Predicting Time Series with Support Vector Machines. - Klaus-Robert Müller; Alexander J. Smola; Gunnar Rätsch; Bernhard Schölkopf; Jens Kohlmorgen; Vladimir Vapnik (ICANN, 1997)

An Improvement of AdaBoost to Avoid Overfitting. - Gunnar Rätsch; Takashi Onoda; Klaus-Robert Müller (ICONIP, 1998)

Kernel PCA and De-Noising in Feature Spaces. - Sebastian Mika; Bernhard Schölkopf; Alexander J. Smola; Klaus-Robert Müller; Matthias Scholz; Gunnar Rätsch (NIPS, 1998)

Regularizing AdaBoost. - Gunnar Rätsch; Takashi Onoda; Klaus-Robert Müller (NIPS, 1998)

Engineering Support Vector Machine Kerneis That Recognize Translation Initialion Sites. - Alexander Zien; Gunnar Rätsch; Sebastian Mika; Bernhard Schölkopf; Christian Lemmen; Alexander J. Smola; Thomas Lengauer; Klaus-Robert Müller (German Conference on Bioinformatics, 1999)

Invariant Feature Extraction and Classification in Kernel Spaces. - Sebastian Mika; Gunnar Rätsch; Jason Weston; Bernhard Schölkopf; Alexander J. Smola; Klaus-Robert Müller (NIPS, 1999)

v-Arc: Ensemble Learning in the Presence of Outliers. - Gunnar Rätsch; Bernhard Schölkopf; Alexander J. Smola; Klaus-Robert Müller; Takashi Onoda; Sebastian Mika (NIPS, 1999)

Input space versus feature space in kernel-based methods. - Bernhard Schölkopf; Sebastian Mika; Christopher J. C. Burges; Phil Knirsch; Klaus-Robert Müller; Gunnar Rätsch; Alexander J. Smola (IEEE Trans. Neural Networks, 1999)

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

A Mathematical Programming Approach to the Kernel Fisher Algorithm. - Sebastian Mika; Gunnar Rätsch; Klaus-Robert Müller (NIPS, 2000)

Robust Ensemble Learning for Data Mining. - Gunnar Rätsch; Bernhard Schölkopf; Alexander J. Smola; Sebastian Mika; Takashi Onoda; Klaus-Robert Müller (PAKDD, 2000)

Engineering support vector machine kernels that recognize translation initiation sites. - Alexander Zien; Gunnar Rätsch; Sebastian Mika; Bernhard Schölkopf; Thomas Lengauer; Klaus-Robert Müller (Bioinform., 2000)

Robustes Boosting durch konvexe Optimierung. - Gunnar Rätsch (Ausgezeichnete Informatikdissertationen, 2001)

Learning to Predict the Leave-One-Out Error of Kernel Based Classifiers. - Koji Tsuda; Gunnar Rätsch; Sebastian Mika; Klaus-Robert Müller (ICANN, 2001)

On the Convergence of Leveraging. - Gunnar Rätsch; Sebastian Mika; Manfred K. Warmuth (NIPS, 2001)

Active Learning in the Drug Discovery Process. - Manfred K. Warmuth; Gunnar Rätsch; Michael Mathieson; Jun Liao; Christian Lemmen (NIPS, 2001)

Soft Margins for AdaBoost. - Gunnar Rätsch; Takashi Onoda; Klaus-Robert Müller (Mach. Learn., 2001)

An introduction to kernel-based learning algorithms. - Klaus-Robert Müller; Sebastian Mika; Gunnar Rätsch; Koji Tsuda; Bernhard Schölkopf (IEEE Trans. Neural Networks, 2001)

Maximizing the Margin with Boosting. - Gunnar Rätsch; Manfred K. Warmuth (COLT, 2002)

New Methods for Splice Site Recognition. - Sören Sonnenburg; Gunnar Rätsch; Arun K. Jagota; Klaus-Robert Müller (ICANN, 2002)

An Introduction to Boosting and Leveraging. - Ron Meir; Gunnar Rätsch (Machine Learning Summer School, 2002)

Adapting Codes and Embeddings for Polychotomies. - Gunnar Rätsch; Alexander J. Smola; Sebastian Mika (NIPS, 2002)

Sparse Regression Ensembles in Infinite and Finite Hypothesis Spaces. - Gunnar Rätsch; Ayhan Demiriz; Kristin P. Bennett (Mach. Learn., 2002)

A New Discriminative Kernel from Probabilistic Models. - Koji Tsuda; Motoaki Kawanabe; Gunnar Rätsch; Sören Sonnenburg; Klaus-Robert Müller (Neural Comput., 2002)

Constructing Boosting Algorithms from SVMs: An Application to One-Class Classification. - Gunnar Rätsch; Sebastian Mika; Bernhard Schölkopf; Klaus-Robert Müller (IEEE Trans. Pattern Anal. Mach. Intell., 2002)

Robust multi-class boosting. - Gunnar Rätsch (INTERSPEECH, 2003)

Active Learning with Support Vector Machines in the Drug Discovery Process. - Manfred K. Warmuth; Jun Liao; Gunnar Rätsch; Michael Mathieson; Santosh Putta; Christian Lemmen (J. Chem. Inf. Comput. Sci., 2003)

Constructing Descriptive and Discriminative Nonlinear Features: Rayleigh Coefficients in Kernel Feature Spaces. - Sebastian Mika; Gunnar Rätsch; Jason Weston; Bernhard Schölkopf; Alexander J. Smola; Klaus-Robert Müller (IEEE Trans. Pattern Anal. Mach. Intell., 2003)

Matrix Exponential Gradient Updates for On-line Learning and Bregman Projection. - Koji Tsuda; Gunnar Rätsch; Manfred K. Warmuth (NIPS, 2004)

Large scale genomic sequence SVM classifiers. - Sören Sonnenburg; Gunnar Rätsch; Bernhard Schölkopf (ICML, 2005)

RASE: recognition of alternatively spliced exons in C.elegans. - Gunnar Rätsch; Sören Sonnenburg; Bernhard Schölkopf (ISMB (Supplement of Bioinformatics), 2005)

A General and Efficient Multiple Kernel Learning Algorithm. - Sören Sonnenburg; Gunnar Rätsch; Christin Schäfer (NIPS, 2005)

Classifying 'Drug-likeness' with Kernel-Based Learning Methods. - Klaus-Robert Müller; Gunnar Rätsch; Sören Sonnenburg; Sebastian Mika; Michael Grimm; Nikolaus Heinrich (J. Chem. Inf. Model., 2005)

Efficient Margin Maximizing with Boosting. - Gunnar Rätsch; Manfred K. Warmuth (J. Mach. Learn. Res., 2005)

Matrix Exponentiated Gradient Updates for On-line Learning and Bregman Projection. - Koji Tsuda; Gunnar Rätsch; Manfred K. Warmuth (J. Mach. Learn. Res., 2005)

Image reconstruction by linear programming. - Koji Tsuda; Gunnar Rätsch (IEEE Trans. Image Process., 2005)

Robust boosting via convex optimization - Gunnar Rätsch (2005)

Solving Semi-infinite Linear Programs Using Boosting-Like Methods. - Gunnar Rätsch (ALT, 2006)

The Solution of Semi-Infinite Linear Programs Using Boosting-Like Methods. - Gunnar Rätsch (Discovery Science, 2006)

Graph Based Semi-supervised Learning with Sharper Edges. - Hyunjung Shin; N. Jeremy Hill; Gunnar Rätsch (ECML, 2006)

PALMA: Perfect Alignments using Large Margin Algorithms. - Gunnar Rätsch; Bettina Hepp; Uta Schulze; Cheng Soon Ong (German Conference on Bioinformatics, 2006)

Totally corrective boosting algorithms that maximize the margin. - Manfred K. Warmuth; Jun Liao; Gunnar Rätsch (ICML, 2006)

ARTS: accurate recognition of transcription starts in human. - Sören Sonnenburg; Alexander Zien; Gunnar Rätsch (ISMB (Supplement of Bioinformatics), 2006)

Large Scale Hidden Semi-Markov SVMs. - Gunnar Rätsch; Sören Sonnenburg (NIPS, 2006)

Learning Interpretable SVMs for Biological Sequence Classification. - Gunnar Rätsch; Sören Sonnenburg; Christin Schäfer (BMC Bioinform., 2006)

Large Scale Multiple Kernel Learning. - Sören Sonnenburg; Gunnar Rätsch; Christin Schäfer; Bernhard Schölkopf (J. Mach. Learn. Res., 2006)

Boosting Algorithms for Maximizing the Soft Margin. - Manfred K. Warmuth; Karen A. Glocer; Gunnar Rätsch (NIPS, 2007)

PALMA: mRNA to genome alignments using large margin algorithms. - Uta Schulze; Bettina Hepp; Cheng Soon Ong; Gunnar Rätsch (Bioinform., 2007)

NIPS workshop on New Problems and Methods in Computational Biology. - Gal Chechik; Christina S. Leslie; William Stafford Noble; Gunnar Rätsch; Quaid Morris; Koji Tsuda (BMC Bioinform., 2007)

Accurate splice site prediction using support vector machines. - Sören Sonnenburg; Gabriele Beate Schweikert; Petra Philips; Jonas Behr; Gunnar Rätsch (BMC Bioinform., 2007)

The Need for Open Source Software in Machine Learning. - Sören Sonnenburg; Mikio L. Braun; Cheng Soon Ong; Samy Bengio; Léon Bottou; Geoffrey Holmes 001; Yann LeCun; Klaus-Robert Müller; Fernando Pereira 003; Carl Edward Rasmussen; Gunnar Rätsch; Bernhard Schölkopf; et al. (J. Mach. Learn. Res., 2007)

Improving the Caenorhabditis elegans Genome Annotation Using Machine Learning. - Gunnar Rätsch; Sören Sonnenburg; Jagan Srinivasan; Hanh Witte; Klaus-Robert Müller; Ralf J. Sommer; Bernhard Schölkopf (PLoS Comput. Biol., 2007)

POIMs: positional oligomer importance matrices - understanding support vector machine-based signal detectors. - Sören Sonnenburg; Alexander Zien; Petra Philips; Gunnar Rätsch (ISMB, 2008)

An Empirical Analysis of Domain Adaptation Algorithms for Genomic Sequence Analysis. - Gabriele Beate Schweikert; Christian Widmer; Bernhard Schölkopf; Gunnar Rätsch (NIPS, 2008)

Transcript Normalization and Segmentation of Tiling Array Data. - Georg Zeller; Stefan R. Henz; Sascha Laubinger; Detlef Weigel; Gunnar Rätsch (Pacific Symposium on Biocomputing, 2008)

Revealing sequence variation patterns in rice with machine learning methods. - Regina Bohnert; Georg Zeller; Richard M. Clark; Kevin L. Childs; Victor Jun M. Ulat; Renee Stokowski; Dennis Ballinger; Kelly Frazer; David Cox; Richard M. Bruskiewich; C. Robin Buell; Jan Leach; et al. (BMC Bioinform., 2008)

Optimal spliced alignments of short sequence reads. - Fabio De Bona; Stephan Ossowski; Korbinian Schneeberger; Gunnar Rätsch (BMC Bioinform., 2008)

Support Vector Machines and Kernels for Computational Biology. - Asa Ben-Hur; Cheng Soon Ong; Sören Sonnenburg; Bernhard Schölkopf; Gunnar Rätsch (PLoS Comput. Biol., 2008)

The Feature Importance Ranking Measure. - Alexander Zien; Nicole Krämer 002; Sören Sonnenburg; Gunnar Rätsch (ECML/PKDD (2), 2009)

Transcript quantification with RNA-Seq data. - Regina Bohnert; Jonas Behr; Gunnar Rätsch (BMC Bioinform., 2009)

KIRMES: kernel-based identification of regulatory modules in euchromatic sequences. - Sebastian J. Schultheiß; Wolfgang Busch; Jan U. Lohmann; Oliver Kohlbacher; Gunnar Rätsch (BMC Bioinform., 2009)

mGene.web: a web service for accurate computational gene finding. - Gabriele Beate Schweikert; Jonas Behr; Alexander Zien; Georg Zeller; Cheng Soon Ong; Sören Sonnenburg; Gunnar Rätsch (Nucleic Acids Res., 2009)

Prototype Classification: Insights from Machine Learning. - Arnulf B. A. Graf; Olivier Bousquet; Gunnar Rätsch; Bernhard Schölkopf (Neural Comput., 2009)

Novel Machine Learning Methods for MHC Class I Binding Prediction. - Christian Widmer; Nora C. Toussaint; Yasemin Altun; Oliver Kohlbacher; Gunnar Rätsch (PRIB, 2010)

Leveraging Sequence Classification by Taxonomy-Based Multitask Learning. - Christian Widmer; Jose Leiva; Yasemin Altun; Gunnar Rätsch (RECOMB, 2010)

Next generation genome annotation with mGene.ngs. - Jonas Behr; Regina Bohnert; Georg Zeller; Gabriele Beate Schweikert; Lisa Hartmann; Gunnar Rätsch (BMC Bioinform., 2010)

Exploiting physico-chemical properties in string kernels. - Nora C. Toussaint; Christian Widmer; Oliver Kohlbacher; Gunnar Rätsch (BMC Bioinform., 2010)

Inferring latent task structure for Multitask Learning by Multiple Kernel Learning. - Christian Widmer; Nora C. Toussaint; Yasemin Altun; Gunnar Rätsch (BMC Bioinform., 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; Vojtech Franc (J. Mach. Learn. Res., 2010)

rQuant.web: a tool for RNA-Seq-based transcript quantitation. - Regina Bohnert; Gunnar Rätsch (Nucleic Acids Res., 2010)

Hierarchical Multitask Structured Output Learning for Large-scale Sequence Segmentation. - Nico Görnitz; Christian Widmer; Georg Zeller; André Kahles; Sören Sonnenburg; Gunnar Rätsch (NIPS, 2011)

Support vector machines-based identification of alternative splicing in Arabidopsis thaliana from whole-genome tiling arrays. - Johannes Eichner; Georg Zeller; Sascha Laubinger; Gunnar Rätsch (BMC Bioinform., 2011)

Oqtans: a Galaxy-integrated workflow for quantitative transcriptome analysis from NGS Data. - Sebastian J. Schultheiß; Géraldine Jean; Jonas Behr; Regina Bohnert; Philipp Drewe; Nico Görnitz; André Kahles; Pramod Mudrakarta; Vipin T. Sreedharan; Georg Zeller; Gunnar Rätsch (BMC Bioinform., 2011)

Efficient Training of Graph-Regularized Multitask SVMs. - Christian Widmer; Marius Kloft; Nico Görnitz; Gunnar Rätsch (ECML/PKDD (1), 2012)

Multitask Learning in Computational Biology. - Christian Widmer; Gunnar Rätsch (ICML Unsupervised and Transfer Learning, 2012)

Multi-task Learning for Computational Biology: Overview and Outlook. - Christian Widmer; Marius Kloft; Gunnar Rätsch (Empirical Inference, 2013)

An Empirical Analysis of Topic Modeling for Mining Cancer Clinical Notes. - Katherine Redfield Chan; Xinghua Lou; Theofanis Karaletsos; Christopher Crosbie; Stuart M. Gardos; David Artz; Gunnar Rätsch (ICDM Workshops, 2013)

MITIE: Simultaneous RNA-Seq-based transcript identification and quantification in multiple samples. - Jonas Behr; André Kahles; Yi Zhong; Vipin T. Sreedharan; Philipp Drewe; Gunnar Rätsch (Bioinform., 2013)

GRED: Graph-Regularized 3D Shape Reconstruction from Highly Anisotropic and Noisy Images. - Christian Widmer; Philipp Drewe; Xinghua Lou; Shefali Umrania; Stephanie Heinrich; Gunnar Rätsch (CoRR, 2013)

Ecological Modeling from Time-Series Inference: Insight into Dynamics and Stability of Intestinal Microbiota. - Richard R. Stein; Vanni Bucci; Nora C. Toussaint; Charlie G. Buffie; Gunnar Rätsch; Eric G. Pamer; Chris Sander; João B. Xavier (PLoS Comput. Biol., 2013)

Session Introduction. - Søren Brunak; Francisco M. de la Vega; Gunnar Rätsch; Joshua M. Stuart (Pacific Symposium on Biocomputing, 2014)

Oqtans: the RNA-seq workbench in the cloud for complete and reproducible quantitative transcriptome analysis. - Vipin T. Sreedharan; Sebastian J. Schultheiß; Géraldine Jean; André Kahles; Regina Bohnert; Philipp Drewe; Pramod Mudrakarta; Nico Görnitz; Georg Zeller; Gunnar Rätsch (Bioinform., 2014)

Oqtans: a multifunctional workbench for RNA-seq data analysis. - Vipin T. Sreedharan; Sebastian J. Schultheiß; Géraldine Jean; André Kahles; Regina Bohnert; Philipp Drewe; Pramod Mudrakarta; Nico Görnitz; Georg Zeller; Gunnar Rätsch (BMC Bioinform., 2014)

Regularization-Based Multitask Learning With Applications to Genome Biology and Biological Imaging. - Christian Widmer; Marius Kloft; Xinghua Lou; Gunnar Rätsch (Künstliche Intell., 2014)

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 (ECML/PKDD (2), 2015)

Session Introduction. - Søren Brunak; Francisco M. de la Vega; Adam A. Margolin; Benjamin J. Raphael; Gunnar Rätsch; Joshua M. Stuart (Pacific Symposium on Biocomputing, 2015)

Integrative Genome-wide Analysis of the Determinants of RNA Splicing in Kidney Renal Clear Cell Carcinoma. - Kjong-Van Lehmann; André Kahles; Cyriac Kandoth; William Lee; Nikolaus Schultz; Oliver Stegle; Gunnar Rätsch (Pacific Symposium on Biocomputing, 2015)

Protein translational control and its contribution to oncogenesis revealed by computational methods. - Yi Zhong; Philipp Drewe; Andrew L. Wolfe; Kamini Singh; Hans-Guido Wendel; Gunnar Rätsch (BMC Bioinform., 2015)

Framework for Multi-task Multiple Kernel Learning and Applications in Genome Analysis. - Christian Widmer; Marius Kloft; Vipin T. Sreedharan; Gunnar Rätsch (CoRR, 2015)

Machine Learning with Interdependent and Non-identically Distributed Data (Dagstuhl Seminar 15152). - Trevor Darrell; Marius Kloft; Massimiliano Pontil; Gunnar Rätsch; Erik Rodner (Dagstuhl Reports, 2015)

Probabilistic clustering of time-evolving distance data. - Julia E. Vogt; Marius Kloft; Stefan Stark; Sudhir Raman; Sandhya Prabhakaran; Volker Roth 001; Gunnar Rätsch (Mach. Learn., 2015)

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

MMR: a tool for read multi-mapper resolution. - André Kahles; Jonas Behr; Gunnar Rätsch (Bioinform., 2016)

SplAdder: identification, quantification and testing of alternative splicing events from RNA-Seq data. - André Kahles; Cheng Soon Ong; Yi Zhong; Gunnar Rätsch (Bioinform., 2016)

Efficient privacy-preserving string search and an application in genomics. - Kana Shimizu; Koji Nuida; Gunnar Rätsch (Bioinform., 2016)

Knowledge Transfer with Medical Language Embeddings. - Stephanie L. Hyland; Theofanis Karaletsos; Gunnar Rätsch (CoRR, 2016)

When crowds hold privileges: Bayesian unsupervised representation learning with oracle constraints. - Theofanis Karaletsos; Serge J. Belongie; Gunnar Rätsch (ICLR (Poster), 2016)

Learning Unitary Operators with Help From u(n). - Stephanie L. Hyland; Gunnar Rätsch (AAAI, 2017)

Greedy Algorithms for Cone Constrained Optimization with Convergence Guarantees. - Francesco Locatello; Michael Tschannen; Gunnar Rätsch; Martin Jaggi (NIPS, 2017)

RiboDiff: detecting changes of mRNA translation efficiency from ribosome footprints. - Yi Zhong; Theofanis Karaletsos; Philipp Drewe; Vipin T. Sreedharan; David Kuo; Kamini Singh; Hans-Guido Wendel; Gunnar Rätsch (Bioinform., 2017)

Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs. - Cristóbal Esteban; Stephanie L. Hyland; Gunnar Rätsch (CoRR, 2017)

Boosting Variational Inference: an Optimization Perspective. - Francesco Locatello; Rajiv Khanna; Joydeep Ghosh; Gunnar Rätsch (AISTATS, 2018)

A Machine Learning-based Early Warning System for Circulatory System Deterioration in Intensive Care Unit Patients. - Stephanie L. Hyland; Martin Faltys; Matthias Hüser; Xinrui Lyu; Cristóbal Esteban; Gunnar Rätsch; Tobias Merz (AMIA, 2018)

Clustering Meets Implicit Generative Models. - Francesco Locatello; Damien Vincent; Ilya O. Tolstikhin; Gunnar Rätsch; Sylvain Gelly; Bernhard Schölkopf (ICLR (Workshop), 2018)

On Matching Pursuit and Coordinate Descent. - Francesco Locatello; Anant Raj; Sai Praneeth Karimireddy; Gunnar Rätsch; Bernhard Schölkopf; Sebastian U. Stich; Martin Jaggi (ICML, 2018)

Predicting circulatory system deterioration in intensive care unit patients. - Stephanie L. Hyland; Matthias Hüser; Xinrui Lyu; Martin Faltys; Tobias Merz; Gunnar Rätsch (AIH@IJCAI, 2018)

Boosting Black Box Variational Inference. - Francesco Locatello; Gideon Dresdner; Rajiv Khanna; Isabel Valera; Gunnar Rätsch (NeurIPS, 2018)

Revisiting First-Order Convex Optimization Over Linear Spaces. - Francesco Locatello; Anant Raj; Sai Praneeth Karimireddy; Gunnar Rätsch; Bernhard Schölkopf; Sebastian U. Stich; Martin Jaggi (CoRR, 2018)

Deep Self-Organization: Interpretable Discrete Representation Learning on Time Series. - Vincent Fortuin; Matthias Hüser; Francesco Locatello; Heiko Strathmann; Gunnar Rätsch (CoRR, 2018)

Scalable Gaussian Processes on Discrete Domains. - Vincent Fortuin; Gideon Dresdner; Heiko Strathmann; Gunnar Rätsch (CoRR, 2018)

Improving Clinical Predictions through Unsupervised Time Series Representation Learning. - Xinrui Lyu; Matthias Hüser; Stephanie L. Hyland; George Zerveas; Gunnar Rätsch (CoRR, 2018)

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

Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations. - Francesco Locatello; Stefan Bauer; Mario Lucic; Gunnar Rätsch; Sylvain Gelly; Bernhard Schölkopf; Olivier Bachem (ICML, 2019)

Dynamic compression schemes for graph coloring. - Harun Mustafa; Ingo Schilken; Mikhail Karasikov; Carsten Eickhoff; Gunnar Rätsch; André Kahles (Bioinform., 2019)

Deep Mean Functions for Meta-Learning in Gaussian Processes. - Vincent Fortuin; Gunnar Rätsch (CoRR, 2019)

Machine learning for early prediction of circulatory failure in the intensive care unit. - Stephanie L. Hyland; Martin Faltys; Matthias Hüser; Xinrui Lyu; Thomas Gumbsch; Cristóbal Esteban; Christian Bock; Max Horn; Michael Moor; Bastian Rieck; Marc Zimmermann; Dean A. Bodenham; et al. (CoRR, 2019)

Unsupervised Extraction of Phenotypes from Cancer Clinical Notes for Association Studies. - Stefan G. Stark; Stephanie L. Hyland; Melanie Fernandes Pradier; Kjong-Van Lehmann; Andreas Wicki; Fernando Pérez-Cruz; Julia E. Vogt; Gunnar Rätsch (CoRR, 2019)

Disentangling Factors of Variation Using Few Labels. - Francesco Locatello; Michael Tschannen; Stefan Bauer; Gunnar Rätsch; Bernhard Schölkopf; Olivier Bachem (CoRR, 2019)

Multivariate Time Series Imputation with Variational Autoencoders. - Vincent Fortuin; Gunnar Rätsch; Stephan Mandt (CoRR, 2019)

Deep Multiple Instance Learning for Taxonomic Classification of Metagenomic read sets. - Andreas Georgiou; Vincent Fortuin; Harun Mustafa; Gunnar Rätsch (CoRR, 2019)

Variational PSOM: Deep Probabilistic Clustering with Self-Organizing Maps. - Laura Manduchi; Matthias Hüser; Gunnar Rätsch; Vincent Fortuin (CoRR, 2019)

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

GP-VAE: Deep Probabilistic Time Series Imputation. - Vincent Fortuin; Dmitry Baranchuk; Gunnar Rätsch; Stephan Mandt (AISTATS, 2020)

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

Weakly-Supervised Disentanglement Without Compromises. - Francesco Locatello; Ben Poole; Gunnar Rätsch; Bernhard Schölkopf; Olivier Bachem; Michael Tschannen (ICML, 2020)

Communication-Efficient Jaccard similarity for High-Performance Distributed Genome Comparisons. - Maciej Besta; Raghavendra Kanakagiri; Harun Mustafa; Mikhail Karasikov; Gunnar Rätsch; Torsten Hoefler; Edgar Solomonik (IPDPS, 2020)

SPHN/PHRT: Forming a Swiss-Wide Infrastructure for Data-Driven Sepsis Research. - Adrian Egli; Manuel Battegay; Andrea C. Büchler; Peter Bühlmann; Thierry Calandra; Philippe Eckert; Hansjakob Furrer; Gilbert Greub; Stephan M. Jakob; Laurent Kaiser; Stephen L. Leib; Stephan Marsch; et al. (MIE, 2020)

AStarix: Fast and Optimal Sequence-to-Graph Alignment. - Pesho Ivanov; Benjamin Bichsel; Harun Mustafa; André Kahles; Gunnar Rätsch; Martin T. Vechev (RECOMB, 2020)

Mutational signature learning with supervised negative binomial non-negative matrix factorization. - Xinrui Lyu; Jean Garret; Gunnar Rätsch; Kjong-Van Lehmann (Bioinform., 2020)

SCIM: universal single-cell matching with unpaired feature sets. - Stefan G. Stark; Joanna Ficek; Francesco Locatello; Ximena Bonilla; Stéphane Chevrier; Franziska Singer; Tumor Profiler Consortium; Gunnar Rätsch; Kjong-Van Lehmann (Bioinform., 2020)

Scalable Gaussian Process Variational Autoencoders. - Metod Jazbec; Vincent Fortuin; Michael Pearce; Stephan Mandt; Gunnar Rätsch (CoRR, 2020)

Sparse Binary Relation Representations for Genome Graph Annotation. - Mikhail Karasikov; Harun Mustafa; Amir Joudaki; Sara Javadzadeh-No; Gunnar Rätsch; André Kahles (J. Comput. Biol., 2020)

A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation. - Francesco Locatello; Stefan Bauer; Mario Lucic; Gunnar Rätsch; Sylvain Gelly; Bernhard Schölkopf; Olivier Bachem (J. Mach. Learn. Res., 2020)

Genomic basis for RNA alterations in cancer. - Claudia Calabrese; Natalie R. Davidson; Deniz Demircioglu; Nuno A. Fonseca; Yao He; André Kahles; Kjong-Van Lehmann; Fenglin Liu; Yuichi Shiraishi; Cameron M. Soulette; Lara Urban; Liliana Greger; et al. (Nat., 2020)

Scalable Gaussian Process Variational Autoencoders. - Metod Jazbec; Matthew Ashman; Vincent Fortuin; Michael Pearce; Stephan Mandt; Gunnar Rätsch (AISTATS, 2021)

T-DPSOM: an interpretable clustering method for unsupervised learning of patient health states. - Laura Manduchi; Matthias Hüser; Martin Faltys; Julia E. Vogt; Gunnar Rätsch; Vincent Fortuin (CHIL, 2021)

Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning. - Alexander Immer; Matthias Bauer 001; Vincent Fortuin; Gunnar Rätsch; Mohammad Emtiyaz Khan (ICML, 2021)

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

Boosting Variational Inference With Locally Adaptive Step-Sizes. - Gideon Dresdner; Saurav Shekhar; Fabian Pedregosa; Francesco Locatello; Gunnar Rätsch (IJCAI, 2021)

HiRID-ICU-Benchmark - A Comprehensive Machine Learning Benchmark on High-resolution ICU Data. - Hugo Yèche; Rita Kuznetsova; Marc Zimmermann; Matthias Hüser; Xinrui Lyu; Martin Faltys; Gunnar Rätsch (NeurIPS Datasets and Benchmarks, 2021)

WRSE - a non-parametric weighted-resolution ensemble for predicting individual survival distributions in the ICU. - Jonathan Heitz; Joanna Ficek; Martin Faltys; Tobias M. Merz; Gunnar Rätsch; Matthias Hüser (SPACA, 2021)

Sparse Gaussian Processes on Discrete Domains. - Vincent Fortuin; Gideon Dresdner; Heiko Strathmann; Gunnar Rätsch (IEEE Access, 2021)

Topology-based sparsification of graph annotations. - Daniel Danciu; Mikhail Karasikov; Harun Mustafa; André Kahles; Gunnar Rätsch (Bioinform., 2021)

On Disentanglement in Gaussian Process Variational Autoencoders. - Simon Bing; Vincent Fortuin; Gunnar Rätsch (CoRR, 2021)

Bayesian Neural Network Priors Revisited. - Vincent Fortuin; Adrià Garriga-Alonso; Florian Wenzel; Gunnar Rätsch; Richard E. Turner; Mark van der Wilk; Laurence Aitchison (CoRR, 2021)

Early prediction of respiratory failure in the intensive care unit. - Matthias Hüser; Martin Faltys; Xinrui Lyu; Chris Barber; Stephanie L. Hyland; Tobias M. Merz; Gunnar Rätsch (CoRR, 2021)

Reconstructing tumor evolutionary histories and clone trees in polynomial-time with SubMARine. - Linda K. Sundermann; Jeff Wintersinger; Gunnar Rätsch; Jens Stoye; Quaid Morris (PLoS Comput. Biol., 2021)

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

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

Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations. - Alexander Immer; Tycho F. A. van der Ouderaa; Gunnar Rätsch; Vincent Fortuin; Mark van der Wilk (NeurIPS, 2022)

Lossless Indexing with Counting de Bruijn Graphs. - Mikhail Karasikov; Harun Mustafa; Gunnar Rätsch; André Kahles (RECOMB, 2022)

SECEDO: SNV-based subclone detection using ultra-low coverage single-cell DNA sequencing. - Hana Rozhonová; Daniel Danciu; Stefan Stark; Gunnar Rätsch; André Kahles; Kjong-Van Lehmann (Bioinform., 2022)

Temporal Label Smoothing for Early Prediction of Adverse Events. - Hugo Yèche; Alizée Pace; Gunnar Rätsch; Rita Kuznetsova (CoRR, 2022)

On the Importance of Clinical Notes in Multi-modal Learning for EHR Data. - Severin Husmann; Hugo Yèche; Gunnar Rätsch; Rita Kuznetsova (CoRR, 2022)

RNA Instant Quality Check: Alignment-Free RNA-Degradation Detection. - Kjong-Van Lehmann; André Kahles; Magdalena Murr; Gunnar Rätsch (J. Comput. Biol., 2022)

Stochastic Marginal Likelihood Gradients using Neural Tangent Kernels. - Alexander Immer; Tycho F. A. van der Ouderaa; Mark van der Wilk; Gunnar Rätsch; Bernhard Schölkopf (ICML, 2023)

Temporal Label Smoothing for Early Event Prediction. - Hugo Yèche; Alizée Pace; Gunnar Rätsch; Rita Kuznetsova (ICML, 2023)

Multi-modal Graph Learning over UMLS Knowledge Graphs. - Manuel Burger; Gunnar Rätsch; Rita Kuznetsova (ML4H@NeurIPS, 2023)

On the Importance of Step-wise Embeddings for Heterogeneous Clinical Time-Series. - Rita Kuznetsova; Alizée Pace; Manuel Burger; Hugo Yèche; Gunnar Rätsch (ML4H@NeurIPS, 2023)

Laplace-Approximated Neural Additive Models: Improving Interpretability with Bayesian Inference. - Kouroche Bouchiat; Alexander Immer; Hugo Yèche; Gunnar Rätsch; Vincent Fortuin (CoRR, 2023)

Language Model Training Paradigms for Clinical Feature Embeddings. - Yurong Hu; Manuel Burger; Gunnar Rätsch; Rita Kuznetsova (CoRR, 2023)

Knowledge Graph Representations to enhance Intensive Care Time-Series Predictions. - Samyak Jain; Manuel Burger; Gunnar Rätsch; Rita Kuznetsova (CoRR, 2023)

Learning Genomic Sequence Representations using Graph Neural Networks over De Bruijn Graphs. - Kacper Kapusniak; Manuel Burger; Gunnar Rätsch; Amir Joudaki (CoRR, 2023)

ResMiCo: Increasing the quality of metagenome-assembled genomes with deep learning. - Olga Mineeva; Daniel Danciu; Bernhard Schölkopf; Ruth E. Ley; Gunnar Rätsch; Nicholas D. Youngblut (PLoS Comput. Biol., 2023)

FAMEWS: a Fairness Auditing tool for Medical Early-Warning Systems. - Marine Hoche; Olga Mineeva; Manuel Burger; Alessandro Blasimme; Gunnar Rätsch (CHIL, 2024)

Dynamic Survival Analysis for Early Event Prediction. - Hugo Yèche; Manuel Burger; Dinara Veshchezerova; Gunnar Rätsch (CHIL, 2024)

Reinforcement Learning for Heart Failure Treatment Optimization in the Intensive Care Unit. - Cristian Drudi; Moritz Fechner; Maximiliano Mollura; Alizée Pace; Gunnar Rätsch; Riccardo Barbieri (EMBC, 2024)

Towards Training Without Depth Limits: Batch Normalization Without Gradient Explosion. - Alexandru Meterez; Amir Joudaki; Francesco Orabona; Alexander Immer; Gunnar Rätsch; Hadi Daneshmand (ICLR, 2024)

Delphic Offline Reinforcement Learning under Nonidentifiable Hidden Confounding. - Alizée Pace; Hugo Yèche; Bernhard Schölkopf; Gunnar Rätsch; Guy Tennenholtz (ICLR, 2024)

Improving Neural Additive Models with Bayesian Principles. - Kouroche Bouchiat; Alexander Immer; Hugo Yèche; Gunnar Rätsch; Vincent Fortuin (ICML, 2024)

Probabilistic pathway-based multimodal factor analysis. - Alexander Immer; Stefan G. Stark; Francis Jacob; Ximena Bonilla; Tinu Thomas; André Kahles; Sandra Götze; Emanuela S. Milani; Bernd Wollscheid; Tumor Profiler Consortium; Gunnar Rätsch; Kjong-Van Lehmann (Bioinform., 2024)

An empirical study on KDIGO-defined acute kidney injury prediction in the intensive care unit. - Xinrui Lyu; Bowen Fan; Matthias Hüser; Philip Hartout; Thomas Gumbsch; Martin Faltys; Tobias M. Merz; Gunnar Rätsch; Karsten M. Borgwardt (Bioinform., 2024)

Label-guided seed-chain-extend alignment on annotated De Bruijn graphs. - Harun Mustafa; Mikhail Karasikov; Nika Mansouri-Ghiasi; Gunnar Rätsch; André Kahles (Bioinform., 2024)

Multi-Modal Contrastive Learning for Online Clinical Time-Series Applications. - Fabian Baldenweg; Manuel Burger; Gunnar Rätsch; Rita Kuznetsova (CoRR, 2024)

Towards Dynamic Feature Acquisition on Medical Time Series by Maximizing Conditional Mutual Information. - Fedor Sergeev; Paola Malsot; Gunnar Rätsch; Vincent Fortuin (CoRR, 2024)

Uncertainty-Penalized Direct Preference Optimization. - Sam Houliston; Alizée Pace; Alexander Immer; Gunnar Rätsch (CoRR, 2024)

Towards Foundation Models for Critical Care Time Series. - Manuel Burger; Fedor Sergeev; Malte Londschien; Daphné Chopard; Hugo Yèche; Eike Gerdes; Polina Leshetkina; Alexander Morgenroth; Zeynep Babür; Jasmina Bogojeska; Martin Faltys; Rita Kuznetsova; et al. (CoRR, 2024)

Modeling multiple sclerosis using mobile and wearable sensor data. - Shkurta Gashi; Pietro Oldrati; Max Möbus; Marc Hilty; Liliana Barrios; Firat Ozdemir; Veronika Kana; Andreas Lutterotti; Gunnar Rätsch; Christian Holz 001 (npj Digit. Medicine, 2024)

Preference Elicitation for Offline Reinforcement Learning. - Alizée Pace; Bernhard Schölkopf; Gunnar Rätsch; Giorgia Ramponi (ICLR, 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 (WACV, 2025)

Revisiting Automatic Data Curation for Vision Foundation Models in Digital Pathology. - Boqi Chen; Cédric Vincent-Cuaz; Lydia A. Schoenpflug; Manuel Madeira; Lisa Fournier; Vaishnavi Subramanian; Sonali Andani; Samuel Ruipérez-Campillo; Julia E. Vogt; Raphaëlle Luisier; Dorina Thanou; Viktor H. Koelzer; et al. (CoRR, 2025)

Domain Generalization and Adaptation in Intensive Care with Anchor Regression. - Malte Londschien; Manuel Burger; Gunnar Rätsch; Peter Bühlmann (CoRR, 2025)