6 papers
Density Estimation on Compact Manifolds under Intrinsic Spectral Block Variation
Olga Klopp, Fedor Noskov
We introduce an intrinsic spectral sparsity model for nonparametric density estimation on compact connected Riemannian manifolds. Instead of penalizing coefficients in an arbitrari…
Near-optimal node-private community estimation in polynomial-time
Laurentiu Marchis, Olga Klopp, Po-Ling Loh +1
In this paper, we resolve an open question of Klopp & Zadik (2026) by providing a high-probability polynomial-time, node-private algorithm which nearly matches the performance of t…
Joint learning of a network of linear dynamical systems via total variation penalization
Claire Donnat, Olga Klopp, Hemant Tyagi
We consider the problem of joint estimation of the parameters of linear dynamical systems, given access to single realizations of their respective trajectories, each of length…
Low-Rank Graphon Estimation: Theory and Applications to Graphon Games
Olga Klopp, Fedor Noskov
We study low-rank estimation of an unknown sparse graphon from sampled network data under operator-norm loss, motivated by targeted interventions in graphon games. Starting from th…
Semi-Supervised Learning on Graphs using Graph Neural Networks
Juntong Chen, Claire Donnat, Olga Klopp +1
Graph neural networks (GNNs) work remarkably well in semi-supervised node regression, yet a rigorous theory explaining when and why they succeed remains lacking. To address this ga…
Understanding the Effect of GCN Convolutions in Regression Tasks
Juntong Chen, Johannes Schmidt-Hieber, Claire Donnat +1
Graph Convolutional Networks (GCNs) have become a pivotal method in machine learning for modeling functions over graphs. Despite their widespread success across various application…