| Critical Points and Convergence Analysis of Generative Deep Linear Networks Trained with Bures-Wasserstein Loss P Bréchet, K Papagiannouli, J An, G Montúfar International Conference in Machine Learning, 2023 | 4 | 2023 |
| Minimax rates for the covariance estimation of multi-dimensional Lévy processes with high-frequency data K Papagiannouli | 3 | 2020 |
| Online graph-based change-point detection for high dimensional data YW Sun, K Papagiannouli, V Spokoiny arXiv preprint arXiv:1906.03001, 2019 | 2 | 2019 |
| A Lepskiĭ-type stopping rule for the covariance estimation of multi-dimensional Lévy processes K Papagiannouli Statistical Inference for Stochastic Processes 25 (3), 505-535, 2022 | 1 | 2022 |
| High dimensional change-point detection: a complete graph approach YW Sun, K Papagiannouli, V Spokoiny arXiv preprint arXiv:2203.08709, 2022 | 1 | 2022 |
| Convergence of Generative Deep Linear Networks Trained with Bures-Wasserstein Loss P Bréchet, K Papagiannouli, J An, G Montufar | | 2022 |
| A Lepski-type stopping rule for the covariance estimation of multi-dimensional Lévy processes K Papagiannouli Statistical Inference for Stochastic Processes, 2022 | | 2022 |
| Large deviation techniques K Papagiannouli | | 2014 |
| Exit problem from a bounded domain K Papagiannouli | | |
