| Semi-Supervised Prediction-Constrained Topic Models. MC Hughes, G Hope, L Weiner, TH McCoy Jr, RH Perlis, EB Sudderth, ... AISTATS, 1067-1076, 2018 | 34 | 2018 |
| Prediction-constrained topic models for antidepressant recommendation MC Hughes, G Hope, L Weiner, TH McCoy, RH Perlis, EB Sudderth, ... arXiv preprint arXiv:1712.00499, 2017 | 9 | 2017 |
| Prediction-constrained training for semi-supervised mixture and topic models MC Hughes, L Weiner, G Hope, TH McCoy Jr, RH Perlis, EB Sudderth, ... arXiv preprint arXiv:1707.07341, 2017 | 7 | 2017 |
| A decoder suffices for query-adaptive variational inference S Agarwal, G Hope, A Younis, EB Sudderth The 39th Conference on Uncertainty in Artificial Intelligence, 2023 | 3 | 2023 |
| Prediction-constrained markov models for medical time series with missing data and few labels P Rath, G Hope, K Heuton, EB Sudderth, MC Hughes NeurIPS 2022 Workshop on Learning from Time Series for Health, 2022 | 1 | 2022 |
| Learning Consistent Deep Generative Models from Sparse Data via Prediction Constraints G Hope, M Abdrakhmanova, X Chen, MC Hughes, EB Sudderth arXiv preprint arXiv:2012.06718, 2020 | 1 | 2020 |
| Unbiased learning of deep generative models with structured discrete representations HC Bendekgey, G Hope, E Sudderth Advances in Neural Information Processing Systems 36, 2024 | | 2024 |
| Learning Consistent Deep Generative Models from Sparsely Labeled Data G Hope, M Abdrakhmanova, X Chen, MC Hughes, EB Sudderth Fourth Symposium on Advances in Approximate Bayesian Inference, 2022 | | 2022 |
| Prediction-Constrained Hidden Markov Models for Semi-Supervised Classification G Hope, MC Hughes, F Doshi-Velez, EB Sudderth | | |
| Supplement: Semi-Supervised Prediction-Constrained Topic Models MC Hughes, G Hope, L Weiner, TH McCoy Jr, RH Perlis, EB Sudderth, ... | | |
Erik B. SudderthProfessor of Computer Science, UC IrvineVerified email at uci.edu
