| Scalable bayesian learning for state space models using variational inference with smc samplers M Hirt, P Dellaportas The 22nd International Conference on Artificial Intelligence and Statistics …, 2019 | 12 | 2019 |
| Copula-like Variational Inference M Hirt, P Dellaportas, A Durmus Advances in Neural Information Processing Systems, 2955-2967, 2019 | 8 | 2019 |
| Reservoir Computing for Macroeconomic Forecasting with Mixed Frequency Data G Ballarin, P Dellaportas, L Grigoryeva, M Hirt, S van Huellen, JP Ortega arXiv preprint arXiv:2211.00363, 2022 | 5 | 2022 |
| Entropy-based adaptive Hamiltonian Monte Carlo M Hirt, M Titsias, P Dellaportas Advances in Neural Information Processing Systems 34, 2021 | 5 | 2021 |
| A Bayesian framework for genome-wide inference of DNA methylation levels M Hirt, A Finke, A Beskos, P Dellaportas, S Beck, I Moghul, S Ecker arXiv preprint arXiv:2211.07311, 2022 | 1 | 2022 |
| Learning multi-modal generative models with permutation-invariant encoders and tighter variational bounds M Hirt, D Campolo, V Leong, JP Ortega arXiv preprint arXiv:2309.00380, 2023 | | 2023 |
| Learning variational autoencoders via MCMC speed measures M Hirt, V Kreouzis, P Dellaportas arXiv preprint arXiv:2308.13731, 2023 | | 2023 |
| A Pose-Informed De-Noising Diffusion Model for Adult Naturalistic EEG Signals A Dutta, M Hirt, L Santamaria, S Georgieva, C Gerloff, B Li, V Leong bioRxiv, 2023.12. 08.567146, 2023 | | 2023 |
| Approximate inference methods in probabilistic machine learning and Bayesian statistics M Hirt UCL (University College London), 2021 | | 2021 |
| Zinsderivate in Multi-Curve-Modellen M Hirt | | 2014 |
| Threshold models and extreme dependence M Hirt | | 2011 |
