All Years Seminars
[INMA] 2025-09-30 (14:00) : Toward Resilient Operation of Large-Scale Cyber-Physical Human Systems
At Euler building (room A.002)
Speaker :
Ahmad Al-Dabbagh (University of British Columbia)
Abstract :
โฏExamples of large-scale cyber-physical human systems are many in society, such as in manufacturing and energyโฏapplications. The systems rely on a high degree of coupling between their cyber, physical, and human components, where operational information is exchanged between the components using communication networks. The involved coupling and the communication networks introduce vulnerabilities which jeopardize the reliability and security of the systems. This seminar provides an overview of decision-making methods for control and monitoring of large-scale cyber-physical human systems, using control theory and artificial intelligence, while focusing on practical challenges related to cybersecurity, fault diagnosis, and alarm management.
[INGI] 2025-09-25 (13:00) : Learning from logical constraints with Lower- and Upper bound arithmetic circuits
At Shannon, Maxwell a.105
Speaker :
Alexandre Dubray (ICTEAM)
Abstract : In this work focuses on the field of ๐ง๐๐ฎ๐ซ๐จ-๐ฌ๐ฒ๐ฆ๐๐จ๐ฅ๐ข๐๐ฅ๐๐๐ซ๐ง๐ข๐ง๐ (NeSy), which aims to bridge the gap between deep learning methods (neural) and the logical knowledge available in certain domains (symbolic). It has been accepted to the Main track at IJCAI 2025, one of the worldโs premier conferences on Artificial Intelligence.
Standard deep learning struggles with logic-based reasoning. One solution is to encode known constraints as ๐๐ซ๐ข๐ญ๐ก๐ฆ๐๐ญ๐ข๐ ๐๐ข๐ซ๐๐ฎ๐ข๐ญ๐ฌ to enable a gradient-based guidance of the parameters being learned. But, for logical knowledge that is too complex to be fully encoded, existing methods use a single lower-bound approximate circuit, often compromising the quality of the computed gradients.
The authors introduce a ๐๐ฎ๐๐ฅ-๐๐จ๐ฎ๐ง๐ ๐๐ฉ๐ฉ๐ซ๐จ๐๐๐ก, ๐ฎ๐ฌ๐ข๐ง๐ ๐๐จ๐ญ๐ก ๐ ๐ฅ๐จ๐ฐ๐๐ซ- ๐๐ง๐ ๐๐ง ๐ฎ๐ฉ๐ฉ๐๐ซ-๐๐จ๐ฎ๐ง๐ ๐๐ข๐ซ๐๐ฎ๐ข๐ญ, to tightly control the error in the gradient approximation. This improves the robustness and trustworthiness of constraint-based learning.
[INMA] 2025-09-23 (14:00) : Newcomers seminars (PhDs)
At Euler building (room a.002)
Section 1:Random Embeddings for Deep Learning: Improving Scalability and Generalization
Speaker :
Roy Makhlouf (PhD UCLouvain/INMA)
Abstract : Increasingly powerful processing units have led to a dramatic surge in the number of parameters of deep neural networks (DNNs), for which training comes with heavy computational costs. As a consequence, it is essential to develop more scalable algorithms for training DNNs.
This work aims to explore the possible benefits of low-dimensional embeddings as a dimensionality reduction technique for DNN training. Instead of considering the entire parameter space, the idea is to restrict training to a lower-dimensional subspace, thereby significantly reducing computational cost. This approach is motivated by prior numerical results, which suggest that training overparameterized neural networks within a subspace of very small dimension still allows to achieve a high test accuracy.
Building on my Master's thesis results, we will first conduct a deeper investigation of random Gaussian embeddings for DNN training under the assumption that the training loss exhibits anisotropic variability. That is, when it varies very slowly along some directions and possibly much faster along others. This setting often occurs in overparameterized neural networks training, where not all parameters influence the training loss equally. We will then consider more structured embeddings known as sparse embeddings, which are closer to techniques already used in deep learning. Finally, we will look at how random embeddings can help avoid sharp spurious minima, a class of minima expected to harm model generalization.
Section 2:Analysis of Hidden Convexity in Neural Networks and Transformers: Toward More Efficient and Robust Deep Learning.
Speaker :
Adeline Colson (PhD UCLouvain/INMA)
Abstract : While nonconvexity is traditionally viewed as a challenge in optimization, many machine learning models exhibit a phenomenon known as benign nonconvexity, where nonconvex formulations are surprisingly tractable and often more scalable than their convex counterparts. The research project aims to understand and leverage this phenomenon to identify models that are both expressive and efficient to train, develop strategies to escape spurious local minima, and propose new formulations with benign nonconvexity across domains. Hidden convexity refers to a convex structure that is not apparent in the original nonconvex problem. By reformulating the problem using local optimality conditions (first and second order), one can analyze it via a convex program. This allows global properties, like optimality of local minima, to be inferred from local conditions.
Section 3:Scalable Control Design for Networked Systems: Coordination through Local Cooperation.
Speaker :
Jonas Hansson (Lund University,Sweden)
Abstract : In this talk, I will present a compositional framework for consensus and coordination, with applications to vehicular formations. The approach, called serial consensus, constructs high-order protocols by cascading simpler first-order dynamics, which makes stability transparent and enables scalable performance guarantees such as string stability. I will also discuss extensions to nonlinear settings, where the framework accommodates constraints such as saturation and time-varying topologies. Altogether, the results show how distributed controllers based only on local relative measurements can ensure robust and scalable coordination in large-scale networks.
[INMA] 2025-09-16 (14:00) : Safety in the Face of Uncertainty: When is a Scenario Decision-Making Algorithm Safe?
At Euler building (room A.002)
Speaker :
Guillaume Berger (UCLouvain)
Abstract :
Making risk-aware decisions in the face of uncertainty is a central problem in many applications of engineering such as autonomous transportation, energy planning, medical devices, etc. Indeed, in these applications, failures or errors come at a high cost, so that it is important to bound the probability of such events. Nevertheless, this problem is often very challenging in practice because the probability distribution of the uncertainty is often unknown to the decision maker, which must thus make decisions in a black-box way. Scenario decision-making is a powerful data-driven approach to risk-aware decision-making, consisting in drawing samples (called scenarios) of the uncertainty and making a decision based on these samples. A key question is when such scenario-based decisions have a low risk. In this talk, I will review the main techniques from the literature for providing such bounds on the risk, and will show that they are incomparable, in that none is more general (i.e., non-vacuous on a larger class of problems) or less conservative than the other. I will then present a more general bound, inspired by the connection between scenario decision-making algorithms, set operators and VC theory. Finally, I will demonstrate the usefulness of the new bound on problems from scenario optimization.
[INGI] 2025-08-26 (11:00) : Robustness for Tabular Machine Learning: A Back-and-Forth Journey between Research and Industry
At Shannon room
Speaker :
Maxime Cordy (SnT Universitรฉ du Luxembourg)
Abstract : Adversarial attacks are widely recognized as a critical security threat for machine learning, but most were originally designed in the context of image recognition, where arbitrary pixel-level perturbations are applied. Such attacks often fail to capture the realities of domains governed by strict constraints on valid inputs. This is particularly the case in tabular machine learning, where only feasible feature-level perturbations can occur. This talk explores the gap between classical adversarial attack formulations and real-world applicability. I will review our recent research on constrained feature-space attacks, which aim to generate realistic adversarial examples under domain-specific restrictions. Drawing on our own experience in applying these methods to industrial use cases, I will highlight the challenges of evaluating robustness in practice and discuss opportunities for new research in this area.
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