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stat.ML2026

Lipschitz bounds for integral kernels

Justin Reverdi, Sixin Zhang, Fabrice Gamboa +1

Feature maps associated with positive definite kernels play a central role in kernel methods and learning theory, where regularity properties such as Lipschitz continuity are close…

stat.ML2026

Feature Representation Transferring to Lightweight Models via Perception Coherence

Hai-Vy Nguyen, Fabrice Gamboa, Sixin Zhang +3

In this paper, we propose a method for transferring feature representation to lightweight student models from larger teacher models. We mathematically define a new notion called \t…

stat.ML2026

Training More Robust Classification Model via Discriminative Loss and Gaussian Noise Injection

Hai-Vy Nguyen, Fabrice Gamboa, Sixin Zhang +3

Robustness of deep neural networks to input noise remains a critical challenge, as naive noise injection often degrades accuracy on clean (uncorrupted) data. We propose a novel tra…

stat.ML2024

Sensitivity Analysis for Active Sampling, with Applications to the Simulation of Analog Circuits

Reda Chhaibi, Fabrice Gamboa, Christophe Oger +3

We propose an active sampling flow, with the use-case of simulating the impact of combined variations on analog circuits. In such a context, given the large number of parameters, i…

stat.ML2024

Combining Statistical Depth and Fermat Distance for Uncertainty Quantification

Hai-Vy Nguyen, Fabrice Gamboa, Reda Chhaibi +3

We measure the Out-of-domain uncertainty in the prediction of Neural Networks using a statistical notion called ``Lens Depth'' (LD) combined with Fermat Distance, which is able to…