Barbara Rakitsch

According to our database, Barbara Rakitsch authored at least 20 papers between 2010 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Global Safe Sequential Learning via Efficient Knowledge Transfer.

Cen-You Li, Olaf Duennbier, Marc Toussaint, Barbara Rakitsch, Christoph Zimmer

CoRR, 2024

Hybrid Modeling Design Patterns.

Maja Rudolph, Stefan Kurz, Barbara Rakitsch

CoRR, 2024

Amortized Active Learning for Nonparametric Functions.

Cen-You Li, Marc Toussaint, Barbara Rakitsch, Christoph Zimmer

Proceedings of the Workshop on Interactive Adaptive Learning co-located with European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2024), 2024

2023

A Deterministic Approximation to Neural SDEs.

Andreas Look, Melih Kandemir, Barbara Rakitsch, Jan Peters

IEEE Trans. Pattern Anal. Mach. Intell., April, 2023

Cheap and Deterministic Inference for Deep State-Space Models of Interacting Dynamical Systems.

Andreas Look, Barbara Rakitsch, Melih Kandemir, Jan Peters

Trans. Mach. Learn. Res., 2023

Sampling-Free Probabilistic Deep State-Space Models.

Andreas Look, Melih Kandemir, Barbara Rakitsch, Jan Peters

CoRR, 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

Combining Slow and Fast: Complementary Filtering for Dynamics Learning.

Katharina Ensinger, Sebastian Ziesche, Barbara Rakitsch, Michael Tiemann, Sebastian Trimpe

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022

Laplace approximated Gaussian process state-space models.

Jakob Lindinger, Barbara Rakitsch, Christoph Lippert

Proceedings of the Uncertainty in Artificial Intelligence, 2022

Learning interacting dynamical systems with latent Gaussian process ODEs.

Çagatay Yildiz, Melih Kandemir, Barbara Rakitsch

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Traversing Time with Multi-Resolution Gaussian Process State-Space Models.

Krista Longi, Jakob Lindinger, Olaf Duennbier, Melih Kandemir, Arto Klami, Barbara Rakitsch

Proceedings of the Learning for Dynamics and Control Conference, 2022

Safe Active Learning for Multi-Output Gaussian Processes.

Cen-You Li, Barbara Rakitsch, Christoph Zimmer

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Learning Partially Known Stochastic Dynamics with Empirical PAC Bayes.

Manuel Haußmann, Sebastian Gerwinn, Andreas Look, Barbara Rakitsch, Melih Kandemir

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Beyond the Mean-Field: Structured Deep Gaussian Processes Improve the Predictive Uncertainties.

Jakob Lindinger, David Reeb, Christoph Lippert, Barbara Rakitsch

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2018

Learning Gaussian Processes by Minimizing PAC-Bayesian Generalization Bounds.

David Reeb, Andreas Doerr, Sebastian Gerwinn, Barbara Rakitsch

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

2015

Modeling the polygenic architecture of complex traits

Barbara Rakitsch

PhD thesis, 2015

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

It is all in the noise: Efficient multi-task Gaussian process inference with structured residuals.

Barbara Rakitsch, Christoph Lippert, Karsten M. Borgwardt, Oliver Stegle

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

2011

ccSVM: correcting Support Vector Machines for confounding factors in biological data classification.

Limin Li, Barbara Rakitsch, Karsten M. Borgwardt

Bioinform., 2011

2010

Pruning population size in XCS for complex problems.

Barbara Rakitsch, Andreas Bernauer, Oliver Bringmann, Wolfgang Rosenstiel

Proceedings of the International Joint Conference on Neural Networks, 2010