2 citations · 2 across the 5 of their papers we have counts for
6 papers
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…
Critical Points and Convergence Analysis of Generative Deep Linear Networks Trained with Bures-Wasserstein Loss
Pierre Bréchet, Katerina Papagiannouli, Jing An +1
We consider a deep matrix factorization model of covariance matrices trained with the Bures-Wasserstein distance. While recent works have made advances in the study of the optimiza…
High dimensional change-point detection: a complete graph approach
Yang-Wen Sun, Katerina Papagiannouli, Vladimir Spokoiny
The aim of online change-point detection is for a accurate, timely discovery of structural breaks. As data dimension outgrows the number of data in observation, online detection be…
A Lepskiĭ-type stopping rule for the covariance estimation of multi-dimensional Lévy processes
Katerina Papagiannouli
We suppose that a Lévy process is observed at discrete time points. Starting from an asymptotically minimax family of estimators for the continuous part of the Lévy Khinchine chara…
Online Graph-Based Change-Point Detection for High Dimensional Data
Yang-Wen Sun, Katerina Papagiannouli, Vladmir Spokoiny
Online change-point detection (OCPD) is important for application in various areas such as finance, biology, and the Internet of Things (IoT). However, OCPD faces major challenges…
Minimax rates for the covariance estimation of multi-dimensional Lévy processes with high-frequency data
Katerina Papagiannouli
This article studies nonparametric methods to estimate the co-integrated volatility for multi-dimensional Lévy processes with high frequency data. We construct a spectral estimator…