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
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.
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.
Members
Associated Members
Publications

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

ter Schure, J.A. (2019). Overal nemen we risico's serieus, behalve op de weg  De Correspondent  2512019.

Dora, S, Pennartz, C, & Bohte, S.M. (2018). A deep predictive coding network for inferring hierarchical causes underlying sensory inputs. In Lecture Notes in Computer Science/Lecture Notes in Artificial Intelligence (pp. 457–467). doi:10.1007/9783030014247_45

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

Pozzi, I, Nusselder, R.B.P, Zambrano, D, Bohte, S.M, & Iliadis, L. (2018). Gating sensory noise in a spiking subtractive LSTM. In V Kůrková, Y Manolopoulos, B Hammer, & I Maglogiannis (Eds.), Artificial Neural Networks and Machine Learning  ICANN 2018 (pp. 284–293). Springer, Cham. doi:10.1007/9783030014186_28

Grünwald, P.D, & de Heide, R. (2018). Invited discussion to the paper Using Stacking to Average Bayesian Predictive Distributions by Yao, Vehtari, Simpson and Gelman. Bayesian Analysis, 13(3), 917–1003.

Yin, B, Balvert, M, Zambrano, D, Schönhuth, A, & Bohte, S.M. (2018). An image representation based convolutional network for DNA classification. In 6th International Conference on Learning Representations.

Grünwald, P.D. (2018). Safe probability. Journal of Statistical Planning and Inference, 195, 47–63. doi:10.1016/j.jspi.2017.09.014

Sterkenburg, T.F. (2018, January 18). Universal prediction : a philosophical investigation.

Leitao Rodriguez, A, Oosterlee, C.W, Ortiz Gracia, L, & Bohte, S.M. (2018). On the datadriven COS method. Applied Mathematics and Computation, 317, 68–84. doi:10.1016/j.amc.2017.09.002
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.

Universiteit van Amsterdam

Vrije Universiteit Amsterdam