Charles Fefferman

activity

20232026

collaborators

6 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 $M$ denote a diameter 1 d-dimensional ma…

math.OC2024

Almost Optimal Agnostic Control of Unknown Linear Dynamics

Jacob Carruth, Maximilian F. Eggl, Charles Fefferman +1

We consider a simple control problem in which the underlying dynamics depend on a parameter $a$ that is unknown and must be learned. We study three variants of the control problem:…

math.ST2023

Fitting a manifold to data in the presence of large noise

Charles Fefferman, Sergei Ivanov, Matti Lassas +1

We assume that $M_{0}$ is a $d$ -dimensional $C^{2, 1}$ -smooth submanifold of $R^{n}$ . Let $K_{0}$ be the convex hull of $M_{0},$ and $B_{1}^{n} (0)$ be the unit ball. We assume that $ M_0 \subse…

math.OC2023

Controlling Unknown Linear Dynamics with Almost Optimal Regret

Jacob Carruth, Maximilian F. Eggl, Charles Fefferman +1

Here and in a companion paper, we consider a simple control problem in which the underlying dynamics depend on a parameter $a$ that is unknown and must be learned. In this paper, w…

math.OC2023

Optimal Agnostic Control of Unknown Linear Dynamics in a Bounded Parameter Range

Jacob Carruth, Maximilian F. Eggl, Charles Fefferman +1

Here and in a follow-on paper, we consider a simple control problem in which the underlying dynamics depend on a parameter $a$ that is unknown and must be learned. In this paper, w…