123 citations · 480 across the 32 of their papers we have counts for
39 papers
Langevin-Informed Transfer Learning: Replacing Target Samples by Black-Box Feedback
Vladimir R. Kostic, Karim Lounici, Hélène Halconruy +3
Many scientific and machine learning systems, from molecular dynamics to diffusion models and beyond, are governed by stochastic dynamics with low-dimensional structure, evolving o…
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…
Representation Learning for Equivariant Inference with Guarantees
Daniel Ordoñez-Apraez, Vladimir Kostić, Alek Fröhlich +3
In many real-world applications of regression, conditional probability estimation, and uncertainty quantification, exploiting symmetries rooted in physics or geometry can dramatica…
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…
Laplace Transform Based Low-Complexity Learning of Continuous Markov Semigroups
Vladimir R. Kostic, Karim Lounici, Hélène Halconruy +3
Markov processes serve as a universal model for many real-world random processes. This paper presents a data-driven approach for learning these models through the spectral decompos…
Neural Conditional Probability for Uncertainty Quantification
Vladimir R. Kostic, Karim Lounici, Gregoire Pacreau +3
We introduce Neural Conditional Probability (NCP), an operator-theoretic approach to learning conditional distributions with a focus on statistical inference tasks. NCP can be used…