Adversarial time-to-event modeling P Chapfuwa, C Tao, C Li, C Page, B Goldstein, L Carin, R Henao International Conference on Machine Learning, 735-744, 2018 | 117 | 2018 |
Nash: Toward end-to-end neural architecture for generative semantic hashing D Shen, Q Su, P Chapfuwa, W Wang, G Wang, L Carin, R Henao Association for Computational Linguistics, 2041-2050, 2018 | 63 | 2018 |
Survival cluster analysis P Chapfuwa, C Li, N Mehta, L Carin, R Henao Proceedings of the ACM Conference on Health, Inference, and Learning, 60-68, 2020 | 41 | 2020 |
Enabling counterfactual survival analysis with balanced representations P Chapfuwa, S Assaad, S Zeng, MJ Pencina, L Carin, R Henao Proceedings of the Conference on Health, Inference, and Learning, 133-145, 2021 | 30* | 2021 |
Calibration and uncertainty in neural time-to-event modeling P Chapfuwa, C Tao, C Li, I Khan, KJ Chandross, MJ Pencina, L Carin, ... IEEE transactions on neural networks and learning systems 34 (4), 1666-1680, 2020 | 13* | 2020 |
AIRIVA: a deep generative model of adaptive immune repertoires MF Pradier, N Prasad, P Chapfuwa, S Ghalebikesabi, M Ilse, ... Machine Learning for Healthcare Conference, 588-611, 2023 | 4 | 2023 |
Flexible Triggering Kernels for Hawkes Process Modeling YA Isik, C Davis, P Chapfuwa, R Henao arXiv preprint arXiv:2202.01869, 2022 | 1 | 2022 |
Hawkes Process with Flexible Triggering Kernels Y Isik, P Chapfuwa, C Davis, R Henao Machine Learning for Healthcare Conference, 308-320, 2023 | | 2023 |
AIRIVA: A Deep Generative Model of Adaptive Immune Repertoires (preprint) MF Pradier, N Prasad, P Chapfuwa, S Ghalebikesabi, M Ilse, ... | | 2023 |
Capturing Actionable Dynamics with Structured Latent Ordinary Differential Equations P Chapfuwa, S Rose, L Carin, E Meeds, R Henao Conference on Uncertainty in Artificial Intelligence, 2022 | | 2022 |
Probabilistic Time-to-Event Modeling Approaches for Risk Profiling P Chapfuwa Duke University, 2021 | | 2021 |