33 results
(View BibTeX file of all listed publications)

**On the Design of LQR Kernels for Efficient Controller Learning**
*Proceedings of the 56th IEEE Annual Conference on Decision and Control (CDC)*, pages: 5193-5200, IEEE, IEEE Conference on Decision and Control, December 2017 (conference)

**Optimal gamification can help people procrastinate less**
*Annual Meeting of the Society for Judgment and Decision Making*, Annual Meeting of the Society for Judgment and Decision Making, November 2017 (conference)

**Coupling Adaptive Batch Sizes with Learning Rates**
In *Proceedings Conference on Uncertainty in Artificial Intelligence (UAI) 2017*, pages: 410-419, (Editors: Gal Elidan and Kristian Kersting), Association for Uncertainty in Artificial Intelligence (AUAI), Conference on Uncertainty in Artificial Intelligence (UAI), August 2017 (inproceedings)

**Dynamic Time-of-Flight**
*Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017*, pages: 170-179, IEEE, Piscataway, NJ, USA, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017 (conference)

**Virtual vs. Real: Trading Off Simulations and Physical Experiments in Reinforcement Learning with Bayesian Optimization**
In *Proceedings of the IEEE International Conference on Robotics and Automation (ICRA)*, pages: 1557-1563, IEEE, Piscataway, NJ, USA, IEEE International Conference on Robotics and Automation (ICRA), May 2017 (inproceedings)

**Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets**
*Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS 2017)*, 54, pages: 528-536, Proceedings of Machine Learning Research, (Editors: Sign, Aarti and Zhu, Jerry), PMLR, April 2017 (conference)

**An automatic method for discovering rational heuristics for risky choice**
In *Proceedings of the 39th Annual Meeting of the Cognitive Science Society. Austin, TX: Cognitive Science Society*, 2017, Falk Lieder and Paul M. Krueger contributed equally to this publication. (inproceedings)

**A reward shaping method for promoting metacognitive learning**
In *Proceedings of the Third Multidisciplinary Conference on Reinforcement Learning and Decision-Making*, 2017 (inproceedings)

**When does bounded-optimal metareasoning favor few cognitive systems?**
In *AAAI Conference on Artificial Intelligence*, 31, 2017 (inproceedings)

**The Structure of Goal Systems Predicts Human Performance**
In *Proceedings of the 39th Annual Meeting of the Cognitive Science Society*, 2017 (inproceedings)

**Learning to (mis) allocate control: maltransfer can lead to self-control failure**
In *The 3rd Multidisciplinary Conference on Reinforcement Learning and Decision Making. Ann Arbor, Michigan*, 2017 (inproceedings)

**Mouselab-MDP: A new paradigm for tracing how people plan**
In *The 3rd multidisciplinary conference on reinforcement learning and decision making*, 2017 (inproceedings)

**Enhancing metacognitive reinforcement learning using reward structures and feedback**
In *Proceedings of the 39th Annual Meeting of the Cognitive Science Society*, 2017 (inproceedings)

**Helping people choose subgoals with sparse pseudo rewards**
In *Proceedings of the Third Multidisciplinary Conference on Reinforcement Learning and Decision Making*, 2017 (inproceedings)

**Probabilistic Progress Bars**
In *Conference on Pattern Recognition (GCPR)*, 8753, pages: 331-341, Lecture Notes in Computer Science, (Editors: Jiang, X., Hornegger, J., and Koch, R.), Springer, GCPR, September 2014 (inproceedings)

**Probabilistic Solutions to Differential Equations and their Application to Riemannian Statistics**
In *Proceedings of the 17th International Conference on Artificial Intelligence and Statistics*, 33, pages: 347-355, JMLR: Workshop and Conference Proceedings, (Editors: S Kaski and J Corander), Microtome Publishing, Brookline, MA, AISTATS, April 2014 (inproceedings)

**Probabilistic ODE Solvers with Runge-Kutta Means**
In *Advances in Neural Information Processing Systems 27*, pages: 739-747, (Editors: Z. Ghahramani, M. Welling, C. Cortes, N.D. Lawrence and K.Q. Weinberger), Curran Associates, Inc., 28th Annual Conference on Neural Information Processing Systems (NIPS), 2014 (inproceedings)

**Active Learning of Linear Embeddings for Gaussian Processes**
In *Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence*, pages: 230-239, (Editors: NL Zhang and J Tian), AUAI Press , Corvallis, Oregon, UAI2014, 2014, another link: http://arxiv.org/abs/1310.6740 (inproceedings)

**Probabilistic Shortest Path Tractography in DTI Using Gaussian Process ODE Solvers**
In *Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014, Lecture Notes in Computer Science Vol. 8675*, pages: 265-272, (Editors: P. Golland, N. Hata, C. Barillot, J. Hornegger and R. Howe), Springer, Heidelberg, MICCAI, 2014 (inproceedings)

**Sampling for Inference in Probabilistic Models with Fast Bayesian Quadrature**
In *Advances in Neural Information Processing Systems 27*, pages: 2789-2797, (Editors: Z. Ghahramani, M. Welling, C. Cortes, N.D. Lawrence and K.Q. Weinberger), Curran Associates, Inc., 28th Annual Conference on Neural Information Processing Systems (NIPS), 2014 (inproceedings)

**Algorithm selection by rational metareasoning as a model of human strategy selection**
In *Advances in Neural Information Processing Systems 27*, 2014 (inproceedings)

**Incremental Local Gaussian Regression**
In *Advances in Neural Information Processing Systems 27*, pages: 972-980, (Editors: Z. Ghahramani, M. Welling, C. Cortes, N.D. Lawrence and K.Q. Weinberger), 28th Annual Conference on Neural Information Processing Systems (NIPS), 2014, clmc (inproceedings)

**Efficient Bayesian Local Model Learning for Control**
In *Proceedings of the IEEE International Conference on Intelligent Robots and Systems*, pages: 2244 - 2249, IROS, 2014, clmc (inproceedings)

**The high availability of extreme events serves resource-rational decision-making**
In *Proceedings of the 36th Annual Conference of the Cognitive Science Society*, 2014 (inproceedings)

**Layers of Abstraction: (Neuro)computational models of learning local and global statistical regularities**
In *20th Annual Meeting of the Organization for Human Brain Mapping*, 2014 (inproceedings)

**The Randomized Dependence Coefficient**
In *Advances in Neural Information Processing Systems 26*, pages: 1-9, (Editors: C.J.C. Burges, L. Bottou, M. Welling, Z. Ghahramani, and K.Q. Weinberger), 27th Annual Conference on Neural Information Processing Systems (NIPS), 2013 (inproceedings)

**Fast Probabilistic Optimization from Noisy Gradients**
In *Proceedings of The 30th International Conference on Machine Learning, JMLR W&CP 28(1)*, pages: 62–70, (Editors: S Dasgupta and D McAllester), ICML, 2013 (inproceedings)

**Nonparametric dynamics estimation for time periodic systems**
In *Proceedings of the 51st Annual Allerton Conference on Communication, Control, and Computing*, pages: 486-493 , 2013 (inproceedings)

**Analytical probabilistic proton dose calculation and range uncertainties**
In *17th International Conference on the Use of Computers in Radiation Therapy*, pages: 6-11, (Editors: A. Haworth and T. Kron), ICCR, 2013 (inproceedings)

**Animating Samples from Gaussian Distributions**
(8), Max Planck Institute for Intelligent Systems, Tübingen, Germany, 2013 (techreport)

**Controllability and Resource-Rational Planning**
In *Computational and Systems Neuroscience (Cosyne)*, pages: 112, 2013 (inproceedings)

**Learned helplessness and generalization**
In *35th Annual Conference of the Cognitive Science Society*, 2013 (inproceedings)

**Reverse-Engineering Resource-Efficient Algorithms**
In *NIPS Workshop Resource-Efficient Machine Learning*, 2013 (inproceedings)