default search action
Gunnar Rätsch
Person information
- affiliation: ETH Zurich, Switzerland
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2024
- [j58]Shkurta Gashi
, Pietro Oldrati
Modeling multiple sclerosis using mobile and wearable sensor data. npj Digit. Medicine 7(1) (2024) - [c89]Alexandru Meterez, Amir Joudaki, Francesco Orabona, Alexander Immer, Gunnar Rätsch, Hadi Daneshmand:
Towards Training Without Depth Limits: Batch Normalization Without Gradient Explosion. ICLR 2024 - [c88]Alizée Pace, Hugo Yèche, Bernhard Schölkopf, Gunnar Rätsch, Guy Tennenholtz:
Delphic Offline Reinforcement Learning under Nonidentifiable Hidden Confounding. ICLR 2024 - [c87]Kouroche Bouchiat, Alexander Immer, Hugo Yèche, Gunnar Rätsch, Vincent Fortuin:
Improving Neural Additive Models with Bayesian Principles. ICML 2024 - [i54]Hugo Yèche, Manuel Burger, Dinara Veshchezerova, Gunnar Rätsch:
Dynamic Survival Analysis for Early Event Prediction. CoRR abs/2403.12818 (2024) - [i53]Fabian Baldenweg, Manuel Burger, Gunnar Rätsch, Rita Kuznetsova:
Multi-Modal Contrastive Learning for Online Clinical Time-Series Applications. CoRR abs/2403.18316 (2024) - [i52]Alizée Pace, Bernhard Schölkopf, Gunnar Rätsch, Giorgia Ramponi:
Preference Elicitation for Offline Reinforcement Learning. CoRR abs/2406.18450 (2024) - [i51]Fedor Sergeev, Paola Malsot, Gunnar Rätsch, Vincent Fortuin:
Towards Dynamic Feature Acquisition on Medical Time Series by Maximizing Conditional Mutual Information. CoRR abs/2407.13429 (2024) - [i50]Sam Houliston, Alizée Pace, Alexander Immer, Gunnar Rätsch:
Uncertainty-Penalized Direct Preference Optimization. CoRR abs/2410.20187 (2024) - 2023
- [j57]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) - [c86]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: 14333-14352 - [c85]Hugo Yèche, Alizée Pace, Gunnar Rätsch, Rita Kuznetsova:
Temporal Label Smoothing for Early Event Prediction. ICML 2023: 39913-39938 - [c84]Manuel Burger, Gunnar Rätsch, Rita Kuznetsova:
Multi-modal Graph Learning over UMLS Knowledge Graphs. ML4H@NeurIPS 2023: 52-81 - [c83]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: 268-291 - [i49]Kouroche Bouchiat, Alexander Immer, Hugo Yèche, Gunnar Rätsch, Vincent Fortuin:
Laplace-Approximated Neural Additive Models: Improving Interpretability with Bayesian Inference. CoRR abs/2305.16905 (2023) - [i48]Alizée Pace, Hugo Yèche, Bernhard Schölkopf, Gunnar Rätsch, Guy Tennenholtz:
Delphic Offline Reinforcement Learning under Nonidentifiable Hidden Confounding. CoRR abs/2306.01157 (2023) - [i47]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 abs/2306.03968 (2023) - [i46]Manuel Burger, Gunnar Rätsch, Rita Kuznetsova
Multi-modal Graph Learning over UMLS Knowledge Graphs. CoRR abs/2307.04461 (2023) - [i45]Alexandru Meterez, Amir Joudaki
Towards Training Without Depth Limits: Batch Normalization Without Gradient Explosion. CoRR abs/2310.02012 (2023) - [i44]Yurong Hu, Manuel Burger
Language Model Training Paradigms for Clinical Feature Embeddings. CoRR abs/2311.00768 (2023) - [i43]Samyak Jain, Manuel Burger, Gunnar Rätsch, Rita Kuznetsova:
Knowledge Graph Representations to enhance Intensive Care Time-Series Predictions. CoRR abs/2311.07180 (2023) - [i42]Rita Kuznetsova, Alizée Pace, Manuel Burger, Hugo Yèche, Gunnar Rätsch:
On the Importance of Step-wise Embeddings for Heterogeneous Clinical Time-Series. CoRR abs/2311.08902 (2023) - [i41]Kacper Kapusniak, Manuel Burger, Gunnar Rätsch, Amir Joudaki
Learning Genomic Sequence Representations using Graph Neural Networks over De Bruijn Graphs. CoRR abs/2312.03865 (2023) - 2022
- [j56]Hana Rozhonová, Daniel Danciu, Stefan Stark
SECEDO: SNV-based subclone detection using ultra-low coverage single-cell DNA sequencing. Bioinform. 38(18): 4293-4300 (2022) - [j55]Kjong-Van Lehmann
RNA Instant Quality Check: Alignment-Free RNA-Degradation Detection. J. Comput. Biol. 29(8): 857-866 (2022) - [c82]Gideon Dresdner, Maria-Luiza Vladarean, Gunnar Rätsch, Francesco Locatello, Volkan Cevher
Faster One-Sample Stochastic Conditional Gradient Method for Composite Convex Minimization. AISTATS 2022: 8439-8457 - [c81]Vincent Fortuin
Bayesian Neural Network Priors Revisited. ICLR 2022 - [c80]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 - [c79]Mikhail Karasikov
Lossless Indexing with Counting de Bruijn Graphs. RECOMB 2022: 374-376 - [i40]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 abs/2202.10638 (2022) - [i39]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 abs/2202.13212 (2022) - [i38]Hugo Yèche, Alizée Pace, Gunnar Rätsch, Rita Kuznetsova
Temporal Label Smoothing for Early Prediction of Adverse Events. CoRR abs/2208.13764 (2022) - [i37]Severin Husmann, Hugo Yèche, Gunnar Rätsch, Rita Kuznetsova
On the Importance of Clinical Notes in Multi-modal Learning for EHR Data. CoRR abs/2212.03044 (2022) - 2021
- [j54]Vincent Fortuin
Sparse Gaussian Processes on Discrete Domains. IEEE Access 9: 76750-76758 (2021) - [j53]Daniel Danciu, Mikhail Karasikov, Harun Mustafa
Topology-based sparsification of graph annotations. Bioinform. 37(Supplement): 169-176 (2021) - [j52]Linda K. Sundermann
Reconstructing tumor evolutionary histories and clone trees in polynomial-time with SubMARine. PLoS Comput. Biol. 17(1) (2021) - [c78]Metod Jazbec, Matthew Ashman, Vincent Fortuin, Michael Pearce, Stephan Mandt, Gunnar Rätsch:
Scalable Gaussian Process Variational Autoencoders. AISTATS 2021: 3511-3519 - [c77]Laura Manduchi, Matthias Hüser, Martin Faltys
T-DPSOM: an interpretable clustering method for unsupervised learning of patient health states. CHIL 2021: 236-245 - [c76]Alexander Immer, Matthias Bauer, Vincent Fortuin, Gunnar Rätsch, Mohammad Emtiyaz Khan:
Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning. ICML 2021: 4563-4573 - [c75]Hugo Yèche, Gideon Dresdner, Francesco Locatello, Matthias Hüser, Gunnar Rätsch:
Neighborhood Contrastive Learning Applied to Online Patient Monitoring. ICML 2021: 11964-11974 - [c74]Gideon Dresdner, Saurav Shekhar, Fabian Pedregosa, Francesco Locatello, Gunnar Rätsch:
Boosting Variational Inference With Locally Adaptive Step-Sizes. IJCAI 2021: 2337-2343 - [c73]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 - [c72]Jonathan Heitz, Joanna Ficek, Martin Faltys
WRSE - a non-parametric weighted-resolution ensemble for predicting individual survival distributions in the ICU. SPACA 2021: 54-69 - [i36]Simon Bing, Vincent Fortuin, Gunnar Rätsch:
On Disentanglement in Gaussian Process Variational Autoencoders. CoRR abs/2102.05507 (2021) - [i35]Vincent Fortuin, Adrià Garriga-Alonso, Florian Wenzel, Gunnar Rätsch, Richard E. Turner, Mark van der Wilk, Laurence Aitchison:
Bayesian Neural Network Priors Revisited. CoRR abs/2102.06571 (2021) - [i34]Alexander Immer, Matthias Bauer, Vincent Fortuin, Gunnar Rätsch, Mohammad Emtiyaz Khan:
Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning. CoRR abs/2104.04975 (2021) - [i33]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 abs/2105.05728 (2021) - [i32]Gideon Dresdner, Saurav Shekhar, Fabian Pedregosa, Francesco Locatello, Gunnar Rätsch:
Boosting Variational Inference With Locally Adaptive Step-Sizes. CoRR abs/2105.09240 (2021) - [i31]Hugo Yèche, Gideon Dresdner, Francesco Locatello, Matthias Hüser, Gunnar Rätsch:
Neighborhood Contrastive Learning Applied to Online Patient Monitoring. CoRR abs/2106.05142 (2021) - [i30]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 abs/2111.08536 (2021) - 2020
- [j51]Xinrui Lyu, Jean Garret, Gunnar Rätsch, Kjong-Van Lehmann
Mutational signature learning with supervised negative binomial non-negative matrix factorization. Bioinform. 36(Supplement-1): i154-i160 (2020) - [j50]Mikhail Karasikov, Harun Mustafa
Sparse Binary Relation Representations for Genome Graph Annotation. J. Comput. Biol. 27(4): 626-639 (2020) - [j49]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: 209:1-209:62 (2020) - [j48]Claudia Calabrese
Genomic basis for RNA alterations in cancer. Nat. 578(7793): 129-136 (2020) - [c71]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: 13681-13684 - [c70]Vincent Fortuin, Dmitry Baranchuk, Gunnar Rätsch, Stephan Mandt:
GP-VAE: Deep Probabilistic Time Series Imputation. AISTATS 2020: 1651-1661 - [c69]Francesco Locatello, Michael Tschannen, Stefan Bauer, Gunnar Rätsch, Bernhard Schölkopf, Olivier Bachem:
Disentangling Factors of Variations Using Few Labels. ICLR 2020 - [c68]Francesco Locatello, Ben Poole, Gunnar Rätsch, Bernhard Schölkopf, Olivier Bachem, Michael Tschannen:
Weakly-Supervised Disentanglement Without Compromises. ICML 2020: 6348-6359 - [c67]Maciej Besta, Raghavendra Kanakagiri, Harun Mustafa
Communication-Efficient Jaccard similarity for High-Performance Distributed Genome Comparisons. IPDPS 2020: 1122-1132 - [c66]Adrian Egli
SPHN/PHRT: Forming a Swiss-Wide Infrastructure for Data-Driven Sepsis Research. MIE 2020: 1163-1167 - [c65]Pesho Ivanov
AStarix: Fast and Optimal Sequence-to-Graph Alignment. RECOMB 2020: 104-119 - [i29]Francesco Locatello, Ben Poole, Gunnar Rätsch, Bernhard Schölkopf, Olivier Bachem, Michael Tschannen:
Weakly-Supervised Disentanglement Without Compromises. CoRR abs/2002.02886 (2020) - [i28]Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem:
A Commentary on the Unsupervised Learning of Disentangled Representations. CoRR abs/2007.14184 (2020) - [i27]Metod Jazbec, Vincent Fortuin, Michael Pearce, Stephan Mandt, Gunnar Rätsch:
Scalable Gaussian Process Variational Autoencoders. CoRR abs/2010.13472 (2020) - [i26]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 abs/2010.14766 (2020) - [i25]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 abs/2011.00865 (2020)
2010 – 2019
- 2019
- [j47]Harun Mustafa
Dynamic compression schemes for graph coloring. Bioinform. 35(3): 407-414 (2019) - [c64]Vincent Fortuin
SOM-VAE: Interpretable Discrete Representation Learning on Time Series. ICLR (Poster) 2019 - [c63]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 - [c62]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: 4114-4124 - [c61]Mikhail Karasikov
Sparse Binary Relation Representations for Genome Graph Annotation. RECOMB 2019: 120-135 - [i24]Vincent Fortuin, Gunnar Rätsch:
Deep Mean Functions for Meta-Learning in Gaussian Processes. CoRR abs/1901.08098 (2019) - [i23]Stephanie L. Hyland, Martin Faltys, Matthias Hüser, Xinrui Lyu, Thomas Gumbsch, Cristóbal Esteban, Christian Bock, Max Horn, Michael Moor, Bastian Rieck
Machine learning for early prediction of circulatory failure in the intensive care unit. CoRR abs/1904.07990 (2019) - [i22]Stefan G. Stark, Stephanie L. Hyland, Melanie Fernandes Pradier, Kjong-Van Lehmann, Andreas Wicki, Fernando Pérez-Cruz, Julia E. Vogt, Gunnar Rätsch:
Unsupervised Extraction of Phenotypes from Cancer Clinical Notes for Association Studies. CoRR abs/1904.12973 (2019) - [i21]Francesco Locatello, Michael Tschannen, Stefan Bauer, Gunnar Rätsch, Bernhard Schölkopf, Olivier Bachem:
Disentangling Factors of Variation Using Few Labels. CoRR abs/1905.01258 (2019) - [i20]Vincent Fortuin, Gunnar Rätsch, Stephan Mandt:
Multivariate Time Series Imputation with Variational Autoencoders. CoRR abs/1907.04155 (2019) - [i19]Andreas Georgiou, Vincent Fortuin, Harun Mustafa
Deep Multiple Instance Learning for Taxonomic Classification of Metagenomic read sets. CoRR abs/1909.13146 (2019) - [i18]Laura Manduchi, Matthias Hüser, Gunnar Rätsch, Vincent Fortuin:
Variational PSOM: Deep Probabilistic Clustering with Self-Organizing Maps. CoRR abs/1910.01590 (2019) - [i17]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 abs/1911.04200 (2019) - 2018
- [c60]Francesco Locatello, Rajiv Khanna, Joydeep Ghosh, Gunnar Rätsch:
Boosting Variational Inference: an Optimization Perspective. AISTATS 2018: 464-472 - [c59]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 - [c58]Francesco Locatello, Damien Vincent, Ilya O. Tolstikhin, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf:
Clustering Meets Implicit Generative Models. ICLR (Workshop) 2018 - [c57]Francesco Locatello, Anant Raj, Sai Praneeth Karimireddy, Gunnar Rätsch, Bernhard Schölkopf, Sebastian U. Stich, Martin Jaggi:
On Matching Pursuit and Coordinate Descent. ICML 2018: 3204-3213 - [c56]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: 87-92 - [c55]Francesco Locatello, Gideon Dresdner, Rajiv Khanna, Isabel Valera
Boosting Black Box Variational Inference. NeurIPS 2018: 3405-3415 - [i16]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 abs/1803.09539 (2018) - [i15]Francesco Locatello, Damien Vincent, Ilya O. Tolstikhin, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf:
Clustering Meets Implicit Generative Models. CoRR abs/1804.11130 (2018) - [i14]Francesco Locatello, Gideon Dresdner, Rajiv Khanna, Isabel Valera, Gunnar Rätsch:
Boosting Black Box Variational Inference. CoRR abs/1806.02185 (2018) - [i13]Vincent Fortuin, Matthias Hüser, Francesco Locatello, Heiko Strathmann, Gunnar Rätsch:
Deep Self-Organization: Interpretable Discrete Representation Learning on Time Series. CoRR abs/1806.02199 (2018) - [i12]Vincent Fortuin, Gideon Dresdner, Heiko Strathmann, Gunnar Rätsch:
Scalable Gaussian Processes on Discrete Domains. CoRR abs/1810.10368 (2018) - [i11]Xinrui Lyu, Matthias Hüser, Stephanie L. Hyland, George Zerveas
Improving Clinical Predictions through Unsupervised Time Series Representation Learning. CoRR abs/1812.00490 (2018) - 2017
- [j46]Yi Zhong, Theofanis Karaletsos, Philipp Drewe, Vipin T. Sreedharan, David Kuo
RiboDiff: detecting changes of mRNA translation efficiency from ribosome footprints. Bioinform. 33(1): 139-141 (2017) - [c54]Stephanie L. Hyland, Gunnar Rätsch:
Learning Unitary Operators with Help From u(n). AAAI 2017: 2050-2058 - [c53]Francesco Locatello, Michael Tschannen, Gunnar Rätsch, Martin Jaggi:
Greedy Algorithms for Cone Constrained Optimization with Convergence Guarantees. NIPS 2017: 773-784 - [i10]Francesco Locatello, Michael Tschannen, Gunnar Rätsch, Martin Jaggi:
Greedy Algorithms for Cone Constrained Optimization with Convergence Guarantees. CoRR abs/1705.11041 (2017) - [i9]Cristóbal Esteban, Stephanie L. Hyland, Gunnar Rätsch:
Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs. CoRR abs/1706.02633 (2017) - [i8]Francesco Locatello, Rajiv Khanna, Joydeep Ghosh, Gunnar Rätsch:
Boosting Variational Inference: an Optimization Perspective. CoRR abs/1708.01733 (2017) - 2016
- [j45]André Kahles, Jonas Behr, Gunnar Rätsch
MMR: a tool for read multi-mapper resolution. Bioinform. 32(5): 770-772 (2016) - [j44]Kana Shimizu
Efficient privacy-preserving string search and an application in genomics. Bioinform. 32(11): 1652-1661 (2016) - [j43]André Kahles, Cheng Soon Ong, Yi Zhong, Gunnar Rätsch
SplAdder: identification, quantification and testing of alternative splicing events from RNA-Seq data. Bioinform. 32(12): 1840-1847 (2016) - [c52]Stephanie L. Hyland, Theofanis Karaletsos, Gunnar Rätsch:
A Generative Model of Words and Relationships from Multiple Sources. AAAI 2016: 2622-2629 - [c51]Theofanis Karaletsos, Serge J. Belongie, Gunnar Rätsch:
When crowds hold privileges: Bayesian unsupervised representation learning with oracle constraints. ICLR (Poster) 2016 - [i7]Stephanie L. Hyland, Theofanis Karaletsos, Gunnar Rätsch:
Knowledge Transfer with Medical Language Embeddings. CoRR abs/1602.03551 (2016) - [i6]Stephanie L. Hyland, Gunnar Rätsch:
Learning Unitary Operators with Help From u(n). CoRR abs/1607.04903 (2016) - 2015
- [j42]Yi Zhong, Philipp Drewe, Andrew L. Wolfe, Kamini Singh, Hans-Guido Wendel, Gunnar Rätsch
Protein translational control and its contribution to oncogenesis revealed by computational methods. BMC Bioinform. 16(S-2): A6 (2015) - [j41]Julia E. Vogt
Probabilistic clustering of time-evolving distance data. Mach. Learn. 100(2-3): 635-654 (2015) - [c50]Marina M.-C. Vidovic, Nico Görnitz, Klaus-Robert Müller
Opening the Black Box: Revealing Interpretable Sequence Motifs in Kernel-Based Learning Algorithms. ECML/PKDD (2) 2015: 137-153 - [c49]Søren Brunak, Francisco M. de la Vega, Adam A. Margolin, Benjamin J. Raphael, Gunnar Rätsch, Joshua M. Stuart:
Session Introduction. Pacific Symposium on Biocomputing 2015: 8-9 - [c48]Kjong-Van Lehmann, André Kahles, Cyriac Kandoth, William Lee, Nikolaus Schultz, Oliver Stegle, Gunnar Rätsch:
Integrative Genome-wide Analysis of the Determinants of RNA Splicing in Kidney Renal Clear Cell Carcinoma. Pacific Symposium on Biocomputing 2015: 44-55 - [i5]Julia E. Vogt, Marius Kloft, Stefan Stark, Sudhir Raman, Sandhya Prabhakaran, Volker Roth, Gunnar Rätsch:
Probabilistic Clustering of Time-Evolving Distance Data. CoRR abs/1504.03701 (2015) - [i4]Christian Widmer, Marius Kloft, Vipin T. Sreedharan, Gunnar Rätsch:
Framework for Multi-task Multiple Kernel Learning and Applications in Genome Analysis. CoRR abs/1506.09153 (2015) - [i3]Stephanie L. Hyland, Theofanis Karaletsos, Gunnar Rätsch:
A Generative Model of Words and Relationships from Multiple Sources. CoRR abs/1510.00259 (2015) - [i2]Trevor Darrell, Marius Kloft, Massimiliano Pontil, Gunnar Rätsch, Erik Rodner:
Machine Learning with Interdependent and Non-identically Distributed Data (Dagstuhl Seminar 15152). Dagstuhl Reports 5(4): 18-55 (2015) - 2014
- [j40]Vipin T. Sreedharan, Sebastian J. Schultheiß
Oqtans: the RNA-seq workbench in the cloud for complete and reproducible quantitative transcriptome analysis. Bioinform. 30(9): 1300-1301 (2014) - [j39]Vipin T. Sreedharan, Sebastian J. Schultheiß
Oqtans: a multifunctional workbench for RNA-seq data analysis. BMC Bioinform. 15(S-3): A7 (2014) - [j38]Christian Widmer, Marius Kloft, Xinghua Lou, Gunnar Rätsch:
Regularization-Based Multitask Learning With Applications to Genome Biology and Biological Imaging. Künstliche Intell. 28(1): 29-33 (2014) - [c47]Søren Brunak, Francisco M. de la Vega, Gunnar Rätsch, Joshua M. Stuart:
Session Introduction. Pacific Symposium on Biocomputing 2014: 1-2 - 2013
- [j37]Jonas Behr, André Kahles, Yi Zhong, Vipin T. Sreedharan, Philipp Drewe, Gunnar Rätsch
MITIE: Simultaneous RNA-Seq-based transcript identification and quantification in multiple samples. Bioinform. 29(20): 2529-2538 (2013) - [j36]Richard R. Stein
Ecological Modeling from Time-Series Inference: Insight into Dynamics and Stability of Intestinal Microbiota. PLoS Comput. Biol. 9(12) (2013) - [c46]Christian Widmer, Marius Kloft, Gunnar Rätsch
Multi-task Learning for Computational Biology: Overview and Outlook. Empirical Inference 2013: 117-127 - [c45]Katherine Redfield Chan, Xinghua Lou, Theofanis Karaletsos, Christopher Crosbie, Stuart M. Gardos
An Empirical Analysis of Topic Modeling for Mining Cancer Clinical Notes. ICDM Workshops 2013: 56-63 - [i1]Christian Widmer, Philipp Drewe, Xinghua Lou, Shefali Umrania, Stephanie Heinrich, Gunnar Rätsch:
GRED: Graph-Regularized 3D Shape Reconstruction from Highly Anisotropic and Noisy Images. CoRR abs/1309.4426 (2013) - 2012
- [c44]Christian Widmer, Marius Kloft, Nico Görnitz, Gunnar Rätsch
Efficient Training of Graph-Regularized Multitask SVMs. ECML/PKDD (1) 2012: 633-647 - [c43]Christian Widmer, Gunnar Rätsch:
Multitask Learning in Computational Biology. ICML Unsupervised and Transfer Learning 2012: 207-216 - 2011
- [j35]Johannes Eichner, Georg Zeller
Support vector machines-based identification of alternative splicing in Arabidopsis thaliana from whole-genome tiling arrays. BMC Bioinform. 12: 55 (2011) - [j34]Sebastian J. Schultheiß
Oqtans: a Galaxy-integrated workflow for quantitative transcriptome analysis from NGS Data. BMC Bioinform. 12(S-11): A7 (2011) - [c42]Nico Görnitz, Christian Widmer, Georg Zeller, André Kahles, Sören Sonnenburg, Gunnar Rätsch:
Hierarchical Multitask Structured Output Learning for Large-scale Sequence Segmentation. NIPS 2011: 2690-2698 - 2010
- [j33]Jonas Behr, Regina Bohnert, Georg Zeller, Gabriele Beate Schweikert, Lisa Hartmann, Gunnar Rätsch
Next generation genome annotation with mGene.ngs. BMC Bioinform. 11(S-10): O8 (2010) - [j32]Christian Widmer, Nora C. Toussaint
Inferring latent task structure for Multitask Learning by Multiple Kernel Learning. BMC Bioinform. 11(S-8): S5 (2010) - [j31]Nora C. Toussaint
Exploiting physico-chemical properties in string kernels. BMC Bioinform. 11(S-8): S7 (2010) - [j30]Sören Sonnenburg, Gunnar Rätsch, Sebastian Henschel, Christian Widmer, Jonas Behr, Alexander Zien, Fabio De Bona, Alexander Binder, Christian Gehl, Vojtech Franc:
The SHOGUN Machine Learning Toolbox. J. Mach. Learn. Res. 11: 1799-1802 (2010) - [j29]Regina Bohnert, Gunnar Rätsch
rQuant.web: a tool for RNA-Seq-based transcript quantitation. Nucleic Acids Res. 38(Web-Server-Issue): 348-351 (2010) - [c41]Christian Widmer, Nora C. Toussaint
Novel Machine Learning Methods for MHC Class I Binding Prediction. PRIB 2010: 98-109 - [c40]Christian Widmer, Jose Leiva, Yasemin Altun, Gunnar Rätsch
Leveraging Sequence Classification by Taxonomy-Based Multitask Learning. RECOMB 2010: 522-534
2000 – 2009
- 2009
- [j28]Sebastian J. Schultheiß
KIRMES: kernel-based identification of regulatory modules in euchromatic sequences. Bioinform. 25(16): 2126-2133 (2009) - [j27]Regina Bohnert, Jonas Behr, Gunnar Rätsch:
Transcript quantification with RNA-Seq data. BMC Bioinform. 10(S-13): 0 (2009) - [j26]Sebastian J. Schultheiß, Wolfgang Busch, Jan U. Lohmann, Oliver Kohlbacher, Gunnar Rätsch:
KIRMES: kernel-based identification of regulatory modules in euchromatic sequences. BMC Bioinform. 10(S-13): 0 (2009) - [j25]Gabriele Beate Schweikert, Jonas Behr, Alexander Zien, Georg Zeller
mGene.web: a web service for accurate computational gene finding. Nucleic Acids Res. 37(Web-Server-Issue): 312-316 (2009) - [j24]Arnulf B. A. Graf, Olivier Bousquet, Gunnar Rätsch
Prototype Classification: Insights from Machine Learning. Neural Comput. 21(1): 272-300 (2009) - [c39]Alexander Zien, Nicole Krämer
The Feature Importance Ranking Measure. ECML/PKDD (2) 2009: 694-709 - 2008
- [j23]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:
Revealing sequence variation patterns in rice with machine learning methods. BMC Bioinform. 9(S-10) (2008) - [j22]Fabio De Bona, Stephan Ossowski, Korbinian Schneeberger, Gunnar Rätsch:
Optimal spliced alignments of short sequence reads. BMC Bioinform. 9(S-10) (2008) - [j21]Asa Ben-Hur
Support Vector Machines and Kernels for Computational Biology. PLoS Comput. Biol. 4(10) (2008) - [c38]Fabio De Bona, Stephan Ossowski
Optimal spliced alignments of short sequence reads. ECCB 2008: 174-180 - [c37]Sebastian J. Schultheiß, Wolfgang Busch, Jan U. Lohmann, Oliver Kohlbacher, Gunnar Rätsch:
KIRMES: Kernel-based Identification of Regulatory Modules in Euchromatic Sequences. German Conference on Bioinformatics 2008: 158-167 - [c36]Sören Sonnenburg, Alexander Zien, Petra Philips, Gunnar Rätsch
POIMs: positional oligomer importance matrices - understanding support vector machine-based signal detectors. ISMB 2008: 6-14 - [c35]Gabriele Beate Schweikert, Christian Widmer, Bernhard Schölkopf, Gunnar Rätsch:
An Empirical Analysis of Domain Adaptation Algorithms for Genomic Sequence Analysis. NIPS 2008: 1433-1440 - [c34]Georg Zeller, Stefan R. Henz, Sascha Laubinger, Detlef Weigel, Gunnar Rätsch:
Transcript Normalization and Segmentation of Tiling Array Data. Pacific Symposium on Biocomputing 2008: 527-538 - 2007
- [j20]Uta Schulze, Bettina Hepp, Cheng Soon Ong, Gunnar Rätsch
PALMA: mRNA to genome alignments using large margin algorithms. Bioinform. 23(15): 1892-1900 (2007) - [j19]Gal Chechik, Christina S. Leslie, William Stafford Noble, Gunnar Rätsch
NIPS workshop on New Problems and Methods in Computational Biology. BMC Bioinform. 8(S-10) (2007) - [j18]Sören Sonnenburg, Gabriele Beate Schweikert, Petra Philips, Jonas Behr, Gunnar Rätsch
Accurate splice site prediction using support vector machines. BMC Bioinform. 8(S-10) (2007) - [j17]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:
The Need for Open Source Software in Machine Learning. J. Mach. Learn. Res. 8: 2443-2466 (2007) - [j16]Gunnar Rätsch
Improving the Caenorhabditis elegans Genome Annotation Using Machine Learning. PLoS Comput. Biol. 3(2) (2007) - [c33]Manfred K. Warmuth, Karen A. Glocer, Gunnar Rätsch:
Boosting Algorithms for Maximizing the Soft Margin. NIPS 2007: 1585-1592 - 2006
- [j15]Gunnar Rätsch
Learning Interpretable SVMs for Biological Sequence Classification. BMC Bioinform. 7(S-1) (2006) - [j14]Sören Sonnenburg, Gunnar Rätsch, Christin Schäfer, Bernhard Schölkopf:
Large Scale Multiple Kernel Learning. J. Mach. Learn. Res. 7: 1531-1565 (2006) - [c32]Gunnar Rätsch
Solving Semi-infinite Linear Programs Using Boosting-Like Methods. ALT 2006: 10-11 - [c31]Gunnar Rätsch
The Solution of Semi-Infinite Linear Programs Using Boosting-Like Methods. Discovery Science 2006: 15 - [c30]Hyunjung Shin, N. Jeremy Hill, Gunnar Rätsch
Graph Based Semi-supervised Learning with Sharper Edges. ECML 2006: 401-412 - [c29]Gunnar Rätsch, Bettina Hepp, Uta Schulze, Cheng Soon Ong:
PALMA: Perfect Alignments using Large Margin Algorithms. German Conference on Bioinformatics 2006: 104-113 - [c28]Manfred K. Warmuth, Jun Liao, Gunnar Rätsch
Totally corrective boosting algorithms that maximize the margin. ICML 2006: 1001-1008 - [c27]Sören Sonnenburg, Alexander Zien, Gunnar Rätsch
ARTS: accurate recognition of transcription starts in human. ISMB (Supplement of Bioinformatics) 2006: 472-480 - [c26]Gunnar Rätsch, Sören Sonnenburg:
Large Scale Hidden Semi-Markov SVMs. NIPS 2006: 1161-1168 - 2005
- [b1]Gunnar Rätsch:
Robust boosting via convex optimization. University of Potsdam, Germany, 2005 - [j13]Klaus-Robert Müller
Classifying 'Drug-likeness' with Kernel-Based Learning Methods. J. Chem. Inf. Model. 45(2): 249-253 (2005) - [j12]Koji Tsuda, Gunnar Rätsch, Manfred K. Warmuth:
Matrix Exponentiated Gradient Updates for On-line Learning and Bregman Projection. J. Mach. Learn. Res. 6: 995-1018 (2005) - [j11]Gunnar Rätsch, Manfred K. Warmuth:
Efficient Margin Maximizing with Boosting. J. Mach. Learn. Res. 6: 2131-2152 (2005) - [j10]Koji Tsuda, Gunnar Rätsch
Image reconstruction by linear programming. IEEE Trans. Image Process. 14(6): 737-744 (2005) - [c25]Sören Sonnenburg, Gunnar Rätsch
Large scale genomic sequence SVM classifiers. ICML 2005: 848-855 - [c24]Gunnar Rätsch
RASE: recognition of alternatively spliced exons in C.elegans. ISMB (Supplement of Bioinformatics) 2005: 369-377 - [c23]Sören Sonnenburg, Gunnar Rätsch, Christin Schäfer:
A General and Efficient Multiple Kernel Learning Algorithm. NIPS 2005: 1273-1280 - [c22]Sören Sonnenburg, Gunnar Rätsch, Christin Schäfer:
Learning Interpretable SVMs for Biological Sequence Classification. RECOMB 2005: 389-407 - 2004
- [c21]Koji Tsuda, Gunnar Rätsch, Manfred K. Warmuth:
Matrix Exponential Gradient Updates for On-line Learning and Bregman Projection. NIPS 2004: 1425-1432 - [e1]Olivier Bousquet, Ulrike von Luxburg, Gunnar Rätsch
Advanced Lectures on Machine Learning, ML Summer Schools 2003, Canberra, Australia, February 2-14, 2003, Tübingen, Germany, August 4-16, 2003, Revised Lectures. Lecture Notes in Computer Science 3176, Springer 2004, ISBN 3-540-23122-6 [contents] - 2003
- [j9]Manfred K. Warmuth, Jun Liao, Gunnar Rätsch
Active Learning with Support Vector Machines in the Drug Discovery Process. J. Chem. Inf. Comput. Sci. 43(2): 667-673 (2003) - [j8]Sebastian Mika, Gunnar Rätsch
Constructing Descriptive and Discriminative Nonlinear Features: Rayleigh Coefficients in Kernel Feature Spaces. IEEE Trans. Pattern Anal. Mach. Intell. 25(5): 623-633 (2003) - [c20]Gunnar Rätsch:
Robust multi-class boosting. INTERSPEECH 2003: 997-1000 - [c19]Koji Tsuda, Gunnar Rätsch:
Image Reconstruction by Linear Programming. NIPS 2003: 57-64 - 2002
- [j7]Gunnar Rätsch
Sparse Regression Ensembles in Infinite and Finite Hypothesis Spaces. Mach. Learn. 48(1-3): 189-218 (2002) - [j6]Koji Tsuda, Motoaki Kawanabe, Gunnar Rätsch
A New Discriminative Kernel from Probabilistic Models. Neural Comput. 14(10): 2397-2414 (2002) - [j5]Gunnar Rätsch
Constructing Boosting Algorithms from SVMs: An Application to One-Class Classification. IEEE Trans. Pattern Anal. Mach. Intell. 24(9): 1184-1199 (2002) - [c18]Gunnar Rätsch
Maximizing the Margin with Boosting. COLT 2002: 334-350 - [c17]Sören Sonnenburg, Gunnar Rätsch
New Methods for Splice Site Recognition. ICANN 2002: 329-336 - [c16]Ron Meir, Gunnar Rätsch
An Introduction to Boosting and Leveraging. Machine Learning Summer School 2002: 118-183 - [c15]Gunnar Rätsch, Alexander J. Smola, Sebastian Mika:
Adapting Codes and Embeddings for Polychotomies. NIPS 2002: 513-520 - 2001
- [j4]Gunnar Rätsch
Soft Margins for AdaBoost. Mach. Learn. 42(3): 287-320 (2001) - [j3]Klaus-Robert Müller
An introduction to kernel-based learning algorithms. IEEE Trans. Neural Networks 12(2): 181-201 (2001) - [c14]Koji Tsuda, Gunnar Rätsch
Learning to Predict the Leave-One-Out Error of Kernel Based Classifiers. ICANN 2001: 331-338 - [c13]Gunnar Rätsch, Sebastian Mika, Manfred K. Warmuth:
On the Convergence of Leveraging. NIPS 2001: 487-494 - [c12]Koji Tsuda, Motoaki Kawanabe, Gunnar Rätsch, Sören Sonnenburg, Klaus-Robert Müller:
A New Discriminative Kernel From Probabilistic Models. NIPS 2001: 977-984 - [c11]Manfred K. Warmuth, Gunnar Rätsch, Michael Mathieson, Jun Liao, Christian Lemmen:
Active Learning in the Drug Discovery Process. NIPS 2001: 1449-1456 - [p1]Gunnar Rätsch:
Robustes Boosting durch konvexe Optimierung. Ausgezeichnete Informatikdissertationen 2001: 135-146 - 2000
- [j2]Alexander Zien, Gunnar Rätsch
Engineering support vector machine kernels that recognize translation initiation sites. Bioinform. 16(9): 799-807 (2000) - [c10]Gunnar Rätsch, Manfred K. Warmuth, Sebastian Mika, Takashi Onoda, Steven Lemm, Klaus-Robert Müller:
Barrier Boosting. COLT 2000: 170-179 - [c9]Sebastian Mika, Gunnar Rätsch, Klaus-Robert Müller:
A Mathematical Programming Approach to the Kernel Fisher Algorithm. NIPS 2000: 591-597 - [c8]Gunnar Rätsch
Robust Ensemble Learning for Data Mining. PAKDD 2000: 341-344
1990 – 1999
- 1999
- [j1]Bernhard Schölkopf, Sebastian Mika, Christopher J. C. Burges, Phil Knirsch, Klaus-Robert Müller
Input space versus feature space in kernel-based methods. IEEE Trans. Neural Networks 10(5): 1000-1017 (1999) - [c7]Alexander Zien, Gunnar Rätsch, Sebastian Mika, Bernhard Schölkopf, Christian Lemmen, Alexander J. Smola, Thomas Lengauer, Klaus-Robert Müller:
Engineering Support Vector Machine Kerneis That Recognize Translation Initialion Sites. German Conference on Bioinformatics 1999: 37-43 - [c6]Sebastian Mika, Gunnar Rätsch, Jason Weston, Bernhard Schölkopf, Alexander J. Smola, Klaus-Robert Müller:
Invariant Feature Extraction and Classification in Kernel Spaces. NIPS 1999: 526-532 - [c5]Gunnar Rätsch, Bernhard Schölkopf, Alexander J. Smola, Klaus-Robert Müller, Takashi Onoda, Sebastian Mika:
v-Arc: Ensemble Learning in the Presence of Outliers. NIPS 1999: 561-567 - 1998
- [c4]Gunnar Rätsch, Takashi Onoda, Klaus-Robert Müller:
An Improvement of AdaBoost to Avoid Overfitting. ICONIP 1998: 506-509 - [c3]Sebastian Mika, Bernhard Schölkopf, Alexander J. Smola, Klaus-Robert Müller, Matthias Scholz, Gunnar Rätsch:
Kernel PCA and De-Noising in Feature Spaces. NIPS 1998: 536-542 - [c2]Gunnar Rätsch, Takashi Onoda, Klaus-Robert Müller:
Regularizing AdaBoost. NIPS 1998: 564-570 - 1997
- [c1]Klaus-Robert Müller
Predicting Time Series with Support Vector Machines. ICANN 1997: 999-1004
Coauthor Index
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from ,
, and
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and
to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from .
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-11-30 01:09 CET by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by: