| The IBP compound Dirichlet process and its application to focused topic modeling S Williamson, C Wang, KA Heller, DM Blei Proceedings of the 27th international conference on machine learning (ICML …, 2010 | 217 | 2010 |
| Variance reduction in stochastic gradient Langevin dynamics KA Dubey, S J Reddi, SA Williamson, B Poczos, AJ Smola, EP Xing Advances in neural information processing systems 29, 2016 | 136 | 2016 |
| Parallel Markov chain Monte Carlo for nonparametric mixture models S Williamson, A Dubey, E Xing International Conference on Machine Learning, 98-106, 2013 | 109 | 2013 |
| The influence of 15-week exercise training on dietary patterns among young adults J Joo, SA Williamson, AI Vazquez, JR Fernandez, MS Bray International Journal of Obesity 43 (9), 1681-1690, 2019 | 107 | 2019 |
| Statistical models for partial membership KA Heller, S Williamson, Z Ghahramani Proceedings of the 25th International Conference on Machine learning, 392-399, 2008 | 87 | 2008 |
| A nonparametric mixture model for topic modeling over time A Dubey, A Hefny, S Williamson, EP Xing Proceedings of the 2013 SIAM international conference on data mining, 530-538, 2013 | 84 | 2013 |
| Nonparametric network models for link prediction SA Williamson Journal of Machine Learning Research 17 (202), 1-21, 2016 | 80 | 2016 |
| Dependent Indian buffet processes S Williamson, P Orbanz, Z Ghahramani Proceedings of the thirteenth international conference on artificial …, 2010 | 70 | 2010 |
| A survey of non-exchangeable priors for Bayesian nonparametric models NJ Foti, SA Williamson IEEE transactions on pattern analysis and machine intelligence 37 (2), 359-371, 2013 | 58 | 2013 |
| Federating recommendations using differentially private prototypes M Ribero, J Henderson, S Williamson, H Vikalo Pattern Recognition 129, 108746, 2022 | 46 | 2022 |
| Scalable Bayesian nonparametric clustering and classification Y Ni, P Müller, M Diesendruck, S Williamson, Y Zhu, Y Ji Journal of Computational and Graphical Statistics 29 (1), 53-65, 2020 | 40 | 2020 |
| Embarrassingly parallel inference for Gaussian processes MM Zhang, SA Williamson Journal of Machine Learning Research 20 (169), 1-26, 2019 | 36 | 2019 |
| Importance weighted generative networks M Diesendruck, ER Elenberg, R Sen, GW Cole, S Shakkottai, ... Joint European Conference on Machine Learning and Knowledge Discovery in …, 2019 | 25 | 2019 |
| Focused topic models S Williamson, C Wang, K Heller, D Blei NIPS Workshop on Applications for Topic Models: Text and Beyond, 1-4, 2009 | 24 | 2009 |
| Sequential Gaussian processes for online learning of nonstationary functions MM Zhang, B Dumitrascu, SA Williamson, BE Engelhardt IEEE Transactions on Signal Processing 71, 1539-1550, 2023 | 22 | 2023 |
| Advanced dietary patterns analysis using sparse latent factor models in young adults J Joo, SA Williamson, AI Vazquez, JR Fernandez, MS Bray The Journal of Nutrition 148 (12), 1984-1992, 2018 | 22 | 2018 |
| A unifying representation for a class of dependent random measures N Foti, J Futoma, D Rockmore, S Williamson Artificial Intelligence and Statistics, 20-28, 2013 | 20 | 2013 |
| Efficient and effective uncertainty quantification for LLMs M Xiong, A Santilli, M Kirchhof, A Golinski, S Williamson Neurips Safe Generative AI Workshop 2024, 2024 | 18 | 2024 |
| Posterior uncertainty quantification in neural networks using data augmentation L Wu, SA Williamson International Conference on Artificial Intelligence and Statistics, 3376-3384, 2024 | 17 | 2024 |
| Dependent nonparametric trees for dynamic hierarchical clustering KA Dubey, Q Ho, SA Williamson, EP Xing Advances in Neural Information Processing Systems 27, 2014 | 17 | 2014 |
Kumar Avinava DubeyGoogle ResearchVerified email at google.com
