Pruning population size in XCS for complex problems. - Barbara Rakitsch; Andreas Bernauer; Oliver Bringmann 001; Wolfgang Rosenstiel (IJCNN, 2010)

ccSVM: correcting Support Vector Machines for confounding factors in biological data classification. - Limin Li; Barbara Rakitsch; Karsten M. Borgwardt (Bioinform., 2011)

It is all in the noise: Efficient multi-task Gaussian process inference with structured residuals. - Barbara Rakitsch; Christoph Lippert; Karsten M. Borgwardt; Oliver Stegle (NIPS, 2013)

A Lasso multi-marker mixed model for association mapping with population structure correction. - Barbara Rakitsch; Christoph Lippert; Oliver Stegle; Karsten M. Borgwardt (Bioinform., 2013)

Modeling the polygenic architecture of complex traits - Barbara Rakitsch (2015)

Learning Gaussian Processes by Minimizing PAC-Bayesian Generalization Bounds. - David Reeb; Andreas Doerr; Sebastian Gerwinn; Barbara Rakitsch (NeurIPS, 2018)

Beyond the Mean-Field: Structured Deep Gaussian Processes Improve the Predictive Uncertainties. - Jakob Lindinger; David Reeb; Christoph Lippert; Barbara Rakitsch (NeurIPS, 2020)

Learning Partially Known Stochastic Dynamics with Empirical PAC Bayes. - Manuel Haußmann; Sebastian Gerwinn; Andreas Look; Barbara Rakitsch; Melih Kandemir (AISTATS, 2021)

Safe Active Learning for Multi-Output Gaussian Processes. - Cen-You Li; Barbara Rakitsch; Christoph Zimmer (AISTATS, 2022)

Traversing Time with Multi-Resolution Gaussian Process State-Space Models. - Krista Longi; Jakob Lindinger; Olaf Dünnbier; Melih Kandemir; Arto Klami; Barbara Rakitsch (L4DC, 2022)

Learning interacting dynamical systems with latent Gaussian process ODEs. - Çagatay Yildiz; Melih Kandemir; Barbara Rakitsch (NeurIPS, 2022)

Laplace approximated Gaussian process state-space models. - Jakob Lindinger; Barbara Rakitsch; Christoph Lippert (UAI, 2022)

Combining Slow and Fast: Complementary Filtering for Dynamics Learning. - Katharina Ensinger; Sebastian Ziesche; Barbara Rakitsch; Michael Tiemann 001; Sebastian Trimpe (AAAI, 2023)

Can you text what is happening? Integrating pre-trained language encoders into trajectory prediction models for autonomous driving. - Ali Keysan; Andreas Look; Eitan Kosman; Gonca Gürsun; Jörg Wagner; Yu Yao; Barbara Rakitsch (CoRR, 2023)

Sampling-Free Probabilistic Deep State-Space Models. - Andreas Look; Melih Kandemir; Barbara Rakitsch; Jan Peters 001 (CoRR, 2023)

Cheap and Deterministic Inference for Deep State-Space Models of Interacting Dynamical Systems. - Andreas Look; Barbara Rakitsch; Melih Kandemir; Jan Peters 001 (Trans. Mach. Learn. Res., 2023)

A Deterministic Approximation to Neural SDEs. - Andreas Look; Melih Kandemir; Barbara Rakitsch; Jan Peters 001 (IEEE Trans. Pattern Anal. Mach. Intell., 2023)

Amortized Active Learning for Nonparametric Functions. - Cen-You Li; Marc Toussaint; Barbara Rakitsch; Christoph Zimmer (IAL@PKDD/ECML, 2024)

Hybrid Modeling Design Patterns. - Maja Rudolph; Stefan Kurz; Barbara Rakitsch (CoRR, 2024)

Global Safe Sequential Learning via Efficient Knowledge Transfer. - Cen-You Li; Olaf Dünnbier; Marc Toussaint; Barbara Rakitsch; Christoph Zimmer (Trans. Mach. Learn. Res., 2025)

Amortized Safe Active Learning for Real-Time Decision-Making: Pretrained Neural Policies from Simulated Nonparametric Functions. - Cen-You Li; Marc Toussaint; Barbara Rakitsch; Christoph Zimmer (CoRR, 2025)