| Normalizing flows for probabilistic modeling and inference G Papamakarios, E Nalisnick, DJ Rezende, S Mohamed, ... Journal of Machine Learning Research 22 (57), 1-64, 2021 | 2035 | 2021 |
| Do deep generative models know what they don't know? E Nalisnick, A Matsukawa, YW Teh, D Gorur, B Lakshminarayanan arXiv preprint arXiv:1810.09136, 2018 | 882 | 2018 |
| Detecting out-of-distribution inputs to deep generative models using typicality E Nalisnick, A Matsukawa, YW Teh, B Lakshminarayanan arXiv preprint arXiv:1906.02994, 2019 | 217 | 2019 |
| Improving document ranking with dual word embeddings E Nalisnick, B Mitra, N Craswell, R Caruana Proceedings of the 25th international conference companion on world wide web …, 2016 | 211 | 2016 |
| Stick-breaking variational autoencoders E Nalisnick, P Smyth arXiv preprint arXiv:1605.06197, 2016 | 206 | 2016 |
| A dual embedding space model for document ranking B Mitra, E Nalisnick, N Craswell, R Caruana arXiv preprint arXiv:1602.01137, 2016 | 184 | 2016 |
| Bayesian batch active learning as sparse subset approximation R Pinsler, J Gordon, E Nalisnick, JM Hernández-Lobato Advances in neural information processing systems 32, 2019 | 147 | 2019 |
| Bayesian deep learning via subnetwork inference E Daxberger, E Nalisnick, JU Allingham, J Antorán, ... International Conference on Machine Learning, 2510-2521, 2021 | 133* | 2021 |
| Character-to-character sentiment analysis in Shakespeare’s plays ET Nalisnick, HS Baird Proceedings of the 51st Annual Meeting of the Association for Computational …, 2013 | 126 | 2013 |
| Approximate inference for deep latent gaussian mixtures E Nalisnick, L Hertel, P Smyth NIPS Workshop on Bayesian Deep Learning 2, 131, 2016 | 115 | 2016 |
| Hybrid models with deep and invertible features E Nalisnick, A Matsukawa, YW Teh, D Gorur, B Lakshminarayanan International Conference on Machine Learning, 4723-4732, 2019 | 100 | 2019 |
| Do Bayesian neural networks need to be fully stochastic? M Sharma, S Farquhar, E Nalisnick, T Rainforth International Conference on Artificial Intelligence and Statistics, 7694-7722, 2023 | 61 | 2023 |
| Calibrated learning to defer with one-vs-all classifiers R Verma, E Nalisnick International Conference on Machine Learning, 22184-22202, 2022 | 54 | 2022 |
| Dropout as a structured shrinkage prior E Nalisnick, JM Hernández-Lobato, P Smyth International Conference on Machine Learning, 4712-4722, 2019 | 48 | 2019 |
| Extracting sentiment networks from Shakespeare's plays ET Nalisnick, HS Baird 2013 12th International Conference on Document Analysis and Recognition, 758-762, 2013 | 48 | 2013 |
| Learning to defer to multiple experts: Consistent surrogate losses, confidence calibration, and conformal ensembles R Verma, D Barrejón, E Nalisnick International Conference on Artificial Intelligence and Statistics, 11415-11434, 2023 | 40 | 2023 |
| On priors for Bayesian neural networks ET Nalisnick University of California, Irvine, 2018 | 39 | 2018 |
| Adapting the linearised laplace model evidence for modern deep learning J Antorán, D Janz, JU Allingham, E Daxberger, RR Barbano, E Nalisnick, ... International Conference on Machine Learning, 796-821, 2022 | 36 | 2022 |
| A scale mixture perspective of multiplicative noise in neural networks E Nalisnick, A Anandkumar, P Smyth arXiv preprint arXiv:1506.03208, 2015 | 27 | 2015 |
| Hate speech criteria: A modular approach to task-specific hate speech definitions U Khurana, I Vermeulen, E Nalisnick, M Van Noorloos, A Fokkens arXiv preprint arXiv:2206.15455, 2022 | 26 | 2022 |
Padhraic SmythDistinguished Professor, Computer Science, University of California IrvineVerified email at ics.uci.edu
