Barbara Rakitsch
Barbara Rakitsch
Bosch Center for AI
Verified email at - Homepage
Cited by
Cited by
A Lasso multi-marker mixed model for association mapping with population structure correction
B Rakitsch, C Lippert, O Stegle, K Borgwardt
Bioinformatics 29 (2), 206-214, 2013
LIMIX: genetic analysis of multiple traits
C Lippert, FP Casale, B Rakitsch, O Stegle
BioRxiv, 003905, 2014
Efficient set tests for the genetic analysis of correlated traits
FP Casale, B Rakitsch, C Lippert, O Stegle
Nature methods 12 (8), 755-758, 2015
It is all in the noise: Efficient multi-task Gaussian process inference with structured residuals
B Rakitsch, C Lippert, K Borgwardt, O Stegle
Advances in neural information processing systems 26, 2013
Genetic architecture of nonadditive inheritance in Arabidopsis thaliana hybrids
DK Seymour, E Chae, DG Grimm, C Martín Pizarro, A Habring-Müller, ...
Proceedings of the National Academy of Sciences 113 (46), E7317-E7326, 2016
ccSVM: correcting Support Vector Machines for confounding factors in biological data classification
L Li, B Rakitsch, K Borgwardt
Bioinformatics 27 (13), i342-i348, 2011
Translating immunopeptidomics to immunotherapy‐decision‐making for patient and personalized target selection
J Fritsche, B Rakitsch, F Hoffgaard, M Römer, H Schuster, DJ Kowalewski, ...
Proteomics 18 (12), 1700284, 2018
Learning gaussian processes by minimizing pac-bayesian generalization bounds
D Reeb, A Doerr, S Gerwinn, B Rakitsch
Advances in Neural Information Processing Systems 31, 2018
Genomic profiles of diversification and genotype–phenotype association in island nematode lineages
A McGaughran, C Rödelsperger, DG Grimm, JM Meyer, E Moreno, ...
Molecular biology and evolution 33 (9), 2257-2272, 2016
Modelling local gene networks increases power to detect trans-acting genetic effects on gene expression
B Rakitsch, O Stegle
Genome biology 17, 1-13, 2016
Joint genetic analysis using variant sets reveals polygenic gene-context interactions
FP Casale, D Horta, B Rakitsch, O Stegle
PLoS genetics 13 (4), e1006693, 2017
Learning partially known stochastic dynamics with empirical PAC Bayes
M Haußmann, S Gerwinn, A Look, B Rakitsch, M Kandemir
International Conference on Artificial Intelligence and Statistics, 478-486, 2021
Learning interacting dynamical systems with latent gaussian process odes
Ç Yıldız, M Kandemir, B Rakitsch
Advances in Neural Information Processing Systems 35, 9188-9200, 2022
Can you text what is happening? integrating pre-trained language encoders into trajectory prediction models for autonomous driving
A Keysan, A Look, E Kosman, G Gürsun, J Wagner, Y Yu, B Rakitsch
arXiv preprint arXiv:2309.05282, 2023
Safe active learning for multi-output gaussian processes
CY Li, B Rakitsch, C Zimmer
International Conference on Artificial Intelligence and Statistics, 4512-4551, 2022
Beyond the mean-field: Structured deep Gaussian processes improve the predictive uncertainties
J Lindinger, D Reeb, C Lippert, B Rakitsch
Advances in Neural Information Processing Systems 33, 8498-8509, 2020
Laplace approximated Gaussian process state-space models
J Lindinger, B Rakitsch, C Lippert
Uncertainty in Artificial Intelligence, 1199-1209, 2022
LIMIX: genetic analysis of multiple traits. bioRxiv. 2014
C Lippert, FP Casale, B Rakitsch, O Stegle
doi 10 (003905), 003905, 0
Cheap and deterministic inference for deep state-space models of interacting dynamical systems
A Look, M Kandemir, B Rakitsch, J Peters
arXiv preprint arXiv:2305.01773, 2023
Bootstrat: population informed bootstrapping for rare variant tests
H Huang, GM Peloso, D Howrigan, B Rakitsch, CJ Simon-Gabriel, ...
bioRxiv, 068999, 2016
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