5 papers
On the Slow Convergence to Trivial Solutions of Algorithms for Hard Optimization Problems
Ali Hussaini Umar, Jean Barbier, Matthieu Jonckheere +1
Hard combinatorial optimization problems, many of which are NP-hard, present fundamental algorithmic challenges. Average-case analysis on random instances has emerged as a powerful…
The Effect of Label Noise on the Information Content of Neural Representations
Ali Hussaini Umar, Franky Kevin Nando Tezoh, Jean Barbier +2
In supervised classification tasks, models are trained to predict a label for each data point. In real-world datasets, these labels are often noisy due to annotation errors. While…
Information-theoretic reduction of deep neural networks to linear models in the overparametrized proportional regime
Francesco Camilli, Daria Tieplova, Eleonora Bergamin +1
We rigorously analyse fully-trained neural networks of arbitrary depth in the Bayesian optimal setting in the so-called proportional scaling regime where the number of training sam…
Information-theoretic limits and approximate message-passing for high-dimensional time series
Daria Tieplova, Samriddha Lahiry, Jean Barbier
High-dimensional time series appear in many scientific setups, demanding a nuanced approach to model and analyze the underlying dependence structure. Theoretical advancements so fa…
Machine learning for cerebral blood vessels' malformations
Irem Topal, Alexander Cherevko, Yuri Bugay +5
Cerebral aneurysms and arteriovenous malformations are life-threatening hemodynamic pathologies of the brain. While surgical intervention is often essential to prevent fatal outcom…