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Barbara Rakitsch
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2020 – today
- 2024
- [c11]Cen-You Li, Marc Toussaint, Barbara Rakitsch, Christoph Zimmer:
Amortized Active Learning for Nonparametric Functions. IAL@PKDD/ECML 2024: 18-32 - [i13]Maja Rudolph, Stefan Kurz, Barbara Rakitsch:
Hybrid Modeling Design Patterns. CoRR abs/2401.00033 (2024) - [i12]Cen-You Li, Olaf Duennbier, Marc Toussaint, Barbara Rakitsch, Christoph Zimmer:
Global Safe Sequential Learning via Efficient Knowledge Transfer. CoRR abs/2402.14402 (2024) - [i11]Cen-You Li, Marc Toussaint, Barbara Rakitsch, Christoph Zimmer:
Amortized Active Learning for Nonparametric Functions. CoRR abs/2407.17992 (2024) - 2023
- [j4]Andreas Look, Melih Kandemir, Barbara Rakitsch, Jan Peters:
A Deterministic Approximation to Neural SDEs. IEEE Trans. Pattern Anal. Mach. Intell. 45(4): 4023-4037 (2023) - [j3]Andreas Look, Barbara Rakitsch, Melih Kandemir, Jan Peters:
Cheap and Deterministic Inference for Deep State-Space Models of Interacting Dynamical Systems. Trans. Mach. Learn. Res. 2023 (2023) - [c10]Katharina Ensinger, Sebastian Ziesche, Barbara Rakitsch, Michael Tiemann, Sebastian Trimpe:
Combining Slow and Fast: Complementary Filtering for Dynamics Learning. AAAI 2023: 7476-7484 - [i10]Katharina Ensinger, Sebastian Ziesche, Barbara Rakitsch, Michael Tiemann, Sebastian Trimpe:
Combining Slow and Fast: Complementary Filtering for Dynamics Learning. CoRR abs/2302.13754 (2023) - [i9]Andreas Look, Melih Kandemir, Barbara Rakitsch, Jan Peters:
Cheap and Deterministic Inference for Deep State-Space Models of Interacting Dynamical Systems. CoRR abs/2305.01773 (2023) - [i8]Ali Keysan, Andreas Look, Eitan Kosman, Gonca Gürsun, Jörg Wagner, Yu Yao, Barbara Rakitsch:
Can you text what is happening? Integrating pre-trained language encoders into trajectory prediction models for autonomous driving. CoRR abs/2309.05282 (2023) - [i7]Andreas Look, Melih Kandemir, Barbara Rakitsch, Jan Peters:
Sampling-Free Probabilistic Deep State-Space Models. CoRR abs/2309.08256 (2023) - 2022
- [c9]Cen-You Li, Barbara Rakitsch, Christoph Zimmer:
Safe Active Learning for Multi-Output Gaussian Processes. AISTATS 2022: 4512-4551 - [c8]Krista Longi, Jakob Lindinger, Olaf Duennbier, Melih Kandemir, Arto Klami, Barbara Rakitsch:
Traversing Time with Multi-Resolution Gaussian Process State-Space Models. L4DC 2022: 366-377 - [c7]Çagatay Yildiz, Melih Kandemir, Barbara Rakitsch:
Learning interacting dynamical systems with latent Gaussian process ODEs. NeurIPS 2022 - [c6]Jakob Lindinger, Barbara Rakitsch, Christoph Lippert:
Laplace approximated Gaussian process state-space models. UAI 2022: 1199-1209 - [i6]Cen-You Li, Barbara Rakitsch, Christoph Zimmer:
Safe Active Learning for Multi-Output Gaussian Processes. CoRR abs/2203.14849 (2022) - [i5]Çagatay Yildiz, Melih Kandemir, Barbara Rakitsch:
Learning Interacting Dynamical Systems with Latent Gaussian Process ODEs. CoRR abs/2205.11894 (2022) - 2021
- [c5]Manuel Haußmann, Sebastian Gerwinn, Andreas Look, Barbara Rakitsch, Melih Kandemir:
Learning Partially Known Stochastic Dynamics with Empirical PAC Bayes. AISTATS 2021: 478-486 - [i4]Krista Longi, Jakob Lindinger, Olaf Duennbier, Melih Kandemir, Arto Klami, Barbara Rakitsch:
Traversing Time with Multi-Resolution Gaussian Process State-Space Models. CoRR abs/2112.03230 (2021) - 2020
- [c4]Jakob Lindinger, David Reeb, Christoph Lippert, Barbara Rakitsch:
Beyond the Mean-Field: Structured Deep Gaussian Processes Improve the Predictive Uncertainties. NeurIPS 2020 - [i3]Jakob Lindinger, David Reeb, Christoph Lippert, Barbara Rakitsch:
Beyond the Mean-Field: Structured Deep Gaussian Processes Improve the Predictive Uncertainties. CoRR abs/2005.11110 (2020) - [i2]Manuel Haußmann, Sebastian Gerwinn, Andreas Look, Barbara Rakitsch, Melih Kandemir:
Learning Partially Known Stochastic Dynamics with Empirical PAC Bayes. CoRR abs/2006.09914 (2020)
2010 – 2019
- 2018
- [c3]David Reeb, Andreas Doerr, Sebastian Gerwinn, Barbara Rakitsch:
Learning Gaussian Processes by Minimizing PAC-Bayesian Generalization Bounds. NeurIPS 2018: 3341-3351 - [i1]David Reeb, Andreas Doerr, Sebastian Gerwinn, Barbara Rakitsch:
Learning Gaussian Processes by Minimizing PAC-Bayesian Generalization Bounds. CoRR abs/1810.12263 (2018) - 2015
- [b1]Barbara Rakitsch:
Modeling the polygenic architecture of complex traits. Tübingen University, Germany, 2015 - 2013
- [j2]Barbara Rakitsch, Christoph Lippert, Oliver Stegle, Karsten M. Borgwardt:
A Lasso multi-marker mixed model for association mapping with population structure correction. Bioinform. 29(2): 206-214 (2013) - [c2]Barbara Rakitsch, Christoph Lippert, Karsten M. Borgwardt, Oliver Stegle:
It is all in the noise: Efficient multi-task Gaussian process inference with structured residuals. NIPS 2013: 1466-1474 - 2011
- [j1]Limin Li, Barbara Rakitsch, Karsten M. Borgwardt:
ccSVM: correcting Support Vector Machines for confounding factors in biological data classification. Bioinform. 27(13): 342-348 (2011) - 2010
- [c1]Barbara Rakitsch, Andreas Bernauer, Oliver Bringmann, Wolfgang Rosenstiel:
Pruning population size in XCS for complex problems. IJCNN 2010: 1-8
Coauthor Index
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last updated on 2024-10-25 21:14 CEST by the dblp team
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