| MisGAN: Learning from Incomplete Data with Generative Adversarial Networks SCX Li, B Jiang, B Marlin International Conference on Learning Representations (ICLR), 2019 | 286 | 2019 |
| A Scalable End-to-End Gaussian Process Adapter for Irregularly Sampled Time Series Classification SCX Li, BM Marlin Advances in Neural Information Processing Systems (NeurIPS), 1804-1812, 2016 | 111 | 2016 |
| Learning from Irregularly-Sampled Time Series: A Missing Data Perspective SCX Li, BM Marlin International Conference on Machine Learning (ICML), 2020 | 80 | 2020 |
| Classification of Sparse and Irregularly Sampled Time Series with Mixtures of Expected Gaussian Kernels and Random Features SCX Li, BM Marlin Conference on Uncertainty in Artificial Intelligence (UAI), 484-493, 2015 | 49 | 2015 |
| The Deep and Transient Universe in the SVOM Era: New Challenges and Opportunities-Scientific prospects of the SVOM mission. arXiv e-prints, art J Wei, B Cordier, S Antier, P Antilogus, JL Atteia, A Bajat, S Basa, ... arXiv preprint arXiv:1610.06892, 2016 | 13 | 2016 |
| The deep and transient universe in the SVOM era: New challenges and opportunities-scientific prospects of the SVOM mission, 2016 J Wei, B Cordier, S Antier, P Antilogus, JL Atteia, A Bajat, S Basa, ... ArXiv E-Prints, 0 | 7 | |
| Misgan: learning from incomplete data with generative adversarial networks (2019) SCX Li, B Jiang, BM Marlin arXiv preprint arXiv:1902.09599, 1902 | 6 | 1902 |
| Learning from Irregularly-Sampled Time Series SCX Li | 3 | 2020 |
| Collaborative Multi-Output Gaussian Processes for Collections of Sparse Multivariate Time Series SCX Li, B Marlin NIPS 2015 Time Series Workshop, 2015 | 2 | 2015 |
| Appendix for Classification of Sparse and Irregularly Sampled Time Series with Mixtures of Expected Gaussian Kernels and Random Features SCX Li, B Marlin | | 2015 |
