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.
Machinelearning 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 deeplearning 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 lowenergy consumption neural machine learning to neuroprosthetics, to increased insight into the question of how the brain works.
Vacancies
Postdoc position in the area of Spiking Neural Networks
The Machine Learning group at the Centrum Wiskunde & Informatica (CWI) in Amsterdam invites applicants for a Postdoc position (2 or 3 years) in the area of Spiking Neural Networks. We are looking for a motivated postdoctoral researcher with expertise in the area of spiking neural networks and an interest in probabilistic computing.
News
ERCIM News 107 on Machine Learning cocoordinated by Sander Bohte  extra Open Access section
In October 2016, ERCIM News No. 107 was published: http://ercimnews.ercim.eu/en107. It features a special theme on current trends and new paradigms in Machine Learning, coordinated by the guest editors Sander Bohte (CWI ) and Hung Son Nguyen (University of Warsaw).
Bayesian statistics not as robust as commonly thought
The widely used method of Bayesian statistics is not as robust as commonly thought. Researcher Thijs van Ommen of Centrum Wiskunde & Informatica (CWI) discovered that for certain types of problems, Bayesian statistics finds nonexisting patterns in the data. Van Ommen defends his thesis on this topic on Wednesday 10 June at Leiden University.
Researchers develop neural model for working memory
Neuroscientists of Centrum Wiskunde & Informatica (CWI) and the Netherlands Institute for Neuroscience (NIN) have developed a biologically plausible neural network model that can learn to remember past events in order to use them in the future. The researchers developed their model by combining theoretical principles from machine learning with insights from neuroscience.
CWI starts research on spiking neural networks
Sander Bohte, researcher in the life science group of the Centrum Wiskunde & Informatica (CWI) in Amsterdam, starts in collaboration with researchers from the University of Amsterdam (UvA) a new project on spiking neural networks.
Members
Associated Members
Publications

Kruijne, W, Bohte, S.M, Roelfsema, P.R, & Olivers, C.N.L. (2021). Flexible working memory through selective gating and attentional tagging. Neural Computation, 33(1), 1–40. doi:10.1162/neco_a_01339

van Gendt, M.J, Siebrecht, M, Briaire, J.J, Bohte, S.M, & Frijns, J.H.M. (2020). Short and longterm adaptation in the auditory nerve stimulated with highrate electrical pulse trains are better described by a power law. Hearing Research, 398. doi:10.1016/j.heares.2020.108090

Degenne, R.R.B.P, Shao, H, & KoolenWijkstra, W.M. (2020). Structure adaptive algorithms for stochastic bandits. In Proceedings of the 37th International Conference on Machine Learning (pp. 2443–2452).

Mhammedi, Z, & KoolenWijkstra, W.M. (2020). Lipschitz and comparatornorm adaptivity in online learning. In Proceedings of Machine Learning Research (pp. 2858–2887).

Turner, R.J. (2020). Safe Statistics for Means and Proportions.

Grünwald, P.D, & Mehta, N.A. (2020). Fast rates for general unbounded loss functions: From ERM to generalized bayes. Journal of Machine Learning Research, 21, 1–80.

Mhammedi, Z, Grünwald, P.D, & Guedj, B. (2019). PACBayes Unexpected Bernstein Inequality. In Proceedings NeurIPS (Annual Conference on Neural Information Processing Systems).

Grünwald, P.D, & Roos, T. (2019). Minimum Description Length Revisited. Mathematics for industry.

van Ommen, M, KoolenWijkstra, W.M, & Grünwald, P.D. (2019). Efficient algorithms for minimax decisions under treestructured incompleteness. In Lecture Notes in Computer Science/Lecture Notes in Artificial Intelligence (pp. 336–347). doi:10.1007/9783030297657_28

ter Schure, J.A, & Grünwald, P.D. (2019). Accumulation Bias in metaanalysis: the need to consider time in error control [version 1; peer review: two approved]. F1000Research, 8. doi:10.12688/f1000research.19375.1
Software
Squint: Experimenting in Prediction with Expert Advice problems
Squint provides a codebase to perform numerical proofofconcept experiments in learning theory, particularly in Prediction with Expert Advice problems, a core problem in learning theory.
Current projects with external funding

Efficient Deep Learning Platforms (eDLP)

Enabling Personalized Interventions (EPI)

Human Brain Project  SGA3 (HBPSGA3)

Efficient Models of DecisionMaking for Asseing Cognitive Processing States (None)

Perceptive acting under uncertainty: safety solutions for autonomous systems (None)

Safe Bayesian Inference: A Theory of Misspecification based on Statistical Learning (SAFEBAYES)

Spiking Neural Networks research program
Related partners

Philips

KPMG

SURFsara B.V.

Technische Universiteit Eindhoven

Universiteit Twente

Universiteit van Amsterdam

Vrije Universiteit Amsterdam