3 papers
stat.ML2026
High-Dimensional Change Point Detection via Graph Spanning Ratio
Katerina Papagiannouli, Yang-wen Sun, Vladimir Spokoiny
Inspired by graph-based methodologies, we introduce a novel graph-spanning algorithm designed to identify changes in both offline and online data across low to high dimensions. Thi…
math.ST2025
A unified theory of the high-dimensional Laplace approximation with application to Bayesian inverse problems
Anya Katsevich, Vladimir Spokoiny
The Laplace approximation (LA) to posteriors is a ubiquitous tool to simplify Bayesian computation, particularly in the high-dimensional settings arising in Bayesian inverse proble…
math.ST2025
Dimension-free bounds in high-dimensional linear regression via error-in-operator approach
Fedor Noskov, Nikita Puchkin, Vladimir Spokoiny
We consider a problem of high-dimensional linear regression with random design. We suggest a novel approach referred to as error-in-operator which does not estimate the design cova…