Description
Leader of the group Machine Learning: Peter Grünwald.
Our research group focuses on how computer programs can learn from and understand data, and then make useful predictions based on it. These algorithms integrate insights from various fields, including statistics, artificial intelligence and neuroscience.
Machine-learning applications are increasingly part of every aspect of life, from speech recognition on cell phones to illness prediction in healthcare. One common problem is extremely polluted data, for which no single model can provide adequate explanations. At CWI we address this issue with statistical machine learning based on combining predictions from different models and experts in order to achieve reliable conclusions.
We also study how networks of neurons in the brain process information, and how modern deep-learning methods can benefit from neuroscience. We develop novel neural networks, like Deep Adaptive Spiking Neural Networks, and also theoretical models of neural learning and information processing in biology. Applications of our work range from low-energy consumption neural machine learning to neuroprosthetics, to increased insight into the question of how the brain works.
News
Sterkenburg argues universal method of prediction is impossible
Is it possible to formulate a universal method of prediction? PhD candidate Tom Sterkenburg of CWI’s Machine Learning group argues in his thesis that it is not. He will defend his thesis on 18 January 2018.
CWI participates in new NWO Perspectief programme
In the coming years almost a hundred researchers are going to develop innovative technologies together with industry and social organisations. That will happen in six new Perspectief programmes, which have been given the green light by NWO, Netherlands Organisation for Scientific Research, on 21 November 2017. CWI's Machine Learning group participates in the programme Efficient Deep Learning Systems.
CWI researchers selected as ACM Future of Computing Academy members
CWI researchers Tim Baarslag and Wouter Koolen have been selected as members of the of the ACM Future of Computing Academy (FCA).
NWO TOP grant for Peter Grünwald
The Netherlands Organisation for Scientific Research (NWO) has awarded a Physical Sciences TOP grant 1 for curiosity driven research to Peter Grünwald of CWI.
Members
Associated Members
Publications
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Mhammedi, Z, Grünwald, P.D, & Guedj, B. (2019). PAC-Bayes Unexpected Bernstein Inequality. In Proceedings NeurIPS (Annual Conference on Neural Information Processing Systems).
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Grünwald, P.D, & Roos, T. (2019). Minimum Description Length Revisited. Mathematics for industry.
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van Ommen, M, Koolen-Wijkstra, W.M, & Grünwald, P.D. (2019). Efficient algorithms for minimax decisions under tree-structured incompleteness. In Lecture Notes in Computer Science/Lecture Notes in Artificial Intelligence (pp. 336–347). doi:10.1007/978-3-030-29765-7_28
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ter Schure, J.A, & Grünwald, P.D. (2019). Accumulation Bias in meta-analysis: the need to consider time in error control [version 1; peer review: two approved]. F1000Research, 8. doi:10.12688/f1000research.19375.1
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Grünwald, P.D, & Mehta, N.A. (2019). A Tight Excess Risk Bound via a Unified PAC-Bayesian-Rademacher-Shtarkov-MDL Complexity. In Proceedings ALT (Algorithmic Learning Theory).
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Liu, S, Oosterlee, C.W, & Bohte, S.M. (2019). Pricing options and computing implied volatilities using neural networks. Risks, 7(1). doi:10.3390/risks7010016
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Wagenmakers, E.-J, Love, J, Marsman, M, Jamil, T, Ly, A, Verhagen, J, … Morey, R.D. (2019). Bayesian inference for psychology. Part II: Example applications with JASP. Psychonomic Bulletin & Review, 25(1), 58–76. doi:10.3758/s13423-017-1323-7
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Borovykh, A.I, Bohte, S.M, & Oosterlee, C.W. (2019). Dilated convolutional neural networks for time series forecasting. Journal of Computational Finance, 73–101. doi:10.21314/JCF.2019.358
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ter Schure, J.A. (2019). Efficient accumulation of scientific knowledge, research waste and accumulation bias. ERCIM News, 116, 8–9.
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Degenne, R.R.B.P, & Koolen-Wijkstra, W.M. (2019). Pure Exploration with Multiple Correct Answers. In Advances in meural information processing systems.
Software
Squint: Experimenting in Prediction with Expert Advice problems
Squint provides a codebase to perform numerical proof-of-concept experiments in learning theory, particularly in Prediction with Expert Advice problems, a core problem in learning theory.
Current projects with external funding
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Efficient Deep Learning Platforms (eDLP)
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Enabling Personalized Interventions (EPI)
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Human Brain Project - SGA3 (HBP-SGA3)
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Safe Bayesian Inference: A Theory of Misspecification based on Statistical Learning (SAFEBAYES)
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Spiking Neural Networks research program
Related partners
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Philips
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KPMG
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SURFsara B.V.
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Technische Universiteit Eindhoven
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Universiteit Twente
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Universiteit van Amsterdam
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Vrije Universiteit Amsterdam