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
ICML, 2010
Parallel Markov chain Monte Carlo for nonparametric mixture models
S Williamson, A Dubey, E Xing
International Conference on Machine Learning, 98-106, 2013
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
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
Dependent Indian buffet processes
S Williamson, P Orbanz, Z Ghahramani
Proceedings of the thirteenth international conference on artificial …, 2010
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
Statistical models for partial membership
KA Heller, S Williamson, Z Ghahramani
Proceedings of the 25th International Conference on Machine learning, 392-399, 2008
Nonparametric network models for link prediction
SA Williamson
The Journal of Machine Learning Research 17 (1), 7102-7121, 2016
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
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
Focused topic models
S Williamson, C Wang, K Heller, D Blei
NIPS Workshop on Applications for Topic Models: Text and Beyond, 1-4, 2009
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
Embarrassingly parallel inference for Gaussian processes
MM Zhang, SA Williamson
Journal of Machine Learning Research, 2019
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
Dependent nonparametric trees for dynamic hierarchical clustering
KA Dubey, Q Ho, SA Williamson, EP Xing
Advances in Neural Information Processing Systems 27, 2014
Federating recommendations using differentially private prototypes
M Ribero, J Henderson, S Williamson, H Vikalo
Pattern Recognition 129, 108746, 2022
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
Parallel markov chain monte carlo for pitman-yor mixture models
A Dubey, S Williamson, E P Xing
Carnegie Mellon University, 2014
Probabilistic models for data combination in recommender systems
S Williamson, Z Ghahramani
NIPS 2008 Workshop: Learning from Multiple Sources, 2008
Modeling images using transformed Indian buffet processes
Y Hu, K Zhai, S Williamson, J Boyd-Graber
International Conference of Machine Learning 8, 2012
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