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## February 2021

### Eindhoven SPOR Seminar

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 regimes of weak and strong reinforcement. At weak reinforcement, the effect of reinforcement disappears in the long-term limit. At strong reinforcement, we observe clear localization phenomena. Moreover, we present a…

Find out more »### Eindhoven SPOR Seminar

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 theoretical and empirical results. Theoretically, we characterize aspects of the adversary’s optimal decisions and prove bounds on their disruptive power. Furthermore, we present a detailed empirical study of several natural…

Find out more »## March 2021

### Eindhoven SPOR Seminar

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 discuss the secretary problem, where we are faced with an online sequence of elements with values. Upon seeing an element we have to make an irrevocable take-it-or-leave-it decision. The goal…

Find out more »## April 2021

### Eindhoven SPOR Seminar

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. To accelerate the solution procedure, a standard technique is to detect symmetries of the problem and to add inequalities (cutting planes) to the problem formulation that prevent the solver from…

Find out more »## May 2021

### Eindhoven SPOR Seminar

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 distances that takes into account the underlying density of the data, and that is suitable for nonuniform data lying on a manifold of lower dimension than the ambient space. The…

Find out more »## June 2021

### Eindhoven SPOR Seminar

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 and present several highlights of classical and recent progress in this area.

Find out more »## December 2021

### Eindhoven SPOR Seminar

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 distributed statistical or learning approaches. In distributed methods, the data is split amongst different administrative units and computations are carried out locally in parallel to each other. The outcome of…

Find out more »## February 2022

### Eindhoven SPOR Seminar

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 have led computers to reach superhuman level play in Atari games and Go, purely through self-play. In this talk I will give a basic introduction to neural networks and reinforcement…

Find out more »## April 2022

### Eindhoven SPOR seminar

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 to make the factors interpretable. Charles Spearman and many others objected to factor rotations because the factors seem to be rotationally invariant. This is an historical enigma because factor rotations…

Find out more »## May 2022

### Eindhoven SPOR Seminar

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 n of vertices tends to infinity. In this talk, we will extend the algorithm in to also include edge-weights. More specifically, we first assign random weights to all edges according…

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