Sinead Williamson
Sinead Williamson
Assistant professor, University of Texas at Austin
Verified email at - Homepage
Cited by
Cited by
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
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
Parallel Markov chain Monte Carlo for nonparametric mixture models
S Williamson, A Dubey, E Xing
International Conference on Machine Learning, 98-106, 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
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
Nonparametric network models for link prediction
SA Williamson
Journal of Machine Learning Research 17 (202), 1-21, 2016
Statistical models for partial membership
KA Heller, S Williamson, Z Ghahramani
Proceedings of the 25th International Conference on Machine learning, 392-399, 2008
Dependent Indian buffet processes
S Williamson, P Orbanz, Z Ghahramani
Proceedings of the thirteenth international conference on artificial …, 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
Federating recommendations using differentially private prototypes
M Ribero, J Henderson, S Williamson, H Vikalo
Pattern Recognition 129, 108746, 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
Embarrassingly parallel inference for Gaussian processes
MM Zhang, SA Williamson
Journal of Machine Learning Research 20 (169), 1-26, 2019
Importance weighted generative networks
M Diesendruck, ER Elenberg, R Sen, GW Cole, S Shakkottai, ...
Machine Learning and Knowledge Discovery in Databases: European Conference …, 2020
Focused topic models
S Williamson, C Wang, K Heller, D Blei
NIPS Workshop on Applications for Topic Models: Text and Beyond, 1-4, 2009
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
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
Dependent nonparametric trees for dynamic hierarchical clustering
KA Dubey, Q Ho, SA Williamson, EP Xing
Advances in Neural Information Processing Systems 27, 2014
Modeling images using transformed Indian buffet processes
Y Hu, K Zhai, S Williamson, J Boyd-Graber
International Conference of Machine Learning 8, 2012
Probabilistic models for data combination in recommender systems
S Williamson, Z Ghahramani
NIPS: Learning from Multiple Sources, 2008
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
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