Barbara Rakitsch

31 papers A* 4A 3B 1Journal 19Unranked 3

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Amortized Safe Active Learning for Real-Time Decision-Making: Pretrained Neural Policies from Simulated Nonparametric Functions. CoRR Cen-You Li, Marc Toussaint, Barbara Rakitsch, Christoph Zimmer
2025	J	`jnl`	Global Safe Sequential Learning via Efficient Knowledge Transfer. Trans. Mach. Learn. Res. Cen-You Li, Olaf Dünnbier, Marc Toussaint, Barbara Rakitsch, Christoph Zimmer
2024	—	`conf`	Amortized Active Learning for Nonparametric Functions. IAL@PKDD/ECML Cen-You Li, Marc Toussaint, Barbara Rakitsch, Christoph Zimmer
2024	J	`jnl`	Amortized Active Learning for Nonparametric Functions. CoRR Cen-You Li, Marc Toussaint, Barbara Rakitsch, Christoph Zimmer
2024	J	`jnl`	Global Safe Sequential Learning via Efficient Knowledge Transfer. CoRR Cen-You Li, Olaf Dünnbier, Marc Toussaint, Barbara Rakitsch, Christoph Zimmer
2024	J	`jnl`	Hybrid Modeling Design Patterns. CoRR Maja Rudolph, Stefan Kurz, Barbara Rakitsch
2023	J	`jnl`	A Deterministic Approximation to Neural SDEs. IEEE Trans. Pattern Anal. Mach. Intell. Andreas Look, Melih Kandemir, Barbara Rakitsch, Jan Peters
2023	J	`jnl`	Can you text what is happening? Integrating pre-trained language encoders into trajectory prediction models for autonomous driving. CoRR Ali Keysan, Andreas Look, Eitan Kosman, Gonca Gürsun, Jörg Wagner, Yu Yao, Barbara Rakitsch
2023	J	`jnl`	Cheap and Deterministic Inference for Deep State-Space Models of Interacting Dynamical Systems. CoRR Andreas Look, Melih Kandemir, Barbara Rakitsch, Jan Peters
2023	J	`jnl`	Cheap and Deterministic Inference for Deep State-Space Models of Interacting Dynamical Systems. Trans. Mach. Learn. Res. Andreas Look, Barbara Rakitsch, Melih Kandemir, Jan Peters
2023	A*	`conf`	Combining Slow and Fast: Complementary Filtering for Dynamics Learning. AAAI Katharina Ensinger, Sebastian Ziesche, Barbara Rakitsch, Michael Tiemann, Sebastian Trimpe
2023	J	`jnl`	Combining Slow and Fast: Complementary Filtering for Dynamics Learning. CoRR Katharina Ensinger, Sebastian Ziesche, Barbara Rakitsch, Michael Tiemann, Sebastian Trimpe
2023	J	`jnl`	Sampling-Free Probabilistic Deep State-Space Models. CoRR Andreas Look, Melih Kandemir, Barbara Rakitsch, Jan Peters
2022	A	`conf`	Laplace approximated Gaussian process state-space models. UAI Jakob Lindinger, Barbara Rakitsch, Christoph Lippert
2022	J	`jnl`	Learning Interacting Dynamical Systems with Latent Gaussian Process ODEs. CoRR Çagatay Yildiz, Melih Kandemir, Barbara Rakitsch
2022	A*	`conf`	Learning interacting dynamical systems with latent Gaussian process ODEs. NeurIPS Çagatay Yildiz, Melih Kandemir, Barbara Rakitsch
2022	A	`conf`	Safe Active Learning for Multi-Output Gaussian Processes. AISTATS Cen-You Li, Barbara Rakitsch, Christoph Zimmer
2022	J	`jnl`	Safe Active Learning for Multi-Output Gaussian Processes. CoRR Cen-You Li, Barbara Rakitsch, Christoph Zimmer
2022	—	`conf`	Traversing Time with Multi-Resolution Gaussian Process State-Space Models. L4DC Krista Longi, Jakob Lindinger, Olaf Dünnbier, Melih Kandemir, Arto Klami, Barbara Rakitsch
2021	A	`conf`	Learning Partially Known Stochastic Dynamics with Empirical PAC Bayes. AISTATS Manuel Haußmann, Sebastian Gerwinn, Andreas Look, Barbara Rakitsch, Melih Kandemir
2021	J	`jnl`	Traversing Time with Multi-Resolution Gaussian Process State-Space Models. CoRR Krista Longi, Jakob Lindinger, Olaf Dünnbier, Melih Kandemir, Arto Klami, Barbara Rakitsch
2020	A*	`conf`	Beyond the Mean-Field: Structured Deep Gaussian Processes Improve the Predictive Uncertainties. NeurIPS Jakob Lindinger, David Reeb, Christoph Lippert, Barbara Rakitsch
2020	J	`jnl`	Beyond the Mean-Field: Structured Deep Gaussian Processes Improve the Predictive Uncertainties. CoRR Jakob Lindinger, David Reeb, Christoph Lippert, Barbara Rakitsch
2020	J	`jnl`	Learning Partially Known Stochastic Dynamics with Empirical PAC Bayes. CoRR Manuel Haußmann, Sebastian Gerwinn, Andreas Look, Barbara Rakitsch, Melih Kandemir
2018	A*	`conf`	Learning Gaussian Processes by Minimizing PAC-Bayesian Generalization Bounds. NeurIPS David Reeb, Andreas Doerr, Sebastian Gerwinn, Barbara Rakitsch
2018	J	`jnl`	Learning Gaussian Processes by Minimizing PAC-Bayesian Generalization Bounds. CoRR David Reeb, Andreas Doerr, Sebastian Gerwinn, Barbara Rakitsch
2015	—	`—`	Modeling the polygenic architecture of complex traits Barbara Rakitsch
2013	J	`jnl`	A Lasso multi-marker mixed model for association mapping with population structure correction. Bioinform. Barbara Rakitsch, Christoph Lippert, Oliver Stegle, Karsten M. Borgwardt
2013	—	`conf`	It is all in the noise: Efficient multi-task Gaussian process inference with structured residuals. NIPS Barbara Rakitsch, Christoph Lippert, Karsten M. Borgwardt, Oliver Stegle
2011	J	`jnl`	ccSVM: correcting Support Vector Machines for confounding factors in biological data classification. Bioinform. Limin Li, Barbara Rakitsch, Karsten M. Borgwardt
2010	B	`conf`	Pruning population size in XCS for complex problems. IJCNN Barbara Rakitsch, Andreas Bernauer, Oliver Bringmann, Wolfgang Rosenstiel