Description

Leader of the group Machine Learning: Peter Grunwald.

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

Sander Bohté, professor by special appointment of Cognitive Neurobiology at UvA

Sander Bohté (1974) has been named professor by special appointment of Cognitive Neurobiology, specialising in Computational Neuroscience, at the University of Amsterdam (UvA)'s Faculty of Science. The chair was designated on behalf of the Science Plus Foundation (Stichting Bèta Plus). He will be combining his professorship with his position as senior researcher at CWI's Machine Learning group.

Sander Bohté, professor by special appointment of Cognitive Neurobiology at UvA - Read More…

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.

Sterkenburg argues universal method of prediction is impossible - Read More…

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 participates in new NWO Perspectief programme - Read More…

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).

CWI researchers selected as ACM Future of Computing Academy members - Read More…

Current events

ML Seminar: Tristan Cossio (Netflix)

2018-07-19T11:00:00+02:00
2018-07-19T12:00:00+02:00

July 19 Thursday

ML Seminar: Tristan Cossio (Netflix)

Start: 2018-07-19 11:00:00+02:00 End: 2018-07-19 12:00:00+02:00

CWI, Lecture room L016

With pleasure we announce this seminar: Data Science at Netflix

Data science at Netflix is much more than just personalized recommendations (e.g. the 2006-2009 Netflix Prize) - it is also a
critical aspect of streaming, quality control, and content development and buying decisions. This talk surveys the breadth of data science work at Netflix, with a specific focus on how the Content Science & Algorithms team (based in Hollywood CA) uses machine learning for valuation and programming of Licensed & Original content.

ML Seminar: Jaron Sanders(Delft University)

2018-09-20T10:00:00+02:00
2018-09-20T11:00:00+02:00

September 20 Thursday

ML Seminar: Jaron Sanders(Delft University)

Start: 2018-09-20 10:00:00+02:00 End: 2018-09-20 11:00:00+02:00

CWI, Lecture room L016

We have the pleasure to announce our CWI Machine Learning seminar with Jaron Sanders with the title: Optimal Clustering Algorithms in Block Markov Chains

This paper considers cluster detection in Block Markov Chains (BMCs). These Markov chains are characterized by a block structure in their transition matrix. More precisely, the n possible states are divided into a finite number of K groups or clusters, such that states in the same cluster exhibit the same transition rates to other states. One observes a trajectory of the Markov chain, and the objective is to recover, from this observation only, the (initially unknown) clusters. In this paper we devise a clustering procedure that accurately, efficiently, and provably detects the clusters. We first derive a fundamental information-theoretical lower bound on the detection error rate satisfied under any clustering algorithm. This bound identifies the parameters of the BMC, and trajectory lengths, for which it is possible to accurately detect the clusters. We next develop two clustering algorithms that can together accurately recover the cluster structure from the shortest possible trajectories, whenever the parameters allow detection. These algorithms thus reach the fundamental detectability limit, and are optimal in that sense.

Members

Associated Members

Publications

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.

Squint: Experimenting in Prediction with Expert Advice problems -

Current projects with external funding

Deep Spiking Vision: Better, Faster, Cheaper (DEVIS)
Efficient Deep Learning Platforms (eDLP)
Enabling Personalized Interventions (EPI)
Safe Bayesian Inference: A Theory of Misspecification based on Statistical Learning (SAFEBAYES)

Related partners

Philips
KPMG
SURFsara B.V.
Universiteit van Amsterdam
Vrije Universiteit Amsterdam