MDS Seminar

With the MDS Seminar, we would like to launch a lecture series in which both researchers from the University of Twente and external researchers present their current work in the field of mathematics of data science. The aim is to get to know and understand the research of other groups and disciplines better. It offers the opportunity for regular exchange as well as a basis for possible collaborations. 

Format
Seminars are held on campus and via Teams. All seminars occur every fortnight on Mondays at 4 p.m. unless otherwise stated (see the program below for the dates and the rooms).

Upcoming seminars

• Speaker: Tom Jacobs (CISPA)
  Title: Controlling Implicit Regularization in Deep Learning via Weight Decay and Mirror Descent
  Abstract: Classical learning theory predicts that overparameterized models should overfit, yet deep neural networks generalize well in this regime. A possible explanation for this is implicit regularization: gradient-based optimization biases solutions toward low-complexity structures (e.g., sparsity or low rank) even without explicit constraints, as observed in settings such as matrix sensing and attention models. In this seminar, I show that weight decay controls this bias: beyond its explicit role as L2-regularization, it modifies the optimization geometry (mirror map), effectively shifting the implicit regularization toward L1-type behavior and thereby promoting sparsity. By turning off weight decay during training, only the implicit effect remains, leading to better generalization. Leveraging this perspective, I introduce PILoT (Parametric Implicit Lottery Ticket), a sparsification method that exploits overparameterization and the L2-to-L1 transition in implicit regularization to produce sparse networks with minimal performance degradation. Building on these insights, I further introduce HAM (Hyperbolic Aware Minimization), a lightweight optimization method that captures the sparsity-inducing implicit bias using mirror descent, thereby directly controlling the implicit bias and leading to improved standard training and state-of-the-art performance in finding sparse networks.

• Speaker: Serte Donderwinkel (RUG)
  Title: Counting connected graphs (or: how to estimate extremely rare events?)
  Abstract: How many connected graphs have a prescribed degree sequence? This classical combinatorial question turns out to admit a surprisingly natural probabilistic interpretation.
  In joint work with Sasha Bell and Remco van der Hofstad, we derive asymptotic formulas for the number of connected graphs with a given degree sequence in the sparse regime. Our approach is based on the probabilistic method: rather than counting graphs directly, we study a random graph model in which the desired structures appear with a certain probability.
  A major challenge is that connectivity is exponentially unlikely when many vertices have degree 1. Estimating probabilities of such rare events is notoriously difficult. We overcome this by using a probabilistic “change of perspective’’ that turns the rare event into a typical one. Concretely, we construct a larger random graph in which the target degree sequence typically emerges in the giant connected component. This viewpoint not only allows us to estimate exponentially small probabilities, but also reveals the most likely mechanism by which the rare event occurs.
  Along the way, I will introduce several probabilistic ideas that have become central in modern network science and random graph theory, including the configuration model, branching process approximations, and local weak convergence, and explain how these tools combine to yield asymptotic counting results.

• Speaker: Yongdai Kim (Seoul National University)
  Title: T.b.a.

• Speaker: Sebastian Kassing (Bergische Universität Wuppertal)
  Title: T.b.a.

• Speaker: Kelan Gray (Imperial College London)
  Title: T.b.a.

• Speaker: Alexis Derumigny (TU Delft)
  Title: T.b.a.

• Speaker: Chen Zhou (Erasmus University Rotterdam)
  Title: T.b.a.

Read more about the past seminars >>