| Functional Isolation Forest G Staerman, P Mozharovskyi, S Clémençon, F d'Alché-Buc Asian Conference of Machine Learning, 332-347, 2019 | 77 | 2019 |
| Automatic Text Evaluation through the Lens of Wasserstein Barycenters P Colombo, G Staerman, C Clavel, P Piantanida Conference on Empirical Methods in Natural Language Processing, 10450--10466, 2021 | 55* | 2021 |
| Beyond Mahalanobis Distance for Textual OOD Detection P Colombo, EDC Gomes, G Staerman, N Noiry, P Piantanida Advances in Neural Information Processing Systems, 2022 | 51 | 2022 |
| Learning Disentangled Textual Representations via Statistical Measures of Similarity P Colombo, G Staerman, N Noiry, P Piantanida 60th Annual Meeting of the Association for Computational Linguistics, 2614–2630, 2022 | 42 | 2022 |
| Unsupervised layer-wise score aggregation for textual ood detection M Darrin, G Staerman, EDC Gomes, JCK Cheung, P Piantanida, ... The 38th Annual AAAI Conference on Artificial Intelligence (AAAI) 2024, 2023 | 39 | 2023 |
| When ot meets mom: Robust estimation of wasserstein distance G Staerman, P Laforgue, P Mozharovskyi, F d’Alché-Buc International Conference on Artificial Intelligence and Statistics, 136-144, 2021 | 34 | 2021 |
| A pseudo-metric between probability distributions based on depth-trimmed regions G Staerman, P Mozharovskyi, P Colombo, S Clémençon, F d'Alché-Buc Transaction of Machine Learning Research (TMLR), 2021 | 33 | 2021 |
| The area of the convex hull of sampled curves: a robust functional statistical depth measure G Staerman, P Mozharovskyi, S Clémençon International Conference on Artificial Intelligence and Statistics, 570-579, 2020 | 27 | 2020 |
| Functional anomaly detection: a benchmark study G Staerman, E Adjakossa, P Mozharovskyi, V Hofer, JS Gupta, ... arXiv preprint arXiv:2201.05115, 2022 | 17 | 2022 |
| Generalization bounds in the presence of outliers: a median-of-means study P Laforgue, G Staerman, S Clémençon International conference on machine learning, 5937-5947, 2021 | 17* | 2021 |
| A Halfspace-Mass Depth-Based Method for Adversarial Attack Detection M Picot, F Granese, G Staerman, M Romanelli, F Messina, P Piantanida, ... Transactions on Machine Learning Research (TMLR), 2023 | 16 | 2023 |
| Affine invariant integrated rank-weighted statistical depth: properties and finite sample analysis S Clémençon, P Mozharovskyi, G Staerman Electronic Journal of Statistics 17 (2), 3854-3892, 2023 | 15* | 2023 |
| Functional anomaly detection and robust estimation G Staerman Institut Polytechnique de Paris, 2022 | 13 | 2022 |
| A Simple Unsupervised Data Depth-based Method to Detect Adversarial Images M Picot, G Staerman, F Granese, N Noiry, F Messina, P Piantanida, ... | 11 | 2023 |
| Functional anomaly detection: a benchmark study G Staerman, E Adjakossa, P Mozharovskyi, V Hofer, J Sen Gupta, ... International Journal of Data Science and Analytics 16 (1), 101-117, 2023 | 10 | 2023 |
| Toward Stronger Textual Attack Detectors P Colombo, M Picot, N Noiry, G Staerman, P Piantanida Findings of EMNLP 2023, 2023 | 7 | 2023 |
| Hypothesis Transfer Learning with Surrogate Classification Losses: Generalization Bounds through Algorithmic Stability A Aghbalou, G Staerman International Conference on Machine Learning, 280-303, 2023 | 7* | 2023 |
| Fadin: Fast discretized inference for hawkes processes with general parametric kernels G Staerman, C Allain, A Gramfort, T Moreau International Conference on Machine Learning, 32575-32597, 2023 | 6 | 2023 |
| A Functional Data Perspective and Baseline On Multi-Layer Out-of-Distribution Detection E Dadalto, P Colombo, G Staerman, N Noiry, P Piantanida arXiv preprint arXiv:2306.03522, 2023 | 2* | 2023 |
| Flexible parametric inference for space-time Hawkes processes E Siviero, G Staerman, S Clémençon, T Moreau arXiv preprint arXiv:2406.06849, 2024 | 1 | 2024 |
Pierre ColomboCS of Equall & Ass. Prof @Univ ParisSacaly (CentraleSupelec)כתובת אימייל מאומתת בדומיין centralesupelec.fr