26 citations · 52 across the 12 of their papers we have counts for
12 papers
VertCoHiRF: Decentralized Vertical Clustering Beyond k-means
Bruno Belucci, Karim Lounici, Vladimir R. Kostic +1
Vertical Federated Learning (VFL) enables collaborative analysis across parties holding complementary feature views of the same samples, yet existing approaches are largely restric…
AdaCap: An Adaptive Contrastive Approach for Small-Data Neural Networks
Bruno Belucci, Karim Lounici, Katia Meziani
Neural networks struggle on small tabular datasets, where tree-based models remain dominant. We introduce Adaptive Contrastive Approach (AdaCap), a training scheme that combines a…
CoHiRF: Hierarchical Consensus for Interpretable Clustering Beyond Scalability Limits
Katia Meziani, Bruno Belucci, Karim Lounici +1
We introduce CoHiRF (Consensus Hierarchical Random Features), a hierarchical consensus framework that enables existing clustering methods to operate beyond their usual computationa…
AdaCap: Adaptive Capacity control for Feed-Forward Neural Networks
Katia Meziani, Karim Lounici, Benjamin Riu
The capacity of a ML model refers to the range of functions this model can approximate. It impacts both the complexity of the patterns a model can learn but also memorization, the…
Muddling Label Regularization: Deep Learning for Tabular Datasets
Karim Lounici, Katia Meziani, Benjamin Riu
Deep Learning (DL) is considered the state-of-the-art in computer vision, speech recognition and natural language processing. Until recently, it was also widely accepted that DL is…
Muddling Labels for Regularization, a novel approach to generalization
Karim Lounici, Katia Meziani, Benjamin Riu
Generalization is a central problem in Machine Learning. Indeed most prediction methods require careful calibration of hyperparameters usually carried out on a hold-out \textit{val…