Publications

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Journals

Scalable Variational Bayes methods for Hawkes processes. D. Sulem, V. Rivoirard, J. Rousseau (2025+). Accepted for publication at JMLR.

Estimating the history of a random recursive tree. S. Briend, C. Giraud, G. Lugosi, D. Sulem (2025+). Accepted for publication at Bernoulli.

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.

Preprints

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).

Working papers

Bayesian computation for high-dimensional Gaussian Graphical Models with spike-and-slab prior. D. Sulem., J. Jewson, D. Rossell.

Minimax estimation of nonlinear Hawkes processes with kernel density estimators, V. Rivoirard, D. Sulem.

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

Posterior concentration rates in high-dimensional Hawkes processes, V. Rivoirard, J. Rousseau, D. Sulem.

Flexible estimation of spatio-temporal Hawkes processes, W. Liu, X. Miscouridou, D. Sulem