| Deep variational information bottleneck AA Alemi, I Fischer, JV Dillon, K Murphy arXiv preprint arXiv:1612.00410, 2016 | 1471 | 2016 |
| Can you trust your model's uncertainty? evaluating predictive uncertainty under dataset shift Y Ovadia, E Fertig, J Ren, Z Nado, D Sculley, S Nowozin, J Dillon, ... Advances in neural information processing systems 32, 2019 | 1407 | 2019 |
| Likelihood ratios for out-of-distribution detection J Ren, PJ Liu, E Fertig, J Snoek, R Poplin, M Depristo, J Dillon, ... Advances in neural information processing systems 32, 2019 | 603 | 2019 |
| Fixing a broken ELBO A Alemi, B Poole, I Fischer, J Dillon, RA Saurous, K Murphy International conference on machine learning, 159-168, 2018 | 570 | 2018 |
| Tensorflow distributions JV Dillon, I Langmore, D Tran, E Brevdo, S Vasudevan, D Moore, B Patton, ... arXiv preprint arXiv:1711.10604, 2017 | 515 | 2017 |
| Neutra-lizing bad geometry in hamiltonian monte carlo using neural transport M Hoffman, P Sountsov, JV Dillon, I Langmore, D Tran, S Vasudevan arXiv preprint arXiv:1903.03704, 2019 | 95 | 2019 |
| The Locally Weighted Bag of Words Framework for Document Representation. G Lebanon, Y Mao, J Dillon Journal of Machine Learning Research 8 (10), 2007 | 95 | 2007 |
| Uncertainty in the variational information bottleneck AA Alemi, I Fischer, JV Dillon arXiv preprint arXiv:1807.00906, 2018 | 84 | 2018 |
| Density of states estimation for out of distribution detection W Morningstar, C Ham, A Gallagher, B Lakshminarayanan, A Alemi, ... International Conference on Artificial Intelligence and Statistics, 3232-3240, 2021 | 69 | 2021 |
| Can you trust your model’s uncertainty Y Ovadia, E Fertig, J Ren, Z Nado, D Sculley, S Nowozin, JV Dillon, ... evaluating predictive uncertainty under dataset shift, 2019 | 56 | 2019 |
| Sequential document visualization Y Mao, J Dillon, G Lebanon IEEE transactions on visualization and computer graphics 13 (6), 1208-1215, 2007 | 52 | 2007 |
| The k-tied normal distribution: A compact parameterization of Gaussian mean field posteriors in Bayesian neural networks J Swiatkowski, K Roth, B Veeling, L Tran, J Dillon, J Snoek, S Mandt, ... International Conference on Machine Learning, 9289-9299, 2020 | 49 | 2020 |
| Hydra: Preserving ensemble diversity for model distillation L Tran, BS Veeling, K Roth, J Swiatkowski, JV Dillon, J Snoek, S Mandt, ... arXiv preprint arXiv:2001.04694, 2020 | 44 | 2020 |
| tfp. mcmc: Modern Markov chain Monte Carlo tools built for modern hardware J Lao, C Suter, I Langmore, C Chimisov, A Saxena, P Sountsov, D Moore, ... arXiv preprint arXiv:2002.01184, 2020 | 36 | 2020 |
| Deep variational information bottleneck. arXiv 2016 AA Alemi, I Fischer, JV Dillon, K Murphy arXiv preprint arXiv:1612.00410, 0 | 35 | |
| Stochastic composite likelihood JV Dillon, G Lebanon The Journal of Machine Learning Research 11, 2597-2633, 2010 | 33 | 2010 |
| A unified optimization framework for robust pseudo-relevance feedback algorithms JV Dillon, K Collins-Thompson Proceedings of the 19th ACM international conference on Information and …, 2010 | 29 | 2010 |
| Statistical translation, heat kernels and expected distances J Dillon, Y Mao, G Lebanon, J Zhang arXiv preprint arXiv:1206.5248, 2012 | 26 | 2012 |
| PACm-Bayes: Narrowing the empirical risk gap in the misspecified Bayesian regime WR Morningstar, A Alemi, JV Dillon International Conference on Artificial Intelligence and Statistics, 8270-8298, 2022 | 21 | 2022 |
| Joint distributions for tensorflow probability D Piponi, D Moore, JV Dillon arXiv preprint arXiv:2001.11819, 2020 | 19 | 2020 |
Alexander A AlemiGoogle ResearchVerified email at google.com
