All Years Seminars
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[INGI] 2026-06-07 (13:00) : TBA
At shannon, Maxwell a.105
Speaker :
Peter VAN ROY (UCL)
Abstract : t'a
[INGI] 2026-02-05 (13:00) : Fair Tabular Data Generation: an Approach using Autoregressive Decision Trees
At Nyquist Maxwell a.164
Speaker :
Benoît Ronval (ICTEAM)
Abstract : In both research and industry, tabular data is among the most widely used data types. Represented using instances (rows) with features (columns), such data is easy for humans to interpret and readily usable by most machine learning algorithms.
Despite its large usage, acquiring new tabular data can be challenging. Data collection can require access to private sources or large-scale surveys, which may be costly and may suffer from low response rates. Moreover, real-world tabular datasets frequently exhibit bias, leading machine learning models to produce unfair classifications for certain subgroups, particularly with respect to sensitive attributes such as the nationality or the education level of a person.
In this seminar, I will present our new method TabFairGDT, which aims to generate data that can reduce fairness concerns in the predictions of machine learning models trained on this data. The approach leverages decision trees in an autoregressive generation framework, including a fairness optimization step. I will also discuss the advantages of decision trees for tabular data generation and present experimental results, including classification performance, fairness metrics, and data quality analyses.
[INGI] 2026-01-22 (13:00) : Distributed, Coordination-Free Programming: 10 Years of Progress Since Lasp
At Shannon, Maxwell a.105
Speaker :
Peter VAN ROY (UCL)
Abstract : Consensus is a critical building block for building fault-tolerant distributed systems. It is widely believed that without consensus, large distributed applications on the Internet could not exist. But recent advances show that consistent replication can be achieved without consensus by using convergent data structures such as CRDTs (conflict-free replicated data types). This is called coordination-free programming and it has become a credible alternative to consensus. The Lasp system is the first to compose CRDTs. It was published in 2015 in the ACM Symposium on Principles and Practice of Declarative Programming (PPDP) and the paper won the 10-year most influential paper award at PPDP 2025. Lasp’s coordination-free model has inspired a decade of progress in academia and industry. As the industry shifts toward multi-region deployments, Lasp’s core insight — that coordination can be the exception, not the rule — continues to shape how we build reliable, scalable systems today.
[ELEN] 2026-01-06 (09:00) : Environmental Net Impact of AI - Reviewing Challenges for Decision Making
At Shannon
Speaker :
Daniel Schien (University of Bristol)
Abstract : The net environmental impact of digital services, and artificial intelligence (AI) services in particular, is of increasing interest to society and governance of this part of the economy. The practice of weighing of both positive and negative consequences to arrive at a net impact of digital services has been around for many years and attracted controversy. Efforts such as methodology development through the EU Green Digital Coalition demonstrate ongoing interest as well as the fundamental challenges with this practice. Following a recent pre-print article, I will review existing net-impact methodologies along with the general epistemological challenges and illustrate them through a case study of estimating the benefits from substituting human translation work through LLMs.
[INMA] 2025-12-16 (14:00) : Geometry of low-rank tensors
At EULER (room A.002)
Speaker :
Simon Jacobsson (KU Leuven)
Abstract : Morally, a manifold is a set where notions from calculus are well-defined. For example, the set of n-by-n orthogonal matrices and the set of n-by-n symmetric positive definite matrices are manifolds. Knowing that a set of matrices or tensors is a manifold allows us to use a host of calculus tools to do numerical analysis on that set. For example, many constrained optimization problems can be formulated as unconstrained manifold optimization problems. Algorithms for these can then make use of manifold gradients and analogues of straight lines called geodesics.
We consider the set of fixed-rank tensors. When the rank is sufficiently low, then (almost) any tensor in this set is related to (almost) any other tensor via a change of basis. We explain how this relation induces a manifold structure, and show how relevant quantities like gradients and geodesics can be computed efficiently. More precisely, we identify the set as a quotient of Lie groups. We also discuss how the manifold perspective can be used to integrate tensor differential equations.
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