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.
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

Chandorkar, M.H. (2019, November 14). Machine learning in space weather : forecasting, identification and uncertainty quantification.

Borovykh, A.I, Oosterlee, C.W, & Bohte, S.M. (2019). Generalization in fullyconnected neural networks for time series forecasting. Journal of Computational Science, 36, 1–15. doi:10.1016/j.jocs.2019.07.007

Yin, B, Balvert, M, van der Spek, R.A.A, Dutilh, B.E, Bohte, S.M, Veldink, J, & Schönhuth, A. (2019). Using the structure of genome data in the design of deep neural networks for predicting amyotrophic lateral sclerosis from genotype. In Bioinformatics (Vol. 35, pp. i538–i547). doi:10.1093/bioinformatics/btz369

Mhammedi, Z, KoolenWijkstra, W.M, & van Erven, T.A.L. (2019). Lipschitz Adaptivity with Multiple Learning Rates in Online Learning. In Proceedings of Machine Learning Research (pp. 1–22).

van Doorn, J, Ly, A, Marsman, M, & Wagenmakers, E.J. (2019). Bayesian estimation of Kendall's τ using a latent normal approach. Statistics & Probability Letters, 145, 268–272. doi:10.1016/j.spl.2018.10.004

Zambrano, D, Nusselder, R.B.P, Scholte, H.S, & Bohte, S.M. (2019). Sparse computation in adaptive spiking neural networks. Frontiers in Neuroscience, 12(JAN). doi:10.3389/fnins.2018.00987

Degenne, R.R.B.P, KoolenWijkstra, W.M, & Ménard, P. (2019). NonAsymptotic Pure Exploration by Solving Games. In Advances in meural information processing systems.

Love, J, Selker, R, Marsman, M, Jamil, T, Dropmann, D, Verhagen, J, … Wagenmakers, E.J. (2019). JASP: Graphical statistical software for common statistical designs. Journal of Statistical Software, 88(1). doi:10.18637/jss.v088.i02

Kaufmann, E, KoolenWijkstra, W.M, & Garivier, A. (2018). Sequential test for the lowest mean: From Thompson to Murphy sampling. In Advances in Neural Information Processing Systems (pp. 6332–6342).

Karamanis, M, Zambrano, D, & Bohte, S.M. (2018). Continuoustime spikebased reinforcement learning for working memory tasks. In Lecture Notes in Computer Science/Lecture Notes in Artificial Intelligence (pp. 250–262). doi:10.1007/9783030014216_25
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)

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