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ORCIDSinead Williamson
Person information
- affiliation: University of Texas at Austin, Department of Statistics and Data Science, Austin, TX, USA
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2020 – today
- 2024
[c18]Luhuan Wu, Sinead A. Williamson:
Posterior Uncertainty Quantification in Neural Networks using Data Augmentation. AISTATS 2024: 3376-3384- [i18]Luhuan Wu, Sinead Williamson:
Posterior Uncertainty Quantification in Neural Networks using Data Augmentation. CoRR abs/2403.12729 (2024) - 2023
- [j10]Michael Minyi Zhang
, Bianca Dumitrascu, Sinead A. Williamson, Barbara E. Engelhardt:
Sequential Gaussian Processes for Online Learning of Nonstationary Functions. IEEE Trans. Signal Process. 71: 1539-1550 (2023) - [i17]Polina Turishcheva, Jason Ramapuram, Sinead Williamson, Dan Busbridge, Eeshan Gunesh Dhekane, Russ Webb:
Bootstrap Your Own Variance. CoRR abs/2312.03213 (2023) - 2022
- [j9]Mónica Ribero, Jette Henderson, Sinead Williamson, Haris Vikalo:
Federating recommendations using differentially private prototypes. Pattern Recognit. 129: 108746 (2022) - [j8]Michael Minyi Zhang, Sinead A. Williamson, Fernando Pérez-Cruz:
Accelerated parallel non-conjugate sampling for Bayesian non-parametric models. Stat. Comput. 32(3): 50 (2022) - [i16]Reza Namazi, Elahe Ghalebi, Sinead Williamson, Hamidreza Mahyar:
SMGRL: A Scalable Multi-resolution Graph Representation Learning Framework. CoRR abs/2201.12670 (2022) - [i15]Natalie Klein, Amber J. Day, Harris Mason, Michael W. Malone, Sinead A. Williamson:
Denoising neural networks for magnetic resonance spectroscopy. CoRR abs/2211.00080 (2022) - 2021
- [j7]Sinead A. Williamson, Jette Henderson:
Understanding Collections of Related Datasets Using Dependent MMD Coresets. Inf. 12(10): 392 (2021) - 2020
- [j6]Sinead A. Williamson, Michael Minyi Zhang, Paul Damien:
A New Class of Time Dependent Latent Factor Models with Applications. J. Mach. Learn. Res. 21: 27:1-27:24 (2020) - [c17]Kumar Avinava Dubey, Michael Minyi Zhang, Eric P. Xing, Sinead Williamson:
Distributed, partially collapsed MCMC for Bayesian Nonparametrics. AISTATS 2020: 3685-3695 - [c16]Jette Henderson, Shubham Sharma, Alan H. Gee, Valeri Alexiev, Steve Draper, Carlos Marin, Yessel Hinojosa, Christine Draper, Michael Perng, Luis Aguirre, Michael Li, Sara Rouhani, Shorya Consul, Susan Michalski, Akarsh Prasad, Mayank Chutani, Aditya Kumar, Shahzad Alam, Prajna Kandarpa, Binnu Jesudasan, Colton Lee, Michael Criscolo, Sinead Williamson, Matt Sanchez, Joydeep Ghosh:
Certifai: A Toolkit for Building Trust in AI Systems. IJCAI 2020: 5249-5251 - [i14]Avinava Dubey, Michael Minyi Zhang, Eric P. Xing, Sinead A. Williamson:
Distributed, partially collapsed MCMC for Bayesian Nonparametrics. CoRR abs/2001.05591 (2020) - [i13]Mónica Ribero, Jette Henderson, Sinead Williamson, Haris Vikalo:
Federating Recommendations Using Differentially Private Prototypes. CoRR abs/2003.00602 (2020) - [i12]Shorya Consul, Sinead Williamson:
Differentially Private Median Forests for Regression and Classification. CoRR abs/2006.08795 (2020) - [i11]Sinead A. Williamson:
ANOVA exemplars for understanding data drift. CoRR abs/2006.14621 (2020)
2010 – 2019
- 2019
- [j5]Michael Minyi Zhang, Sinead A. Williamson:
Embarrassingly Parallel Inference for Gaussian Processes. J. Mach. Learn. Res. 20: 169:1-169:26 (2019) - [c15]Mohamed Baker Alawieh, Sinead A. Williamson, David Z. Pan:
Rethinking Sparsity in Performance Modeling for Analog and Mixed Circuits using Spike and Slab Models. DAC 2019: 65 - [c14]Maurice Diesendruck, Ethan R. Elenberg, Rajat Sen, Guy W. Cole, Sanjay Shakkottai, Sinead A. Williamson:
Importance Weighted Generative Networks. ECML/PKDD (2) 2019: 249-265 - [c13]Sinead A. Williamson, Mauricio Tec:
Random Clique Covers for Graphs with Local Density and Global Sparsity. UAI 2019: 228-238 - [i10]Thom Lake, Sinead A. Williamson, Alexander T. Hawk, Christopher C. Johnson, Benjamin P. Wing:
Large-scale Collaborative Filtering with Product Embeddings. CoRR abs/1901.04321 (2019) - [i9]Guy W. Cole, Sinead A. Williamson:
Stochastic Blockmodels with Edge Information. CoRR abs/1904.02016 (2019) - [i8]Sinead A. Williamson, Michael Minyi Zhang, Paul Damien:
A New Class of Time Dependent Latent Factor Models with Applications. CoRR abs/1904.08548 (2019) - [i7]Michael Minyi Zhang, Bianca Dumitrascu, Sinead A. Williamson, Barbara E. Engelhardt:
Sequential Gaussian Processes for Online Learning of Nonstationary Functions. CoRR abs/1905.10003 (2019) - [i6]Elahe Ghalebi, Hamidreza Mahyar, Radu Grosu, Sinead Williamson:
Dynamic Nonparametric Edge-Clustering Model for Time-Evolving Sparse Networks. CoRR abs/1905.11724 (2019) - [i5]Guy W. Cole, Sinead A. Williamson:
Avoiding Resentment Via Monotonic Fairness. CoRR abs/1909.01251 (2019) - [i4]Elahe Ghalebi, Hamidreza Mahyar, Radu Grosu, Graham W. Taylor, Sinead A. Williamson:
A Nonparametric Bayesian Model for Sparse Temporal Multigraphs. CoRR abs/1910.05098 (2019) - 2018
- [j4]Markus Peters, Maytal Saar-Tsechansky, Wolfgang Ketter, Sinead A. Williamson, Perry Groot, Tom Heskes:
A scalable preference model for autonomous decision-making. Mach. Learn. 107(6): 1039-1068 (2018) - [i3]Maurice Diesendruck, Ethan R. Elenberg, Rajat Sen, Guy W. Cole, Sanjay Shakkottai, Sinead A. Williamson:
Importance weighted generative networks. CoRR abs/1806.02512 (2018) - 2017
- [j3]Finale Doshi-Velez, Sinead A. Williamson:
Restricted Indian buffet processes. Stat. Comput. 27(5): 1205-1223 (2017) - 2016
- [j2]Sinead A. Williamson:
Nonparametric Network Models for Link Prediction. J. Mach. Learn. Res. 17: 202:1-202:21 (2016) - [c12]Kumar Avinava Dubey, Sashank J. Reddi, Sinead A. Williamson, Barnabás Póczos, Alexander J. Smola, Eric P. Xing:
Variance Reduction in Stochastic Gradient Langevin Dynamics. NIPS 2016: 1154-1162 - 2015
- [j1]Nicholas J. Foti, Sinead A. Williamson:
A Survey of Non-Exchangeable Priors for Bayesian Nonparametric Models. IEEE Trans. Pattern Anal. Mach. Intell. 37(2): 359-371 (2015) - 2014
- [c11]Kumar Avinava Dubey, Qirong Ho, Sinead A. Williamson, Eric P. Xing:
Dependent nonparametric trees for dynamic hierarchical clustering. NIPS 2014: 1152-1160 - [c10]Kumar Avinava Dubey, Sinead Williamson, Eric P. Xing:
Parallel Markov Chain Monte Carlo for Pitman-Yor Mixture Models. UAI 2014: 142-151 - 2013
- [c9]Nicholas J. Foti, Joseph D. Futoma, Daniel N. Rockmore, Sinead Williamson:
A unifying representation for a class of dependent random measures. AISTATS 2013: 20-28 - [c8]Sinead Williamson, Avinava Dubey, Eric P. Xing:
Parallel Markov Chain Monte Carlo for Nonparametric Mixture Models. ICML (1) 2013: 98-106 - [c7]Sinead Williamson, Steve N. MacEachern, Eric P. Xing:
Restricting exchangeable nonparametric distributions. NIPS 2013: 2598-2606 - [c6]Avinava Dubey, Ahmed Hefny, Sinead Williamson, Eric P. Xing:
A Nonparametric Mixture Model for Topic Modeling over Time. SDM 2013: 530-538 - 2012
- [c5]Ke Zhai, Yuening Hu, Jordan L. Boyd-Graber, Sinead Williamson:
Modeling Images using Transformed Indian Buffet Processes. ICML 2012 - [c4]Nicholas J. Foti, Sinead Williamson:
Slice sampling normalized kernel-weighted completely random measure mixture models. NIPS 2012: 2249-2257 - [i2]Nicholas J. Foti, Joseph D. Futoma, Daniel N. Rockmore, Sinead Williamson:
A unifying representation for a class of dependent random measures. CoRR abs/1211.4753 (2012) - [i1]Nicholas J. Foti, Sinead Williamson:
A survey of non-exchangeable priors for Bayesian nonparametric models. CoRR abs/1211.4798 (2012) - 2010
- [c3]Sinead Williamson, Chong Wang, Katherine A. Heller, David M. Blei:
The IBP Compound Dirichlet Process and its Application to Focused Topic Modeling. ICML 2010: 1151-1158 - [c2]Sinead Williamson, Peter Orbanz, Zoubin Ghahramani:
Dependent Indian Buffet Processes. AISTATS 2010: 924-931
2000 – 2009
- 2008
- [c1]Katherine A. Heller, Sinead Williamson, Zoubin Ghahramani:
Statistical models for partial membership. ICML 2008: 392-399
Coauthor Index
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last updated on 2024-09-13 01:38 CEST by the dblp team
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