Christian Hirsch (RUG) Modeling synaptic plasticity through dynamically reinforced random networks Graph-based Pólya urns are a promising approach to model random processes on networks that exhibit self-reinforcing phenomena such as the ones inspired by synaptic plasticity in neuroscience. As in classic Pólya urns, the network-based analogues behave very differently in…

Mikloz Racz (Princeton) An Adversarial Perspective on Network Disruption I will discuss a simple new model of network disruption, where an adversary can take over a limited number of user profiles in a social network with the aim of maximizing disagreement and/or polarization in the network. I will present both…

Tim Oosterwijk  (UM)  The secretary problem with independent sampling Sequential decision making under uncertainty is a basic problem that bridges several areas. Examples include online algorithms in computer science, and optimal stopping problems with stochastic input in the field of operations research and applied probability. In this talk, I will…

Christopher Hojny (TU/e) Possibilities and Limitations of Symmetry Handling Cutting Planes Branch-and-bound is a well-established tool for solving combinatorial optimization problems. If the combinatorial problem contains symmetric structures (such as symmetric graphs or identical objects), however, branch-and-bound will also explore many symmetric, and thus unnecessary, subproblems during the solving process.…

Matthieu Jonckheere (UBA) Distance learning using Euclidean percolation: Following Fermat's principle In unsupervised statistical learning tasks such as clustering, recommendation, or dimension reduction, a notion of distance or similarity between points is crucial but usually not directly available as an input. We proposed a new density-based estimator for weighted geodesic…

Tim van Erven (UvA) Highlights of Online Machine Learning Online machine learning algorithms process data sequentially, either because the data are inherently sequential or because the whole data set is too large to load into memory all at once (e.g. when training neural networks). I will introduce the formal setting…

Botond Szabo (Bocconi University) On distributed estimation and testing In recent years, the amount of available information has become so vast in certain fields of applications that it is infeasible or undesirable to carry out all the computations on a single server. This has motivated the design and study of…

Adam Zsolt Wagner (Tel Aviv University) Constructions in combinatorics via neural networks Recently, significant progress has been made in the area of machine learning algorithms, and they have quickly become some of the most exciting tools in a scientist’s toolbox. In particular, recent advances in the field of reinforcement learning…

Karl Rohe (UW Madison) Vintage Factor Analysis with Varimax Performs Statistical Inference Psychologists developed Multiple Factor Analysis to decompose multivariate data into a small number of interpretable factors without any a priori knowledge about those factors. In this form of factor analysis, the Varimax "factor rotation" is a key step…

Rik Versendaal (UU)  Sampling simple random graphs under degree and edge-weight constraints In , the problem was studied of sampling simple random graphs with degree constraints, which is typically a hard problem. Therefore, the authors proposed a sequential algorithm that samples such graphs asymptotically uniformly at random, when the number…