Gunnar Rätsch

ORCID

Publication Activity (10 Years)

Years Active: 1997-2024
Publications (10 Years): 102

Top Venues

Publications

Alexandru Meterez, Amir Joudaki, Francesco Orabona, Alexander Immer, Gunnar Rätsch, Hadi Daneshmand
Towards Training Without Depth Limits: Batch Normalization Without Gradient Explosion. ICLR (2024)
Hugo Yèche, Manuel Burger, Dinara Veshchezerova, Gunnar Rätsch
Dynamic Survival Analysis for Early Event Prediction. CoRR (2024)
Alizée Pace, Hugo Yèche, Bernhard Schölkopf, Gunnar Rätsch, Guy Tennenholtz
Delphic Offline Reinforcement Learning under Nonidentifiable Hidden Confounding. ICLR (2024)
Fedor Sergeev, Paola Malsot, Gunnar Rätsch, Vincent Fortuin
Towards Dynamic Feature Acquisition on Medical Time Series by Maximizing Conditional Mutual Information. CoRR (2024)
Fabian Baldenweg, Manuel Burger, Gunnar Rätsch, Rita Kuznetsova
Multi-Modal Contrastive Learning for Online Clinical Time-Series Applications. CoRR (2024)
Alizée Pace, Bernhard Schölkopf, Gunnar Rätsch, Giorgia Ramponi
Preference Elicitation for Offline Reinforcement Learning. CoRR (2024)
Shkurta Gashi, Pietro Oldrati, Max Möbus, Marc Hilty, Liliana Barrios, Firat Ozdemir, Veronika Kana, Andreas Lutterotti, Gunnar Rätsch, Christian Holz
Modeling multiple sclerosis using mobile and wearable sensor data. npj Digit. Medicine 7 (1) (2024)
Samyak Jain, Manuel Burger, Gunnar Rätsch, Rita Kuznetsova
Knowledge Graph Representations to enhance Intensive Care Time-Series Predictions. CoRR (2023)
Alexander Immer, Tycho F. A. van der Ouderaa, Mark van der Wilk, Gunnar Rätsch, Bernhard Schölkopf
Stochastic Marginal Likelihood Gradients using Neural Tangent Kernels. ICML (2023)
Rita Kuznetsova, Alizée Pace, Manuel Burger, Hugo Yèche, Gunnar Rätsch
On the Importance of Step-wise Embeddings for Heterogeneous Clinical Time-Series. CoRR (2023)
Alexander Immer, Tycho F. A. van der Ouderaa, Mark van der Wilk, Gunnar Rätsch, Bernhard Schölkopf
Stochastic Marginal Likelihood Gradients using Neural Tangent Kernels. CoRR (2023)
Alexandru Meterez, Amir Joudaki, Francesco Orabona, Alexander Immer, Gunnar Rätsch, Hadi Daneshmand
Towards Training Without Depth Limits: Batch Normalization Without Gradient Explosion. CoRR (2023)
Yurong Hu, Manuel Burger, Gunnar Rätsch, Rita Kuznetsova
Language Model Training Paradigms for Clinical Feature Embeddings. CoRR (2023)
Manuel Burger, Gunnar Rätsch, Rita Kuznetsova
Multi-modal Graph Learning over UMLS Knowledge Graphs. ML4H@NeurIPS (2023)
Kouroche Bouchiat, Alexander Immer, Hugo Yèche, Gunnar Rätsch, Vincent Fortuin
Laplace-Approximated Neural Additive Models: Improving Interpretability with Bayesian Inference. CoRR (2023)
Kacper Kapusniak, Manuel Burger, Gunnar Rätsch, Amir Joudaki
Learning Genomic Sequence Representations using Graph Neural Networks over De Bruijn Graphs. CoRR (2023)
Manuel Burger, Gunnar Rätsch, Rita Kuznetsova
Multi-modal Graph Learning over UMLS Knowledge Graphs. CoRR (2023)
Hugo Yèche, Alizée Pace, Gunnar Rätsch, Rita Kuznetsova
Temporal Label Smoothing for Early Event Prediction. ICML (2023)
Rita Kuznetsova, Alizée Pace, Manuel Burger, Hugo Yèche, Gunnar Rätsch
On the Importance of Step-wise Embeddings for Heterogeneous Clinical Time-Series. ML4H@NeurIPS (2023)
Alizée Pace, Hugo Yèche, Bernhard Schölkopf, Gunnar Rätsch, Guy Tennenholtz
Delphic Offline Reinforcement Learning under Nonidentifiable Hidden Confounding. CoRR (2023)
Olga Mineeva, Daniel Danciu, Bernhard Schölkopf, Ruth E. Ley, Gunnar Rätsch, Nicholas D. Youngblut
ResMiCo: Increasing the quality of metagenome-assembled genomes with deep learning. PLoS Comput. Biol. 19 (5) (2023)
Mikhail Karasikov, Harun Mustafa, Gunnar Rätsch, André Kahles
Lossless Indexing with Counting de Bruijn Graphs. RECOMB (2022)
Gideon Dresdner, Maria-Luiza Vladarean, Gunnar Rätsch, Francesco Locatello, Volkan Cevher, Alp Yurtsever
Faster One-Sample Stochastic Conditional Gradient Method for Composite Convex Minimization. CoRR (2022)
Alexander Immer, Tycho F. A. van der Ouderaa, Vincent Fortuin, Gunnar Rätsch, Mark van der Wilk
Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations. CoRR (2022)
Gideon Dresdner, Maria-Luiza Vladarean, Gunnar Rätsch, Francesco Locatello, Volkan Cevher, Alp Yurtsever
Faster One-Sample Stochastic Conditional Gradient Method for Composite Convex Minimization. AISTATS (2022)
Vincent Fortuin, Adrià Garriga-Alonso, Sebastian W. Ober, Florian Wenzel, Gunnar Rätsch, Richard E. Turner, Mark van der Wilk, Laurence Aitchison
Bayesian Neural Network Priors Revisited. ICLR (2022)
Hana Rozhonová, Daniel Danciu, Stefan Stark, Gunnar Rätsch, André Kahles, Kjong-Van Lehmann
SECEDO: SNV-based subclone detection using ultra-low coverage single-cell DNA sequencing. Bioinform. 38 (18) (2022)
Alexander Immer, Tycho F. A. van der Ouderaa, Gunnar Rätsch, Vincent Fortuin, Mark van der Wilk
Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations. NeurIPS (2022)
Hugo Yèche, Alizée Pace, Gunnar Rätsch, Rita Kuznetsova
Temporal Label Smoothing for Early Prediction of Adverse Events. CoRR (2022)
Kjong-Van Lehmann, André Kahles, Magdalena Murr, Gunnar Rätsch
RNA Instant Quality Check: Alignment-Free RNA-Degradation Detection. J. Comput. Biol. 29 (8) (2022)
Severin Husmann, Hugo Yèche, Gunnar Rätsch, Rita Kuznetsova
On the Importance of Clinical Notes in Multi-modal Learning for EHR Data. CoRR (2022)
Vincent Fortuin, Gideon Dresdner, Heiko Strathmann, Gunnar Rätsch
Sparse Gaussian Processes on Discrete Domains. IEEE Access 9 (2021)
Alexander Immer, Matthias Bauer, Vincent Fortuin, Gunnar Rätsch, Mohammad Emtiyaz Khan
Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning. ICML (2021)
Laura Manduchi, Matthias Hüser, Martin Faltys, Julia E. Vogt, Gunnar Rätsch, Vincent Fortuin
T-DPSOM: an interpretable clustering method for unsupervised learning of patient health states. CHIL (2021)
Gideon Dresdner, Saurav Shekhar, Fabian Pedregosa, Francesco Locatello, Gunnar Rätsch
Boosting Variational Inference With Locally Adaptive Step-Sizes. IJCAI (2021)
Gideon Dresdner, Saurav Shekhar, Fabian Pedregosa, Francesco Locatello, Gunnar Rätsch
Boosting Variational Inference With Locally Adaptive Step-Sizes. CoRR (2021)
Hugo Yèche, Gideon Dresdner, Francesco Locatello, Matthias Hüser, Gunnar Rätsch
Neighborhood Contrastive Learning Applied to Online Patient Monitoring. ICML (2021)
Matthias Hüser, Martin Faltys, Xinrui Lyu, Chris Barber, Stephanie L. Hyland, Tobias M. Merz, Gunnar Rätsch
Early prediction of respiratory failure in the intensive care unit. CoRR (2021)
Daniel Danciu, Mikhail Karasikov, Harun Mustafa, André Kahles, Gunnar Rätsch
Topology-based sparsification of graph annotations. Bioinform. 37 (Supplement) (2021)
Vincent Fortuin, Adrià Garriga-Alonso, Florian Wenzel, Gunnar Rätsch, Richard E. Turner, Mark van der Wilk, Laurence Aitchison
Bayesian Neural Network Priors Revisited. CoRR (2021)
Alexander Immer, Matthias Bauer, Vincent Fortuin, Gunnar Rätsch, Mohammad Emtiyaz Khan
Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning. CoRR (2021)
Hugo Yèche, Gideon Dresdner, Francesco Locatello, Matthias Hüser, Gunnar Rätsch
Neighborhood Contrastive Learning Applied to Online Patient Monitoring. CoRR (2021)
Jonathan Heitz, Joanna Ficek, Martin Faltys, Tobias M. Merz, Gunnar Rätsch, Matthias Hüser
WRSE - a non-parametric weighted-resolution ensemble for predicting individual survival distributions in the ICU. SPACA (2021)
Hugo Yèche, Rita Kuznetsova, Marc Zimmermann, Matthias Hüser, Xinrui Lyu, Martin Faltys, Gunnar Rätsch
HiRID-ICU-Benchmark - A Comprehensive Machine Learning Benchmark on High-resolution ICU Data. NeurIPS Datasets and Benchmarks (2021)
Simon Bing, Vincent Fortuin, Gunnar Rätsch
On Disentanglement in Gaussian Process Variational Autoencoders. CoRR (2021)
Metod Jazbec, Matthew Ashman, Vincent Fortuin, Michael Pearce, Stephan Mandt, Gunnar Rätsch
Scalable Gaussian Process Variational Autoencoders. AISTATS (2021)
Linda K. Sundermann, Jeff Wintersinger, Gunnar Rätsch, Jens Stoye, Quaid Morris
Reconstructing tumor evolutionary histories and clone trees in polynomial-time with SubMARine. PLoS Comput. Biol. 17 (1) (2021)
Hugo Yèche, Rita Kuznetsova, Marc Zimmermann, Matthias Hüser, Xinrui Lyu, Martin Faltys, Gunnar Rätsch
HiRID-ICU-Benchmark - A Comprehensive Machine Learning Benchmark on High-resolution ICU Data. CoRR (2021)
Jonathan Heitz, Joanna Ficek, Martin Faltys, Tobias M. Merz, Gunnar Rätsch, Matthias Hüser
WRSE - a non-parametric weighted-resolution ensemble for predicting individual survival distributions in the ICU. CoRR (2020)
Xinrui Lyu, Jean Garret, Gunnar Rätsch, Kjong-Van Lehmann
Mutational signature learning with supervised negative binomial non-negative matrix factorization. Bioinform. 36 (Supplement-1) (2020)
Metod Jazbec, Vincent Fortuin, Michael Pearce, Stephan Mandt, Gunnar Rätsch
Scalable Gaussian Process Variational Autoencoders. CoRR (2020)
Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem
A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation. CoRR (2020)
Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem
A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation. J. Mach. Learn. Res. 21 (2020)
Maciej Besta, Raghavendra Kanakagiri, Harun Mustafa, Mikhail Karasikov, Gunnar Rätsch, Torsten Hoefler, Edgar Solomonik
Communication-Efficient Jaccard similarity for High-Performance Distributed Genome Comparisons. IPDPS (2020)
Francesco Locatello, Ben Poole, Gunnar Rätsch, Bernhard Schölkopf, Olivier Bachem, Michael Tschannen
Weakly-Supervised Disentanglement Without Compromises. ICML (2020)
Pesho Ivanov, Benjamin Bichsel, Harun Mustafa, André Kahles, Gunnar Rätsch, Martin T. Vechev
AStarix: Fast and Optimal Sequence-to-Graph Alignment. RECOMB (2020)
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
Genomic basis for RNA alterations in cancer. Nat. 578 (7793) (2020)
Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem
A Commentary on the Unsupervised Learning of Disentangled Representations. AAAI (2020)
Francesco Locatello, Michael Tschannen, Stefan Bauer, Gunnar Rätsch, Bernhard Schölkopf, Olivier Bachem
Disentangling Factors of Variations Using Few Labels. ICLR (2020)
Vincent Fortuin, Dmitry Baranchuk, Gunnar Rätsch, Stephan Mandt
GP-VAE: Deep Probabilistic Time Series Imputation. AISTATS (2020)
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
SPHN/PHRT: Forming a Swiss-Wide Infrastructure for Data-Driven Sepsis Research. MIE (2020)
Francesco Locatello, Ben Poole, Gunnar Rätsch, Bernhard Schölkopf, Olivier Bachem, Michael Tschannen
Weakly-Supervised Disentanglement Without Compromises. CoRR (2020)
Mikhail Karasikov, Harun Mustafa, Amir Joudaki, Sara Javadzadeh-No, Gunnar Rätsch, André Kahles
Sparse Binary Relation Representations for Genome Graph Annotation. J. Comput. Biol. 27 (4) (2020)
Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem
A Commentary on the Unsupervised Learning of Disentangled Representations. CoRR (2020)
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
Machine learning for early prediction of circulatory failure in the intensive care unit. CoRR (2019)
Vincent Fortuin, Matthias Hüser, Francesco Locatello, Heiko Strathmann, Gunnar Rätsch
SOM-VAE: Interpretable Discrete Representation Learning on Time Series. ICLR (Poster) (2019)
Andreas Georgiou, Vincent Fortuin, Harun Mustafa, Gunnar Rätsch
Deep Multiple Instance Learning for Taxonomic Classification of Metagenomic read sets. CoRR (2019)
Stefan G. Stark, Stephanie L. Hyland, Melanie Fernandes Pradier, Kjong Lehmann, Andreas Wicki, Fernando Pérez-Cruz, Julia E. Vogt, Gunnar Rätsch
Unsupervised Extraction of Phenotypes from Cancer Clinical Notes for Association Studies. CoRR (2019)
Laura Manduchi, Matthias Hüser, Gunnar Rätsch, Vincent Fortuin
Variational PSOM: Deep Probabilistic Clustering with Self-Organizing Maps. CoRR (2019)
Vincent Fortuin, Gunnar Rätsch, Stephan Mandt
Multivariate Time Series Imputation with Variational Autoencoders. CoRR (2019)
Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem
Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations. RML@ICLR (2019)
Francesco Locatello, Michael Tschannen, Stefan Bauer, Gunnar Rätsch, Bernhard Schölkopf, Olivier Bachem
Disentangling Factors of Variation Using Few Labels. CoRR (2019)
Maciej Besta, Raghavendra Kanakagiri, Harun Mustafa, Mikhail Karasikov, Gunnar Rätsch, Torsten Hoefler, Edgar Solomonik
Communication-Efficient Jaccard Similarity for High-Performance Distributed Genome Comparisons. CoRR (2019)
Vincent Fortuin, Gunnar Rätsch
Deep Mean Functions for Meta-Learning in Gaussian Processes. CoRR (2019)
Harun Mustafa, Ingo Schilken, Mikhail Karasikov, Carsten Eickhoff, Gunnar Rätsch, André Kahles
Dynamic compression schemes for graph coloring. Bioinform. 35 (3) (2019)
Mikhail Karasikov, Harun Mustafa, Amir Joudaki, Sara Javadzadeh-No, Gunnar Rätsch, André Kahles
Sparse Binary Relation Representations for Genome Graph Annotation. RECOMB (2019)
Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem
Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations. ICML (2019)
Francesco Locatello, Gideon Dresdner, Rajiv Khanna, Isabel Valera, Gunnar Rätsch
Boosting Black Box Variational Inference. NeurIPS (2018)
Francesco Locatello, Anant Raj, Sai Praneeth Karimireddy, Gunnar Rätsch, Bernhard Schölkopf, Sebastian U. Stich, Martin Jaggi
On Matching Pursuit and Coordinate Descent. ICML (2018)
Francesco Locatello, Gideon Dresdner, Rajiv Khanna, Isabel Valera, Gunnar Rätsch
Boosting Black Box Variational Inference. CoRR (2018)
Francesco Locatello, Anant Raj, Sai Praneeth Karimireddy, Gunnar Rätsch, Bernhard Schölkopf, Sebastian U. Stich, Martin Jaggi
Revisiting First-Order Convex Optimization Over Linear Spaces. CoRR (2018)
Stephanie L. Hyland, Matthias Hüser, Xinrui Lyu, Martin Faltys, Tobias Merz, Gunnar Rätsch
Predicting circulatory system deterioration in intensive care unit patients. AIH@IJCAI (2018)
Francesco Locatello, Rajiv Khanna, Joydeep Ghosh, Gunnar Rätsch
Boosting Variational Inference: an Optimization Perspective. AISTATS (2018)
Xinrui Lyu, Matthias Hüser, Stephanie L. Hyland, George Zerveas, Gunnar Rätsch
Improving Clinical Predictions through Unsupervised Time Series Representation Learning. CoRR (2018)
Francesco Locatello, Damien Vincent, Ilya O. Tolstikhin, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf
Clustering Meets Implicit Generative Models. CoRR (2018)
Vincent Fortuin, Gideon Dresdner, Heiko Strathmann, Gunnar Rätsch
Scalable Gaussian Processes on Discrete Domains. CoRR (2018)
Vincent Fortuin, Matthias Hüser, Francesco Locatello, Heiko Strathmann, Gunnar Rätsch
Deep Self-Organization: Interpretable Discrete Representation Learning on Time Series. CoRR (2018)
Stephanie L. Hyland, Martin Faltys, Matthias Hüser, Xinrui Lyu, Cristóbal Esteban, Gunnar Rätsch, Tobias Merz
A Machine Learning-based Early Warning System for Circulatory System Deterioration in Intensive Care Unit Patients. AMIA (2018)
Francesco Locatello, Damien Vincent, Ilya O. Tolstikhin, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf
Clustering Meets Implicit Generative Models. ICLR (Workshop) (2018)
Francesco Locatello, Michael Tschannen, Gunnar Rätsch, Martin Jaggi
Greedy Algorithms for Cone Constrained Optimization with Convergence Guarantees. NIPS (2017)
Yi Zhong, Theofanis Karaletsos, Philipp Drewe, Vipin T. Sreedharan, David Kuo, Kamini Singh, Hans-Guido Wendel, Gunnar Rätsch
RiboDiff: detecting changes of mRNA translation efficiency from ribosome footprints. Bioinform. 33 (1) (2017)
Cristóbal Esteban, Stephanie L. Hyland, Gunnar Rätsch
Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs. CoRR (2017)
Francesco Locatello, Rajiv Khanna, Joydeep Ghosh, Gunnar Rätsch
Boosting Variational Inference: an Optimization Perspective. CoRR (2017)
Francesco Locatello, Michael Tschannen, Gunnar Rätsch, Martin Jaggi
Greedy Algorithms for Cone Constrained Optimization with Convergence Guarantees. CoRR (2017)
Stephanie L. Hyland, Gunnar Rätsch
Learning Unitary Operators with Help From u(n). AAAI (2017)
André Kahles, Cheng Soon Ong, Yi Zhong, Gunnar Rätsch
: identification, quantification and testing of alternative splicing events from RNA-Seq data. Bioinform. 32 (12) (2016)
Theofanis Karaletsos, Serge J. Belongie, Gunnar Rätsch
When crowds hold privileges: Bayesian unsupervised representation learning with oracle constraints. ICLR (Poster) (2016)
Stephanie L. Hyland, Theofanis Karaletsos, Gunnar Rätsch
Knowledge Transfer with Medical Language Embeddings. CoRR (2016)
Stephanie L. Hyland, Gunnar Rätsch
Learning Unitary Operators with Help From u(n). CoRR (2016)
Stephanie L. Hyland, Theofanis Karaletsos, Gunnar Rätsch
A Generative Model of Words and Relationships from Multiple Sources. AAAI (2016)

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Publication Activity (10 Years)

Top Topics

Top Venues

Gunnar Rätsch