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Working papers

Fast and interpretable Bayesian inference in Gaussian Graphical Models. J. Jewson, D. Rossell, D. Sulem.

Estimation of vertices arrival times in temporal random graphs. S. Brient, C. Giraud, G. Lugosi, D. Sulem.

Heterogenous network regression. C. Brownlees, L. Capello, D. Rossell, D. Sulem.

Adaptive estimation and minimax rates of nonlinear Hawkes processes. V. Rivoirard, D. Sulem.

Robust estimation in graph synchronisation. M. Cucuringu, X. Dong, C. Gao, D. Sulem.

Statistical properties of Tree Wasserstein Distance. D. Sulem, M. Yamada.

Preprints

Scalable Variational Bayes methods for Hawkes processes. D. Sulem, V. Rivoirard, J. Rousseau (2022). Under review.

Diverse counterfactual explanations for anomaly detection in time series. D. Sulem, M. Donini, M. B. Zafar, F.-X. Aubet, J. Gasthaus, T. Januschowski, K. Kenthapadi, S. Das, C. Archambeau (2022).

Journals

Bayesian estimation of nonlinear Hawkes processes. D. Sulem, V. Rivoirard, J. Rousseau (2024). Bernoulli, 30(2):1257 – 1286.

Graph similarity learning for change-point detection in dynamic networks. D. Sulem, H. Kenlay, M. Cucuringu, X. Dong (2023), Machine Learning: 1-44.

Simple discrete-time self-exciting models can describe complex dynamic processes: A case study of COVID-19. R. Brownwing, D. Sulem, K. Mengersen, V. Rivoirard, J. Rousseau. PLoS ONE, 2021.

Regularized spectral methods for clustering signed networks. M. Cucuringu, A. Singh, D. Sulem, H. Tyagi. JMLR, 2021.