Rakitsch, Barbara

11 publications

TMLR 2025 Global Safe Sequential Learning via Efficient Knowledge Transfer Cen-You Li, Olaf Dünnbier, Marc Toussaint, Barbara Rakitsch, Christoph Zimmer

TMLR 2023 Cheap and Deterministic Inference for Deep State-Space Models of Interacting Dynamical Systems Andreas Look, Barbara Rakitsch, Melih Kandemir, Jan Peters

AAAI 2023 Combining Slow and Fast: Complementary Filtering for Dynamics Learning Katharina Ensinger, Sebastian Ziesche, Barbara Rakitsch, Michael Tiemann, Sebastian Trimpe

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

UAI 2022 Laplace Approximated Gaussian Process State-Space Models Jakob Lindinger, Barbara Rakitsch, Christoph Lippert

NeurIPS 2022 Learning Interacting Dynamical Systems with Latent Gaussian Process ODEs Çağatay Yıldız, Melih Kandemir, Barbara Rakitsch

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

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

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

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

NeurIPS 2013 It Is All in the Noise: Efficient Multi-Task Gaussian Process Inference with Structured Residuals Barbara Rakitsch, Christoph Lippert, Karsten Borgwardt, Oliver Stegle