| Fusion of probability density functions G Koliander, Y El-Laham, PM Djurić, F Hlawatsch Proceedings of the IEEE 110 (4), 404-453, 2022 | 82 | 2022 |
| Synthetic data applications in finance VK Potluru, D Borrajo, A Coletta, N Dalmasso, Y El-Laham, E Fons, ... arXiv preprint arXiv:2401.00081, 2023 | 23 | 2023 |
| Robust covariance adaptation in adaptive importance sampling Y El-Laham, V Elvira, MF Bugallo IEEE Signal Processing Letters 25 (7), 1049-1053, 2018 | 22 | 2018 |
| Hypertime: Implicit neural representation for time series E Fons, A Sztrajman, Y El-Laham, A Iosifidis, S Vyetrenko arXiv preprint arXiv:2208.05836, 2022 | 21 | 2022 |
| Efficient adaptive multiple importance sampling Y El-Laham, L Martino, V Elvira, MF Bugallo 2019 27th European Signal Processing Conference (EUSIPCO), 1-5, 2019 | 20 | 2019 |
| Particle filtering under general regime switching Y El-Laham, L Yang, PM Djurić, MF Bugallo 2020 28th European Signal Processing Conference (EUSIPCO), 2378-2382, 2021 | 18 | 2021 |
| Recursive shrinkage covariance learning in adaptive importance sampling Y El-Laham, V Elvira, M Bugallo 2019 IEEE 8th International Workshop on Computational Advances in Multi …, 2019 | 12 | 2019 |
| A variational adaptive population importance sampler Y El-Laham, PM Djurić, MF Bugallo ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and …, 2019 | 11 | 2019 |
| Policy gradient importance sampling for Bayesian inference Y El-Laham, MF Bugallo IEEE Transactions on Signal Processing 69, 4245-4256, 2021 | 9 | 2021 |
| DSLOB: a synthetic limit order book dataset for benchmarking forecasting algorithms under distributional shift D Cao, Y El-Laham, L Trinh, S Vyetrenko, Y Liu arXiv preprint arXiv:2211.11513, 2022 | 8* | 2022 |
| Indoor altitude estimation of unmanned aerial vehicles using a bank of Kalman filters L Yang, H Wang, Y El-Laham, JIL Fonte, DT Pérez, MF Bugallo ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and …, 2020 | 8 | 2020 |
| Stochastic gradient population monte carlo Y El-Laham, MF Bugallo IEEE Signal Processing Letters 27, 46-50, 2019 | 8 | 2019 |
| Deep gaussian mixture ensembles Y El-Laham, N Dalmasso, E Fons, S Vyetrenko Uncertainty in Artificial Intelligence, 549-559, 2023 | 6 | 2023 |
| Styletime: Style transfer for synthetic time series generation Y El-Laham, S Vyetrenko Proceedings of the Third ACM International Conference on AI in Finance, 489-496, 2022 | 6 | 2022 |
| Robust frequency and phase estimation for three-phase power systems using a bank of Kalman filters ZK Stuart, Y El-Laham, MF Bugallo IEEE Signal Processing Letters 28, 1235-1239, 2021 | 6 | 2021 |
| Augment on manifold: Mixup regularization with umap Y El-Laham, E Fons, D Daudert, S Vyetrenko ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and …, 2024 | 4 | 2024 |
| Neural stochastic differential equations with change points: A generative adversarial approach Z Sun, Y El-Laham, S Vyetrenko ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and …, 2024 | 4 | 2024 |
| Graphical network and topology estimation for autoregressive models using Gibbs sampling M Iloska, Y El-Laham, MF Bugallo Signal Processing 190, 108303, 2022 | 2 | 2022 |
| Particle Gibbs Sampling for Regime-Switching State-Space Models Y El-Laham, L Yang, HJ Lynch, PM Djurić, MF Bugallo ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and …, 2021 | 2 | 2021 |
| A particle Gibbs sampling approach to topology inference in gene regulatory networks M Iloska, Y El-Laham, MF Bugallo ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and …, 2020 | 2 | 2020 |
Svitlana VyetrenkoJ. P. Morgan AI ResearchVerified email at jpmchase.com