3 papers
stat.ME2026
Denoising data using convex relaxations
Charles Fefferman, Aalok Gangopadhyay, Matti Lassas +2
We study the problem of denoising observations \(Y_i=X_i+Z_i\), where the latent variables \(X_i\) are sampled from a low-dimensional manifold in \(\mathbb{R}^n\) and the noise var…
stat.ML2025
Reconstruction of Manifold Distances from Noisy Observations
Charles Fefferman, Jonathan Marty, Kevin Ren
We consider the problem of reconstructing the intrinsic geometry of a manifold from noisy pairwise distance observations. Specifically, let denote a diameter 1 d-dimensional ma…
cs.LG2023
Adversarial Attacks on the Interpretation of Neuron Activation Maximization
Geraldin Nanfack, Alexander Fulleringer, Jonathan Marty +2
The internal functional behavior of trained Deep Neural Networks is notoriously difficult to interpret. Activation-maximization approaches are one set of techniques used to interpr…