999 V-Arc: Ensemble Learning in the Presence of Outliers Gunnar Rätsch, Bernhard Schölkopf, Alex J. Smola, Klaus-Robert Müller, Takashi Onoda, Sebastian Mika

NeurIPS 1998 Kernel PCA and De-Noising in Feature Spaces Sebastian Mika, Bernhard Schölkopf, Alex J. Smola, Klaus-Robert Müller, Matthias Scholz, Gunnar Rätsch

NeurIPS 1998 Regularizing AdaBoost Gunnar Rätsch, Takashi Onoda, Klaus R. Müller