Google scholar

Github

Journals

Scalable Variational Bayes methods for Hawkes processes. D. Sulem, V. Rivoirard, J. Rousseau (2022). Accepted for publication at JMLR.

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

Estimating the history of a random recursive tree. S. Briend, C. Giraud, G. Lugosi, D. Sulem. Under review at Bernoulli.

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. J. Jewson, D. Rossell, 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.