Gunnar Rätsch

220 papers A* 24A 8B 9C 4Misc 5Journal 122Unranked 45

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	ImmunoPepper: extracting personalized peptides from complex splicing graphs. Bioinform. Laurie Prélot, Jiayu Chen, Matthias Hüser, André Kahles, Gunnar Rätsch
2025	J	`jnl`	Data-Driven Discovery of Feature Groups in Clinical Time Series. CoRR Fedor Sergeev, Manuel Burger, Polina Leshetkina, Vincent Fortuin, Gunnar Rätsch, Rita Kuznetsova
2025	J	`jnl`	Domain Generalization and Adaptation in Intensive Care with Anchor Regression. CoRR Malte Londschien, Manuel Burger, Gunnar Rätsch, Peter Bühlmann
2025	A	`conf`	Generalizable Single-Source Cross-Modality Medical Image Segmentation via Invariant Causal Mechanisms. WACV Boqi Chen, Yuanzhi Zhu, Yunke Ao, Sebastiano Caprara, Reto Sutter, Gunnar Rätsch, Ender Konukoglu, Anna Susmelj
2025	A*	`conf`	Preference Elicitation for Offline Reinforcement Learning. ICLR Alizée Pace, Bernhard Schölkopf, Gunnar Rätsch, Giorgia Ramponi
2025	J	`jnl`	RMS: a ML-based system for ICU respiratory monitoring and resource planning. npj Digit. Medicine Matthias Hüser, Xinrui Lyu, Martin Faltys, Alizée Pace, David Berger, Marine Hoche, Stephanie L. Hyland, Hugo Yèche, Manuel Burger, Tobias M. Merz, Gunnar Rätsch
2025	—	`conf`	Revisiting Automatic Data Curation for Vision Foundation Models in Digital Pathology. MICCAI (6) 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, Pascal Frossard, Gabriele Campanella, Gunnar Rätsch
2025	J	`jnl`	Revisiting Automatic Data Curation for Vision Foundation Models in Digital Pathology. CoRR 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, Pascal Frossard, Gabriele Campanella, Gunnar Rätsch
2024	J	`jnl`	An empirical study on KDIGO-defined acute kidney injury prediction in the intensive care unit. Bioinform. Xinrui Lyu, Bowen Fan, Matthias Hüser, Philip Hartout, Thomas Gumbsch, Martin Faltys, Tobias M. Merz, Gunnar Rätsch, Karsten M. Borgwardt
2024	A*	`conf`	Delphic Offline Reinforcement Learning under Nonidentifiable Hidden Confounding. ICLR Alizée Pace, Hugo Yèche, Bernhard Schölkopf, Gunnar Rätsch, Guy Tennenholtz
2024	—	`conf`	Dynamic Survival Analysis for Early Event Prediction. CHIL Hugo Yèche, Manuel Burger, Dinara Veshchezerova, Gunnar Rätsch
2024	J	`jnl`	Dynamic Survival Analysis for Early Event Prediction. CoRR Hugo Yèche, Manuel Burger, Dinara Veshchezerova, Gunnar Rätsch
2024	—	`conf`	FAMEWS: a Fairness Auditing tool for Medical Early-Warning Systems. CHIL Marine Hoche, Olga Mineeva, Manuel Burger, Alessandro Blasimme, Gunnar Rätsch
2024	J	`jnl`	Generalizable Single-Source Cross-modality Medical Image Segmentation via Invariant Causal Mechanisms. CoRR Boqi Chen, Yuanzhi Zhu, Yunke Ao, Sebastiano Caprara, Reto Sutter, Gunnar Rätsch, Ender Konukoglu, Anna Susmelj
2024	A*	`conf`	Improving Neural Additive Models with Bayesian Principles. ICML Kouroche Bouchiat, Alexander Immer, Hugo Yèche, Gunnar Rätsch, Vincent Fortuin
2024	J	`jnl`	Label-guided seed-chain-extend alignment on annotated De Bruijn graphs. Bioinform. Harun Mustafa, Mikhail Karasikov, Nika Mansouri-Ghiasi, Gunnar Rätsch, André Kahles
2024	J	`jnl`	Modeling multiple sclerosis using mobile and wearable sensor data. npj Digit. Medicine Shkurta Gashi, Pietro Oldrati, Max Möbus, Marc Hilty, Liliana Barrios, Firat Ozdemir, Veronika Kana, Andreas Lutterotti, Gunnar Rätsch, Christian Holz
2024	J	`jnl`	Multi-Modal Contrastive Learning for Online Clinical Time-Series Applications. CoRR Fabian Baldenweg, Manuel Burger, Gunnar Rätsch, Rita Kuznetsova
2024	J	`jnl`	Preference Elicitation for Offline Reinforcement Learning. CoRR Alizée Pace, Bernhard Schölkopf, Gunnar Rätsch, Giorgia Ramponi
2024	J	`jnl`	Probabilistic pathway-based multimodal factor analysis. Bioinform. 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
2024	—	`conf`	Reinforcement Learning for Heart Failure Treatment Optimization in the Intensive Care Unit. EMBC Cristian Drudi, Moritz Fechner, Maximiliano Mollura, Alizée Pace, Gunnar Rätsch, Riccardo Barbieri
2024	J	`jnl`	SimReadUntil for benchmarking selective sequencing algorithms on ONT devices. Bioinform. Maximilian Mordig, Gunnar Rätsch, André Kahles
2024	J	`jnl`	Towards Dynamic Feature Acquisition on Medical Time Series by Maximizing Conditional Mutual Information. CoRR Fedor Sergeev, Paola Malsot, Gunnar Rätsch, Vincent Fortuin
2024	J	`jnl`	Towards Foundation Models for Critical Care Time Series. CoRR Manuel Burger, Fedor Sergeev, Malte Londschien, Daphné Chopard, Hugo Yèche, Eike Gerdes, Polina Leshetkina, Alexander Morgenroth, Zeynep Babür, Jasmina Bogojeska, Martin Faltys, Rita Kuznetsova, Gunnar Rätsch
2024	A*	`conf`	Towards Training Without Depth Limits: Batch Normalization Without Gradient Explosion. ICLR Alexandru Meterez, Amir Joudaki, Francesco Orabona, Alexander Immer, Gunnar Rätsch, Hadi Daneshmand
2024	J	`jnl`	Uncertainty-Penalized Direct Preference Optimization. CoRR Sam Houliston, Alizée Pace, Alexander Immer, Gunnar Rätsch
2023	J	`jnl`	Delphic Offline Reinforcement Learning under Nonidentifiable Hidden Confounding. CoRR Alizée Pace, Hugo Yèche, Bernhard Schölkopf, Gunnar Rätsch, Guy Tennenholtz
2023	J	`jnl`	Knowledge Graph Representations to enhance Intensive Care Time-Series Predictions. CoRR Samyak Jain, Manuel Burger, Gunnar Rätsch, Rita Kuznetsova
2023	J	`jnl`	Language Model Training Paradigms for Clinical Feature Embeddings. CoRR Yurong Hu, Manuel Burger, Gunnar Rätsch, Rita Kuznetsova
2023	J	`jnl`	Laplace-Approximated Neural Additive Models: Improving Interpretability with Bayesian Inference. CoRR Kouroche Bouchiat, Alexander Immer, Hugo Yèche, Gunnar Rätsch, Vincent Fortuin
2023	J	`jnl`	Learning Genomic Sequence Representations using Graph Neural Networks over De Bruijn Graphs. CoRR Kacper Kapusniak, Manuel Burger, Gunnar Rätsch, Amir Joudaki
2023	—	`conf`	Multi-modal Graph Learning over UMLS Knowledge Graphs. ML4H@NeurIPS Manuel Burger, Gunnar Rätsch, Rita Kuznetsova
2023	J	`jnl`	Multi-modal Graph Learning over UMLS Knowledge Graphs. CoRR Manuel Burger, Gunnar Rätsch, Rita Kuznetsova
2023	—	`conf`	On the Importance of Step-wise Embeddings for Heterogeneous Clinical Time-Series. ML4H@NeurIPS Rita Kuznetsova, Alizée Pace, Manuel Burger, Hugo Yèche, Gunnar Rätsch
2023	J	`jnl`	On the Importance of Step-wise Embeddings for Heterogeneous Clinical Time-Series. CoRR Rita Kuznetsova, Alizée Pace, Manuel Burger, Hugo Yèche, Gunnar Rätsch
2023	J	`jnl`	ResMiCo: Increasing the quality of metagenome-assembled genomes with deep learning. PLoS Comput. Biol. Olga Mineeva, Daniel Danciu, Bernhard Schölkopf, Ruth E. Ley, Gunnar Rätsch, Nicholas D. Youngblut
2023	A*	`conf`	Stochastic Marginal Likelihood Gradients using Neural Tangent Kernels. ICML Alexander Immer, Tycho F. A. van der Ouderaa, Mark van der Wilk, Gunnar Rätsch, Bernhard Schölkopf
2023	J	`jnl`	Stochastic Marginal Likelihood Gradients using Neural Tangent Kernels. CoRR Alexander Immer, Tycho F. A. van der Ouderaa, Mark van der Wilk, Gunnar Rätsch, Bernhard Schölkopf
2023	A*	`conf`	Temporal Label Smoothing for Early Event Prediction. ICML Hugo Yèche, Alizée Pace, Gunnar Rätsch, Rita Kuznetsova
2023	J	`jnl`	Towards Training Without Depth Limits: Batch Normalization Without Gradient Explosion. CoRR Alexandru Meterez, Amir Joudaki, Francesco Orabona, Alexander Immer, Gunnar Rätsch, Hadi Daneshmand
2022	A*	`conf`	Bayesian Neural Network Priors Revisited. ICLR Vincent Fortuin, Adrià Garriga-Alonso, Sebastian W. Ober, Florian Wenzel, Gunnar Rätsch, Richard E. Turner, Mark van der Wilk, Laurence Aitchison
2022	A	`conf`	Faster One-Sample Stochastic Conditional Gradient Method for Composite Convex Minimization. AISTATS Gideon Dresdner, Maria-Luiza Vladarean, Gunnar Rätsch, Francesco Locatello, Volkan Cevher, Alp Yurtsever
2022	J	`jnl`	Faster One-Sample Stochastic Conditional Gradient Method for Composite Convex Minimization. CoRR Gideon Dresdner, Maria-Luiza Vladarean, Gunnar Rätsch, Francesco Locatello, Volkan Cevher, Alp Yurtsever
2022	A*	`conf`	Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations. NeurIPS Alexander Immer, Tycho F. A. van der Ouderaa, Gunnar Rätsch, Vincent Fortuin, Mark van der Wilk
2022	J	`jnl`	Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations. CoRR Alexander Immer, Tycho F. A. van der Ouderaa, Vincent Fortuin, Gunnar Rätsch, Mark van der Wilk
2022	B	`conf`	Lossless Indexing with Counting de Bruijn Graphs. RECOMB Mikhail Karasikov, Harun Mustafa, Gunnar Rätsch, André Kahles
2022	J	`jnl`	On the Importance of Clinical Notes in Multi-modal Learning for EHR Data. CoRR Severin Husmann, Hugo Yèche, Gunnar Rätsch, Rita Kuznetsova
2022	J	`jnl`	RNA Instant Quality Check: Alignment-Free RNA-Degradation Detection. J. Comput. Biol. Kjong-Van Lehmann, André Kahles, Magdalena Murr, Gunnar Rätsch
2022	J	`jnl`	SECEDO: SNV-based subclone detection using ultra-low coverage single-cell DNA sequencing. Bioinform. Hana Rozhonová, Daniel Danciu, Stefan Stark, Gunnar Rätsch, André Kahles, Kjong-Van Lehmann
2022	J	`jnl`	Temporal Label Smoothing for Early Prediction of Adverse Events. CoRR Hugo Yèche, Alizée Pace, Gunnar Rätsch, Rita Kuznetsova
2021	J	`jnl`	Bayesian Neural Network Priors Revisited. CoRR Vincent Fortuin, Adrià Garriga-Alonso, Florian Wenzel, Gunnar Rätsch, Richard E. Turner, Mark van der Wilk, Laurence Aitchison
2021	A*	`conf`	Boosting Variational Inference With Locally Adaptive Step-Sizes. IJCAI Gideon Dresdner, Saurav Shekhar, Fabian Pedregosa, Francesco Locatello, Gunnar Rätsch
2021	J	`jnl`	Boosting Variational Inference With Locally Adaptive Step-Sizes. CoRR Gideon Dresdner, Saurav Shekhar, Fabian Pedregosa, Francesco Locatello, Gunnar Rätsch
2021	J	`jnl`	Early prediction of respiratory failure in the intensive care unit. CoRR Matthias Hüser, Martin Faltys, Xinrui Lyu, Chris Barber, Stephanie L. Hyland, Tobias M. Merz, Gunnar Rätsch
2021	—	`conf`	HiRID-ICU-Benchmark - A Comprehensive Machine Learning Benchmark on High-resolution ICU Data. NeurIPS Datasets and Benchmarks Hugo Yèche, Rita Kuznetsova, Marc Zimmermann, Matthias Hüser, Xinrui Lyu, Martin Faltys, Gunnar Rätsch
2021	J	`jnl`	HiRID-ICU-Benchmark - A Comprehensive Machine Learning Benchmark on High-resolution ICU Data. CoRR Hugo Yèche, Rita Kuznetsova, Marc Zimmermann, Matthias Hüser, Xinrui Lyu, Martin Faltys, Gunnar Rätsch
2021	A*	`conf`	Neighborhood Contrastive Learning Applied to Online Patient Monitoring. ICML Hugo Yèche, Gideon Dresdner, Francesco Locatello, Matthias Hüser, Gunnar Rätsch
2021	J	`jnl`	Neighborhood Contrastive Learning Applied to Online Patient Monitoring. CoRR Hugo Yèche, Gideon Dresdner, Francesco Locatello, Matthias Hüser, Gunnar Rätsch
2021	J	`jnl`	On Disentanglement in Gaussian Process Variational Autoencoders. CoRR Simon Bing, Vincent Fortuin, Gunnar Rätsch
2021	J	`jnl`	Reconstructing tumor evolutionary histories and clone trees in polynomial-time with SubMARine. PLoS Comput. Biol. Linda K. Sundermann, Jeff Wintersinger, Gunnar Rätsch, Jens Stoye, Quaid Morris
2021	A	`conf`	Scalable Gaussian Process Variational Autoencoders. AISTATS Metod Jazbec, Matthew Ashman, Vincent Fortuin, Michael Pearce, Stephan Mandt, Gunnar Rätsch
2021	A*	`conf`	Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning. ICML Alexander Immer, Matthias Bauer, Vincent Fortuin, Gunnar Rätsch, Mohammad Emtiyaz Khan
2021	J	`jnl`	Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning. CoRR Alexander Immer, Matthias Bauer, Vincent Fortuin, Gunnar Rätsch, Mohammad Emtiyaz Khan
2021	J	`jnl`	Sparse Gaussian Processes on Discrete Domains. IEEE Access Vincent Fortuin, Gideon Dresdner, Heiko Strathmann, Gunnar Rätsch
2021	—	`conf`	T-DPSOM: an interpretable clustering method for unsupervised learning of patient health states. CHIL Laura Manduchi, Matthias Hüser, Martin Faltys, Julia E. Vogt, Gunnar Rätsch, Vincent Fortuin
2021	J	`jnl`	Topology-based sparsification of graph annotations. Bioinform. Daniel Danciu, Mikhail Karasikov, Harun Mustafa, André Kahles, Gunnar Rätsch
2021	—	`conf`	WRSE - a non-parametric weighted-resolution ensemble for predicting individual survival distributions in the ICU. SPACA Jonathan Heitz, Joanna Ficek, Martin Faltys, Tobias M. Merz, Gunnar Rätsch, Matthias Hüser
2020	A*	`conf`	A Commentary on the Unsupervised Learning of Disentangled Representations. AAAI Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem
2020	J	`jnl`	A Commentary on the Unsupervised Learning of Disentangled Representations. CoRR Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem
2020	J	`jnl`	A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation. CoRR Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem
2020	J	`jnl`	A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation. J. Mach. Learn. Res. Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem
2020	B	`conf`	AStarix: Fast and Optimal Sequence-to-Graph Alignment. RECOMB Pesho Ivanov, Benjamin Bichsel, Harun Mustafa, André Kahles, Gunnar Rätsch, Martin T. Vechev
2020	A	`conf`	Communication-Efficient Jaccard similarity for High-Performance Distributed Genome Comparisons. IPDPS Maciej Besta, Raghavendra Kanakagiri, Harun Mustafa, Mikhail Karasikov, Gunnar Rätsch, Torsten Hoefler, Edgar Solomonik
2020	A*	`conf`	Disentangling Factors of Variations Using Few Labels. ICLR Francesco Locatello, Michael Tschannen, Stefan Bauer, Gunnar Rätsch, Bernhard Schölkopf, Olivier Bachem
2020	A	`conf`	GP-VAE: Deep Probabilistic Time Series Imputation. AISTATS Vincent Fortuin, Dmitry Baranchuk, Gunnar Rätsch, Stephan Mandt
2020	J	`jnl`	Genomic basis for RNA alterations in cancer. Nat. 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, Siliang Li, Dongbing Liu, Marc D. Perry, Qian Xiang, Fan Zhang, Junjun Zhang, Peter Bailey, Serap Erkek, Katherine A. Hoadley, Yong Hou, Matthew R. Huska, Helena Kilpinen, Jan O. Korbel, Maximillian G. Marin, Julia Markowski, Tannistha Nandi, Qiang Pan-Hammarström, Chandra Sekhar Pedamallu, Reiner Siebert, Stefan G. Stark, Hong Su, Patrick Tan, Sebastian M. Waszak, Christina K. Yung, Shida Zhu, Philip Awadalla, Matthew Meyerson, B. F. Francis Ouellette, Kui Wu, Huanming Yang, Samirkumar B. Amin, Aurélien Chateigner, Isidro Cortés-Ciriano, Brian Craft, Milana Frenkel-Morgenstern, Mary Goldman, Ekta Khurana, Fabien C. Lamaze, Chang Li, Xiaobo Li, Xinyue Li, Xingmin Liu, Morten Muhlig Nielsen, Akinyemi I. Ojesina, Peter J. Park, Jakob Skou Pedersen, Bin Tean Teh, Jian Wang, Heng Xiong, Sergei Yakneen, Chen Ye, Xiuqing Zhang, Liangtao Zheng, Jingchun Zhu, Chad Creighton, Jonathan Göke, Roland F. Schwarz, Oliver Stegle, Zemin Zhang, Alvis Brazma, Gunnar Rätsch, Angela N. Brooks
2020	J	`jnl`	Mutational signature learning with supervised negative binomial non-negative matrix factorization. Bioinform. Xinrui Lyu, Jean Garret, Gunnar Rätsch, Kjong-Van Lehmann
2020	J	`jnl`	SCIM: universal single-cell matching with unpaired feature sets. Bioinform. Stefan G. Stark, Joanna Ficek, Francesco Locatello, Ximena Bonilla, Stéphane Chevrier, Franziska Singer, Tumor Profiler Consortium, Gunnar Rätsch, Kjong-Van Lehmann
2020	—	`conf`	SPHN/PHRT: Forming a Swiss-Wide Infrastructure for Data-Driven Sepsis Research. MIE 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, Nicolai Meinshausen, Jean-Luc Pagani, Jerome Pugin, Gunnar Rätsch, Jacques Schrenzel, Reto Schüpbach, Martin Siegemund, Nicola Zamboni, Reinhard Zbinden, Annelies Zinkernagel, Karsten M. Borgwardt
2020	J	`jnl`	Scalable Gaussian Process Variational Autoencoders. CoRR Metod Jazbec, Vincent Fortuin, Michael Pearce, Stephan Mandt, Gunnar Rätsch
2020	J	`jnl`	Sparse Binary Relation Representations for Genome Graph Annotation. J. Comput. Biol. Mikhail Karasikov, Harun Mustafa, Amir Joudaki, Sara Javadzadeh-No, Gunnar Rätsch, André Kahles
2020	J	`jnl`	WRSE - a non-parametric weighted-resolution ensemble for predicting individual survival distributions in the ICU. CoRR Jonathan Heitz, Joanna Ficek, Martin Faltys, Tobias M. Merz, Gunnar Rätsch, Matthias Hüser
2020	A*	`conf`	Weakly-Supervised Disentanglement Without Compromises. ICML Francesco Locatello, Ben Poole, Gunnar Rätsch, Bernhard Schölkopf, Olivier Bachem, Michael Tschannen
2020	J	`jnl`	Weakly-Supervised Disentanglement Without Compromises. CoRR Francesco Locatello, Ben Poole, Gunnar Rätsch, Bernhard Schölkopf, Olivier Bachem, Michael Tschannen
2019	—	`conf`	Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations. RML@ICLR Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem
2019	A*	`conf`	Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations. ICML Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem
2019	J	`jnl`	Communication-Efficient Jaccard Similarity for High-Performance Distributed Genome Comparisons. CoRR Maciej Besta, Raghavendra Kanakagiri, Harun Mustafa, Mikhail Karasikov, Gunnar Rätsch, Torsten Hoefler, Edgar Solomonik
2019	J	`jnl`	Deep Mean Functions for Meta-Learning in Gaussian Processes. CoRR Vincent Fortuin, Gunnar Rätsch
2019	J	`jnl`	Deep Multiple Instance Learning for Taxonomic Classification of Metagenomic read sets. CoRR Andreas Georgiou, Vincent Fortuin, Harun Mustafa, Gunnar Rätsch
2019	J	`jnl`	Disentangling Factors of Variation Using Few Labels. CoRR Francesco Locatello, Michael Tschannen, Stefan Bauer, Gunnar Rätsch, Bernhard Schölkopf, Olivier Bachem
2019	J	`jnl`	Dynamic compression schemes for graph coloring. Bioinform. Harun Mustafa, Ingo Schilken, Mikhail Karasikov, Carsten Eickhoff, Gunnar Rätsch, André Kahles
2019	J	`jnl`	Machine learning for early prediction of circulatory failure in the intensive care unit. CoRR 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, Karsten M. Borgwardt, Gunnar Rätsch, Tobias M. Merz
2019	J	`jnl`	Multivariate Time Series Imputation with Variational Autoencoders. CoRR Vincent Fortuin, Gunnar Rätsch, Stephan Mandt
2019	—	`conf`	SOM-VAE: Interpretable Discrete Representation Learning on Time Series. ICLR (Poster) Vincent Fortuin, Matthias Hüser, Francesco Locatello, Heiko Strathmann, Gunnar Rätsch
2019	B	`conf`	Sparse Binary Relation Representations for Genome Graph Annotation. RECOMB Mikhail Karasikov, Harun Mustafa, Amir Joudaki, Sara Javadzadeh-No, Gunnar Rätsch, André Kahles
2019	J	`jnl`	Unsupervised Extraction of Phenotypes from Cancer Clinical Notes for Association Studies. CoRR Stefan G. Stark, Stephanie L. Hyland, Melanie Fernandes Pradier, Kjong-Van Lehmann, Andreas Wicki, Fernando Pérez-Cruz, Julia E. Vogt, Gunnar Rätsch
2019	J	`jnl`	Variational PSOM: Deep Probabilistic Clustering with Self-Organizing Maps. CoRR Laura Manduchi, Matthias Hüser, Gunnar Rätsch, Vincent Fortuin
2018	Misc	`conf`	A Machine Learning-based Early Warning System for Circulatory System Deterioration in Intensive Care Unit Patients. AMIA Stephanie L. Hyland, Martin Faltys, Matthias Hüser, Xinrui Lyu, Cristóbal Esteban, Gunnar Rätsch, Tobias Merz
2018	A*	`conf`	Boosting Black Box Variational Inference. NeurIPS Francesco Locatello, Gideon Dresdner, Rajiv Khanna, Isabel Valera, Gunnar Rätsch
2018	J	`jnl`	Boosting Black Box Variational Inference. CoRR Francesco Locatello, Gideon Dresdner, Rajiv Khanna, Isabel Valera, Gunnar Rätsch
2018	A	`conf`	Boosting Variational Inference: an Optimization Perspective. AISTATS Francesco Locatello, Rajiv Khanna, Joydeep Ghosh, Gunnar Rätsch
2018	—	`conf`	Clustering Meets Implicit Generative Models. ICLR (Workshop) Francesco Locatello, Damien Vincent, Ilya O. Tolstikhin, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf
2018	J	`jnl`	Clustering Meets Implicit Generative Models. CoRR Francesco Locatello, Damien Vincent, Ilya O. Tolstikhin, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf
2018	J	`jnl`	Deep Self-Organization: Interpretable Discrete Representation Learning on Time Series. CoRR Vincent Fortuin, Matthias Hüser, Francesco Locatello, Heiko Strathmann, Gunnar Rätsch
2018	J	`jnl`	Improving Clinical Predictions through Unsupervised Time Series Representation Learning. CoRR Xinrui Lyu, Matthias Hüser, Stephanie L. Hyland, George Zerveas, Gunnar Rätsch
2018	A*	`conf`	On Matching Pursuit and Coordinate Descent. ICML Francesco Locatello, Anant Raj, Sai Praneeth Karimireddy, Gunnar Rätsch, Bernhard Schölkopf, Sebastian U. Stich, Martin Jaggi
2018	—	`conf`	Predicting circulatory system deterioration in intensive care unit patients. AIH@IJCAI Stephanie L. Hyland, Matthias Hüser, Xinrui Lyu, Martin Faltys, Tobias Merz, Gunnar Rätsch
2018	J	`jnl`	Revisiting First-Order Convex Optimization Over Linear Spaces. CoRR Francesco Locatello, Anant Raj, Sai Praneeth Karimireddy, Gunnar Rätsch, Bernhard Schölkopf, Sebastian U. Stich, Martin Jaggi
2018	J	`jnl`	Scalable Gaussian Processes on Discrete Domains. CoRR Vincent Fortuin, Gideon Dresdner, Heiko Strathmann, Gunnar Rätsch
2017	J	`jnl`	Boosting Variational Inference: an Optimization Perspective. CoRR Francesco Locatello, Rajiv Khanna, Joydeep Ghosh, Gunnar Rätsch
2017	—	`conf`	Greedy Algorithms for Cone Constrained Optimization with Convergence Guarantees. NIPS Francesco Locatello, Michael Tschannen, Gunnar Rätsch, Martin Jaggi
2017	J	`jnl`	Greedy Algorithms for Cone Constrained Optimization with Convergence Guarantees. CoRR Francesco Locatello, Michael Tschannen, Gunnar Rätsch, Martin Jaggi
2017	A*	`conf`	Learning Unitary Operators with Help From u(n). AAAI Stephanie L. Hyland, Gunnar Rätsch
2017	J	`jnl`	Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs. CoRR Cristóbal Esteban, Stephanie L. Hyland, Gunnar Rätsch
2017	J	`jnl`	RiboDiff: detecting changes of mRNA translation efficiency from ribosome footprints. Bioinform. Yi Zhong, Theofanis Karaletsos, Philipp Drewe, Vipin T. Sreedharan, David Kuo, Kamini Singh, Hans-Guido Wendel, Gunnar Rätsch
2016	A*	`conf`	A Generative Model of Words and Relationships from Multiple Sources. AAAI Stephanie L. Hyland, Theofanis Karaletsos, Gunnar Rätsch
2016	J	`jnl`	Efficient privacy-preserving string search and an application in genomics. Bioinform. Kana Shimizu, Koji Nuida, Gunnar Rätsch
2016	J	`jnl`	Knowledge Transfer with Medical Language Embeddings. CoRR Stephanie L. Hyland, Theofanis Karaletsos, Gunnar Rätsch
2016	J	`jnl`	Learning Unitary Operators with Help From u(n). CoRR Stephanie L. Hyland, Gunnar Rätsch
2016	J	`jnl`	MMR: a tool for read multi-mapper resolution. Bioinform. André Kahles, Jonas Behr, Gunnar Rätsch
2016	A*	`conf`	When crowds hold privileges: Bayesian unsupervised representation learning with oracle constraints. ICLR (Poster) Theofanis Karaletsos, Serge J. Belongie, Gunnar Rätsch
2015	J	`jnl`	A Generative Model of Words and Relationships from Multiple Sources. CoRR Stephanie L. Hyland, Theofanis Karaletsos, Gunnar Rätsch
2015	J	`jnl`	Framework for Multi-task Multiple Kernel Learning and Applications in Genome Analysis. CoRR Christian Widmer, Marius Kloft, Vipin T. Sreedharan, Gunnar Rätsch
2015	Misc	`conf`	Integrative Genome-wide Analysis of the Determinants of RNA Splicing in Kidney Renal Clear Cell Carcinoma. Pacific Symposium on Biocomputing Kjong-Van Lehmann, André Kahles, Cyriac Kandoth, William Lee, Nikolaus Schultz, Oliver Stegle, Gunnar Rätsch
2015	J	`jnl`	Machine Learning with Interdependent and Non-identically Distributed Data (Dagstuhl Seminar 15152). Dagstuhl Reports Trevor Darrell, Marius Kloft, Massimiliano Pontil, Gunnar Rätsch, Erik Rodner
2015	—	`conf`	Opening the Black Box: Revealing Interpretable Sequence Motifs in Kernel-Based Learning Algorithms. ECML/PKDD (2) Marina M.-C. Vidovic, Nico Görnitz, Klaus-Robert Müller, Gunnar Rätsch, Marius Kloft
2015	J	`jnl`	Probabilistic Clustering of Time-Evolving Distance Data. CoRR Julia E. Vogt, Marius Kloft, Stefan Stark, Sudhir Raman, Sandhya Prabhakaran, Volker Roth, Gunnar Rätsch
2015	J	`jnl`	Probabilistic clustering of time-evolving distance data. Mach. Learn. Julia E. Vogt, Marius Kloft, Stefan Stark, Sudhir Raman, Sandhya Prabhakaran, Volker Roth, Gunnar Rätsch
2015	J	`jnl`	Protein translational control and its contribution to oncogenesis revealed by computational methods. BMC Bioinform. Yi Zhong, Philipp Drewe, Andrew L. Wolfe, Kamini Singh, Hans-Guido Wendel, Gunnar Rätsch
2015	Misc	`conf`	Session Introduction. Pacific Symposium on Biocomputing Søren Brunak, Francisco M. de la Vega, Adam A. Margolin, Benjamin J. Raphael, Gunnar Rätsch, Joshua M. Stuart
2014	J	`jnl`	Oqtans: a multifunctional workbench for RNA-seq data analysis. BMC Bioinform. Vipin T. Sreedharan, Sebastian J. Schultheiß, Géraldine Jean, André Kahles, Regina Bohnert, Philipp Drewe, Pramod Mudrakarta, Nico Görnitz, Georg Zeller, Gunnar Rätsch
2014	J	`jnl`	Oqtans: the RNA-seq workbench in the cloud for complete and reproducible quantitative transcriptome analysis. Bioinform. Vipin T. Sreedharan, Sebastian J. Schultheiß, Géraldine Jean, André Kahles, Regina Bohnert, Philipp Drewe, Pramod Mudrakarta, Nico Görnitz, Georg Zeller, Gunnar Rätsch
2014	J	`jnl`	Regularization-Based Multitask Learning With Applications to Genome Biology and Biological Imaging. Künstliche Intell. Christian Widmer, Marius Kloft, Xinghua Lou, Gunnar Rätsch
2014	Misc	`conf`	Session Introduction. Pacific Symposium on Biocomputing Søren Brunak, Francisco M. de la Vega, Gunnar Rätsch, Joshua M. Stuart
2013	—	`conf`	An Empirical Analysis of Topic Modeling for Mining Cancer Clinical Notes. ICDM Workshops Katherine Redfield Chan, Xinghua Lou, Theofanis Karaletsos, Christopher Crosbie, Stuart M. Gardos, David Artz, Gunnar Rätsch
2013	J	`jnl`	Ecological Modeling from Time-Series Inference: Insight into Dynamics and Stability of Intestinal Microbiota. PLoS Comput. Biol. Richard R. Stein, Vanni Bucci, Nora C. Toussaint, Charlie G. Buffie, Gunnar Rätsch, Eric G. Pamer, Chris Sander, João B. Xavier
2013	J	`jnl`	GRED: Graph-Regularized 3D Shape Reconstruction from Highly Anisotropic and Noisy Images. CoRR Christian Widmer, Philipp Drewe, Xinghua Lou, Shefali Umrania, Stephanie Heinrich, Gunnar Rätsch
2013	J	`jnl`	MITIE: Simultaneous RNA-Seq-based transcript identification and quantification in multiple samples. Bioinform. Jonas Behr, André Kahles, Yi Zhong, Vipin T. Sreedharan, Philipp Drewe, Gunnar Rätsch
2013	—	`conf`	Multi-task Learning for Computational Biology: Overview and Outlook. Empirical Inference Christian Widmer, Marius Kloft, Gunnar Rätsch
2012	—	`conf`	Efficient Training of Graph-Regularized Multitask SVMs. ECML/PKDD (1) Christian Widmer, Marius Kloft, Nico Görnitz, Gunnar Rätsch
2012	—	`conf`	Multitask Learning in Computational Biology. ICML Unsupervised and Transfer Learning Christian Widmer, Gunnar Rätsch
2011	—	`conf`	Hierarchical Multitask Structured Output Learning for Large-scale Sequence Segmentation. NIPS Nico Görnitz, Christian Widmer, Georg Zeller, André Kahles, Sören Sonnenburg, Gunnar Rätsch
2011	J	`jnl`	Oqtans: a Galaxy-integrated workflow for quantitative transcriptome analysis from NGS Data. BMC Bioinform. Sebastian J. Schultheiß, Géraldine Jean, Jonas Behr, Regina Bohnert, Philipp Drewe, Nico Görnitz, André Kahles, Pramod Mudrakarta, Vipin T. Sreedharan, Georg Zeller, Gunnar Rätsch
2011	J	`jnl`	Support vector machines-based identification of alternative splicing in Arabidopsis thaliana from whole-genome tiling arrays. BMC Bioinform. Johannes Eichner, Georg Zeller, Sascha Laubinger, Gunnar Rätsch
2010	J	`jnl`	Exploiting physico-chemical properties in string kernels. BMC Bioinform. Nora C. Toussaint, Christian Widmer, Oliver Kohlbacher, Gunnar Rätsch
2010	J	`jnl`	Inferring latent task structure for Multitask Learning by Multiple Kernel Learning. BMC Bioinform. Christian Widmer, Nora C. Toussaint, Yasemin Altun, Gunnar Rätsch
2010	B	`conf`	Leveraging Sequence Classification by Taxonomy-Based Multitask Learning. RECOMB Christian Widmer, Jose Leiva, Yasemin Altun, Gunnar Rätsch
2010	J	`jnl`	Next generation genome annotation with mGene.ngs. BMC Bioinform. Jonas Behr, Regina Bohnert, Georg Zeller, Gabriele Beate Schweikert, Lisa Hartmann, Gunnar Rätsch
2010	—	`conf`	Novel Machine Learning Methods for MHC Class I Binding Prediction. PRIB Christian Widmer, Nora C. Toussaint, Yasemin Altun, Oliver Kohlbacher, Gunnar Rätsch
2010	J	`jnl`	The SHOGUN Machine Learning Toolbox. J. Mach. Learn. Res. Sören Sonnenburg, Gunnar Rätsch, Sebastian Henschel, Christian Widmer, Jonas Behr, Alexander Zien, Fabio De Bona, Alexander Binder, Christian Gehl, Vojtech Franc
2010	J	`jnl`	rQuant.web: a tool for RNA-Seq-based transcript quantitation. Nucleic Acids Res. Regina Bohnert, Gunnar Rätsch
2009	J	`jnl`	KIRMES: kernel-based identification of regulatory modules in euchromatic sequences. Bioinform. Sebastian J. Schultheiß, Wolfgang Busch, Jan U. Lohmann, Oliver Kohlbacher, Gunnar Rätsch
2009	J	`jnl`	KIRMES: kernel-based identification of regulatory modules in euchromatic sequences. BMC Bioinform. Sebastian J. Schultheiß, Wolfgang Busch, Jan U. Lohmann, Oliver Kohlbacher, Gunnar Rätsch
2009	J	`jnl`	Prototype Classification: Insights from Machine Learning. Neural Comput. Arnulf B. A. Graf, Olivier Bousquet, Gunnar Rätsch, Bernhard Schölkopf
2009	—	`conf`	The Feature Importance Ranking Measure. ECML/PKDD (2) Alexander Zien, Nicole Krämer, Sören Sonnenburg, Gunnar Rätsch
2009	J	`jnl`	Transcript quantification with RNA-Seq data. BMC Bioinform. Regina Bohnert, Jonas Behr, Gunnar Rätsch
2008	—	`conf`	An Empirical Analysis of Domain Adaptation Algorithms for Genomic Sequence Analysis. NIPS Gabriele Beate Schweikert, Christian Widmer, Bernhard Schölkopf, Gunnar Rätsch
2008	—	`conf`	KIRMES: Kernel-based Identification of Regulatory Modules in Euchromatic Sequences. German Conference on Bioinformatics Sebastian J. Schultheiß, Wolfgang Busch, Jan U. Lohmann, Oliver Kohlbacher, Gunnar Rätsch
2008	C	`conf`	Optimal spliced alignments of short sequence reads. ECCB Fabio De Bona, Stephan Ossowski, Korbinian Schneeberger, Gunnar Rätsch
2008	J	`jnl`	Optimal spliced alignments of short sequence reads. BMC Bioinform. Fabio De Bona, Stephan Ossowski, Korbinian Schneeberger, Gunnar Rätsch
2008	A	`conf`	POIMs: positional oligomer importance matrices - understanding support vector machine-based signal detectors. ISMB Sören Sonnenburg, Alexander Zien, Petra Philips, Gunnar Rätsch
2008	J	`jnl`	Revealing sequence variation patterns in rice with machine learning methods. BMC Bioinform. 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, Hei Leung, Kenneth L. McNally, Detlef Weigel, Gunnar Rätsch
2008	J	`jnl`	Support Vector Machines and Kernels for Computational Biology. PLoS Comput. Biol. Asa Ben-Hur, Cheng Soon Ong, Sören Sonnenburg, Bernhard Schölkopf, Gunnar Rätsch
2008	Misc	`conf`	Transcript Normalization and Segmentation of Tiling Array Data. Pacific Symposium on Biocomputing Georg Zeller, Stefan R. Henz, Sascha Laubinger, Detlef Weigel, Gunnar Rätsch
2007	J	`jnl`	Accurate splice site prediction using support vector machines. BMC Bioinform. Sören Sonnenburg, Gabriele Beate Schweikert, Petra Philips, Jonas Behr, Gunnar Rätsch
2007	—	`conf`	Boosting Algorithms for Maximizing the Soft Margin. NIPS Manfred K. Warmuth, Karen A. Glocer, Gunnar Rätsch
2007	J	`jnl`	Improving the PLoS Comput. Biol. Gunnar Rätsch, Sören Sonnenburg, Jagan Srinivasan, Hanh Witte, Klaus-Robert Müller, Ralf J. Sommer, Bernhard Schölkopf
2007	J	`jnl`	NIPS workshop on New Problems and Methods in Computational Biology. BMC Bioinform. Gal Chechik, Christina S. Leslie, William Stafford Noble, Gunnar Rätsch, Quaid Morris, Koji Tsuda
2007	J	`jnl`	PALMA: mRNA to genome alignments using large margin algorithms. Bioinform. Uta Schulze, Bettina Hepp, Cheng Soon Ong, Gunnar Rätsch
2007	J	`jnl`	The Need for Open Source Software in Machine Learning. J. Mach. Learn. Res. Sören Sonnenburg, Mikio L. Braun, Cheng Soon Ong, Samy Bengio, Léon Bottou, Geoffrey Holmes, Yann LeCun, Klaus-Robert Müller, Fernando Pereira, Carl Edward Rasmussen, Gunnar Rätsch, Bernhard Schölkopf, Alexander J. Smola, Pascal Vincent, Jason Weston, Robert C. Williamson
2006	—	`conf`	ARTS: accurate recognition of transcription starts in human. ISMB (Supplement of Bioinformatics) Sören Sonnenburg, Alexander Zien, Gunnar Rätsch
2006	—	`conf`	Graph Based Semi-supervised Learning with Sharper Edges. ECML Hyunjung Shin, N. Jeremy Hill, Gunnar Rätsch
2006	—	`conf`	Large Scale Hidden Semi-Markov SVMs. NIPS Gunnar Rätsch, Sören Sonnenburg
2006	J	`jnl`	Large Scale Multiple Kernel Learning. J. Mach. Learn. Res. Sören Sonnenburg, Gunnar Rätsch, Christin Schäfer, Bernhard Schölkopf
2006	J	`jnl`	Learning Interpretable SVMs for Biological Sequence Classification. BMC Bioinform. Gunnar Rätsch, Sören Sonnenburg, Christin Schäfer
2006	—	`conf`	PALMA: Perfect Alignments using Large Margin Algorithms. German Conference on Bioinformatics Gunnar Rätsch, Bettina Hepp, Uta Schulze, Cheng Soon Ong
2006	B	`conf`	Solving Semi-infinite Linear Programs Using Boosting-Like Methods. ALT Gunnar Rätsch
2006	B	`conf`	The Solution of Semi-Infinite Linear Programs Using Boosting-Like Methods. Discovery Science Gunnar Rätsch
2006	A*	`conf`	Totally corrective boosting algorithms that maximize the margin. ICML Manfred K. Warmuth, Jun Liao, Gunnar Rätsch
2005	—	`conf`	A General and Efficient Multiple Kernel Learning Algorithm. NIPS Sören Sonnenburg, Gunnar Rätsch, Christin Schäfer
2005	J	`jnl`	Classifying 'Drug-likeness' with Kernel-Based Learning Methods. J. Chem. Inf. Model. Klaus-Robert Müller, Gunnar Rätsch, Sören Sonnenburg, Sebastian Mika, Michael Grimm, Nikolaus Heinrich
2005	J	`jnl`	Efficient Margin Maximizing with Boosting. J. Mach. Learn. Res. Gunnar Rätsch, Manfred K. Warmuth
2005	J	`jnl`	Image reconstruction by linear programming. IEEE Trans. Image Process. Koji Tsuda, Gunnar Rätsch
2005	A*	`conf`	Large scale genomic sequence SVM classifiers. ICML Sören Sonnenburg, Gunnar Rätsch, Bernhard Schölkopf
2005	B	`conf`	Learning Interpretable SVMs for Biological Sequence Classification. RECOMB Sören Sonnenburg, Gunnar Rätsch, Christin Schäfer
2005	J	`jnl`	Matrix Exponentiated Gradient Updates for On-line Learning and Bregman Projection. J. Mach. Learn. Res. Koji Tsuda, Gunnar Rätsch, Manfred K. Warmuth
2005	—	`conf`	RASE: recognition of alternatively spliced exons in ISMB (Supplement of Bioinformatics) Gunnar Rätsch, Sören Sonnenburg, Bernhard Schölkopf
2005	—	`—`	Robust boosting via convex optimization Gunnar Rätsch
2004	—	`ed.`	Advanced Lectures on Machine Learning, ML Summer Schools 2003, Canberra, Australia, February 2-14, 2003, Tübingen, Germany, August 4-16, 2003, Revised Lectures Advanced Lectures on Machine Learning Olivier Bousquet, Ulrike von Luxburg, Gunnar Rätsch
2004	—	`conf`	Matrix Exponential Gradient Updates for On-line Learning and Bregman Projection. NIPS Koji Tsuda, Gunnar Rätsch, Manfred K. Warmuth
2003	J	`jnl`	Active Learning with Support Vector Machines in the Drug Discovery Process. J. Chem. Inf. Comput. Sci. Manfred K. Warmuth, Jun Liao, Gunnar Rätsch, Michael Mathieson, Santosh Putta, Christian Lemmen
2003	J	`jnl`	Constructing Descriptive and Discriminative Nonlinear Features: Rayleigh Coefficients in Kernel Feature Spaces. IEEE Trans. Pattern Anal. Mach. Intell. Sebastian Mika, Gunnar Rätsch, Jason Weston, Bernhard Schölkopf, Alexander J. Smola, Klaus-Robert Müller
2003	—	`conf`	Image Reconstruction by Linear Programming. NIPS Koji Tsuda, Gunnar Rätsch
2003	A	`conf`	Robust multi-class boosting. INTERSPEECH Gunnar Rätsch
2002	J	`jnl`	A New Discriminative Kernel from Probabilistic Models. Neural Comput. Koji Tsuda, Motoaki Kawanabe, Gunnar Rätsch, Sören Sonnenburg, Klaus-Robert Müller
2002	—	`conf`	Adapting Codes and Embeddings for Polychotomies. NIPS Gunnar Rätsch, Alexander J. Smola, Sebastian Mika
2002	—	`conf`	An Introduction to Boosting and Leveraging. Machine Learning Summer School Ron Meir, Gunnar Rätsch
2002	J	`jnl`	Constructing Boosting Algorithms from SVMs: An Application to One-Class Classification. IEEE Trans. Pattern Anal. Mach. Intell. Gunnar Rätsch, Sebastian Mika, Bernhard Schölkopf, Klaus-Robert Müller
2002	A*	`conf`	Maximizing the Margin with Boosting. COLT Gunnar Rätsch, Manfred K. Warmuth
2002	C	`conf`	New Methods for Splice Site Recognition. ICANN Sören Sonnenburg, Gunnar Rätsch, Arun K. Jagota, Klaus-Robert Müller
2002	J	`jnl`	Sparse Regression Ensembles in Infinite and Finite Hypothesis Spaces. Mach. Learn. Gunnar Rätsch, Ayhan Demiriz, Kristin P. Bennett
2001	—	`conf`	A New Discriminative Kernel From Probabilistic Models. NIPS Koji Tsuda, Motoaki Kawanabe, Gunnar Rätsch, Sören Sonnenburg, Klaus-Robert Müller
2001	—	`conf`	Active Learning in the Drug Discovery Process. NIPS Manfred K. Warmuth, Gunnar Rätsch, Michael Mathieson, Jun Liao, Christian Lemmen
2001	J	`jnl`	An introduction to kernel-based learning algorithms. IEEE Trans. Neural Networks Klaus-Robert Müller, Sebastian Mika, Gunnar Rätsch, Koji Tsuda, Bernhard Schölkopf
2001	C	`conf`	Learning to Predict the Leave-One-Out Error of Kernel Based Classifiers. ICANN Koji Tsuda, Gunnar Rätsch, Sebastian Mika, Klaus-Robert Müller
2001	—	`conf`	On the Convergence of Leveraging. NIPS Gunnar Rätsch, Sebastian Mika, Manfred K. Warmuth
2001	—	`ch.`	Robustes Boosting durch konvexe Optimierung. Ausgezeichnete Informatikdissertationen Gunnar Rätsch
2001	J	`jnl`	Soft Margins for AdaBoost. Mach. Learn. Gunnar Rätsch, Takashi Onoda, Klaus-Robert Müller
2000	—	`conf`	A Mathematical Programming Approach to the Kernel Fisher Algorithm. NIPS Sebastian Mika, Gunnar Rätsch, Klaus-Robert Müller
2000	A*	`conf`	Barrier Boosting. COLT Gunnar Rätsch, Manfred K. Warmuth, Sebastian Mika, Takashi Onoda, Steven Lemm, Klaus-Robert Müller
2000	J	`jnl`	Engineering support vector machine kernels that recognize translation initiation sites. Bioinform. Alexander Zien, Gunnar Rätsch, Sebastian Mika, Bernhard Schölkopf, Thomas Lengauer, Klaus-Robert Müller
2000	B	`conf`	Robust Ensemble Learning for Data Mining. PAKDD Gunnar Rätsch, Bernhard Schölkopf, Alexander J. Smola, Sebastian Mika, Takashi Onoda, Klaus-Robert Müller
1999	—	`conf`	Engineering Support Vector Machine Kerneis That Recognize Translation Initialion Sites. German Conference on Bioinformatics Alexander Zien, Gunnar Rätsch, Sebastian Mika, Bernhard Schölkopf, Christian Lemmen, Alexander J. Smola, Thomas Lengauer, Klaus-Robert Müller
1999	J	`jnl`	Input space versus feature space in kernel-based methods. IEEE Trans. Neural Networks Bernhard Schölkopf, Sebastian Mika, Christopher J. C. Burges, Phil Knirsch, Klaus-Robert Müller, Gunnar Rätsch, Alexander J. Smola
1999	—	`conf`	Invariant Feature Extraction and Classification in Kernel Spaces. NIPS Sebastian Mika, Gunnar Rätsch, Jason Weston, Bernhard Schölkopf, Alexander J. Smola, Klaus-Robert Müller
1999	—	`conf`	v-Arc: Ensemble Learning in the Presence of Outliers. NIPS Gunnar Rätsch, Bernhard Schölkopf, Alexander J. Smola, Klaus-Robert Müller, Takashi Onoda, Sebastian Mika
1998	B	`conf`	An Improvement of AdaBoost to Avoid Overfitting. ICONIP Gunnar Rätsch, Takashi Onoda, Klaus-Robert Müller
1998	—	`conf`	Kernel PCA and De-Noising in Feature Spaces. NIPS Sebastian Mika, Bernhard Schölkopf, Alexander J. Smola, Klaus-Robert Müller, Matthias Scholz, Gunnar Rätsch
1998	—	`conf`	Regularizing AdaBoost. NIPS Gunnar Rätsch, Takashi Onoda, Klaus-Robert Müller
1997	C	`conf`	Predicting Time Series with Support Vector Machines. ICANN Klaus-Robert Müller, Alexander J. Smola, Gunnar Rätsch, Bernhard Schölkopf, Jens Kohlmorgen, Vladimir Vapnik