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…
math.DG2025
Reconstruction and interpolation of manifolds II: Inverse problems with partial data for distances observations and for the heat kernel
Charles Fefferman, Sergei Ivanov, Matti Lassas +2
We consider how a closed Riemannian manifold and its metric tensor can be approximately reconstructed from local distance measurements. Moreover, we consider an inverse pro…