Research chairs
-
-
ADDX
Objectives of the chair At vero eos et accusamus et iusto odio dignissimos ducimus qui blanditiis praesentium voluptatum deleniti atque corrupti quos dolores et quas molestias excepturi sint occaecati cupiditate non provident, similique sunt in culpa qui officia deserunt mollitia animi, id est laborum et dolorum fuga. Et harum quidem rerum facilis est et expedita
-
AMINA
Objectives of the chair Many problems in machine learning and signal processing involve the optimisation of a loss function with respect to a set of parameters of interest. A common choice is the quadratic loss because it enjoys mathematical properties that make it convenient for optimisation. However, from a modelling point of view, the quadratic
-
MADLADS
Objectives of the chair Representation learning is a fundamental aspect of artificial intelligence that involves automatically extracting a compact and useful representation of input data for classification, clustering, and prediction tasks. However, this can be a challenging task in deep learning, where overparameterization is a concern, and in high-dimensional statistics, where sparse structures may be
-
MoAI
Objectives of the chair Research on ethical artificial intelligence has primarily focused on value alignment —the challenge of ensuring that intelligent machines operate in accordance with human moral norms and values. This approach assumes that morals are fixed: either prescriptive, as defined by experts, or descriptive, as uncovered by behavioral science. While this perspective is
-
RELOAD
Objectives of the chair Deep reinforcement learning (RL) – learning optimal behaviors from interaction data, using deep neural networks – is often seen as one of the next frontiers in artificial intelligence. While current RL algorithms do not escape the relentless pursuit of larger models, bigger data and more computation demands, we posit realworld impacts
-
RL4SN
Objectives of the chair Markov decision processes (MDPs) and their equivalents in reinforcement learning have been highly effective in solving large-scale problems over the past two decades. However, their success often depends on exceptional computational resources, limiting their applicability in contexts with restricted data volume or computing power. In contrast with this general trend, RL4SN
-
HuCAD
Objectives of the chair Research objectives Collaborations