Sinead Williamson

According to our database, Sinead Williamson authored at least 38 papers between 2008 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Posterior Uncertainty Quantification in Neural Networks using Data Augmentation.

Luhuan Wu, Sinead A. Williamson

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023

Sequential Gaussian Processes for Online Learning of Nonstationary Functions.

Michael Minyi Zhang, Bianca Dumitrascu, Sinead A. Williamson, Barbara E. Engelhardt

IEEE Trans. Signal Process., 2023

Bootstrap Your Own Variance.

Polina Turishcheva, Jason Ramapuram, Sinead Williamson, Dan Busbridge, Eeshan Gunesh Dhekane, Russ Webb

CoRR, 2023

2022

Accelerated parallel non-conjugate sampling for Bayesian non-parametric models.

Michael Minyi Zhang, Sinead A. Williamson, Fernando Pérez-Cruz

Stat. Comput., 2022

Federating recommendations using differentially private prototypes.

Mónica Ribero, Jette Henderson, Sinead Williamson, Haris Vikalo

Pattern Recognit., 2022

Denoising neural networks for magnetic resonance spectroscopy.

Natalie Klein, Amber J. Day, Harris Mason, Michael W. Malone, Sinead A. Williamson

CoRR, 2022

SMGRL: A Scalable Multi-resolution Graph Representation Learning Framework.

Reza Namazi, Elahe Ghalebi, Sinead Williamson, Hamidreza Mahyar

CoRR, 2022

2021

Understanding Collections of Related Datasets Using Dependent MMD Coresets.

Sinead A. Williamson, Jette Henderson

Inf., 2021

2020

A New Class of Time Dependent Latent Factor Models with Applications.

Sinead A. Williamson, Michael Minyi Zhang, Paul Damien

J. Mach. Learn. Res., 2020

ANOVA exemplars for understanding data drift.

Sinead A. Williamson

CoRR, 2020

Differentially Private Median Forests for Regression and Classification.

Shorya Consul, Sinead Williamson

CoRR, 2020

Certifai: A Toolkit for Building Trust in AI Systems.

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

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020

Distributed, partially collapsed MCMC for Bayesian Nonparametrics.

Kumar Avinava Dubey, Michael Minyi Zhang, Eric P. Xing, Sinead Williamson

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Embarrassingly Parallel Inference for Gaussian Processes.

Michael Minyi Zhang, Sinead A. Williamson

J. Mach. Learn. Res., 2019

A Nonparametric Bayesian Model for Sparse Temporal Multigraphs.

Elahe Ghalebi, Hamidreza Mahyar, Radu Grosu, Graham W. Taylor, Sinead A. Williamson

CoRR, 2019

Avoiding Resentment Via Monotonic Fairness.

Guy W. Cole, Sinead A. Williamson

CoRR, 2019

Dynamic Nonparametric Edge-Clustering Model for Time-Evolving Sparse Networks.

Elahe Ghalebi, Hamidreza Mahyar, Radu Grosu, Sinead Williamson

CoRR, 2019

Stochastic Blockmodels with Edge Information.

Guy W. Cole, Sinead A. Williamson

CoRR, 2019

Large-scale Collaborative Filtering with Product Embeddings.

Thom Lake, Sinead A. Williamson, Alexander T. Hawk, Christopher C. Johnson, Benjamin P. Wing

CoRR, 2019

Random Clique Covers for Graphs with Local Density and Global Sparsity.

Sinead A. Williamson, Mauricio Tec

Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

Importance Weighted Generative Networks.

Maurice Diesendruck, Ethan R. Elenberg, Rajat Sen, Guy W. Cole, Sanjay Shakkottai, Sinead A. Williamson

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2019

Rethinking Sparsity in Performance Modeling for Analog and Mixed Circuits using Spike and Slab Models.

Mohamed Baker Alawieh, Sinead A. Williamson, David Z. Pan

Proceedings of the 56th Annual Design Automation Conference 2019, 2019

2018

A scalable preference model for autonomous decision-making.

Markus Peters, Maytal Saar-Tsechansky, Wolfgang Ketter, Sinead A. Williamson, Perry Groot, Tom Heskes

Mach. Learn., 2018

2017

Restricted Indian buffet processes.

Finale Doshi-Velez, Sinead A. Williamson

Stat. Comput., 2017

2016

Nonparametric Network Models for Link Prediction.

Sinead A. Williamson

J. Mach. Learn. Res., 2016

Variance Reduction in Stochastic Gradient Langevin Dynamics.

Kumar Avinava Dubey, Sashank J. Reddi, Sinead A. Williamson, Barnabás Póczos, Alexander J. Smola, Eric P. Xing

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

2015

A Survey of Non-Exchangeable Priors for Bayesian Nonparametric Models.

Nicholas J. Foti, Sinead A. Williamson

IEEE Trans. Pattern Anal. Mach. Intell., 2015

2014

Parallel Markov Chain Monte Carlo for Pitman-Yor Mixture Models.

Kumar Avinava Dubey, Sinead Williamson, Eric P. Xing

Proceedings of the Thirtieth Conference on Uncertainty in Artificial Intelligence, 2014

Dependent nonparametric trees for dynamic hierarchical clustering.

Kumar Avinava Dubey, Qirong Ho, Sinead A. Williamson, Eric P. Xing

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

2013

A Nonparametric Mixture Model for Topic Modeling over Time.

Avinava Dubey, Ahmed Hefny, Sinead Williamson, Eric P. Xing

Proceedings of the 13th SIAM International Conference on Data Mining, 2013

Restricting exchangeable nonparametric distributions.

Sinead Williamson, Steve N. MacEachern, Eric P. Xing

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Parallel Markov Chain Monte Carlo for Nonparametric Mixture Models.

Sinead Williamson, Avinava Dubey, Eric P. Xing

Proceedings of the 30th International Conference on Machine Learning, 2013

A unifying representation for a class of dependent random measures.

Nicholas J. Foti, Joseph D. Futoma, Daniel N. Rockmore, Sinead Williamson

Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics, 2013

2012

Slice sampling normalized kernel-weighted completely random measure mixture models.

Nicholas J. Foti, Sinead Williamson

Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

Modeling Images using Transformed Indian Buffet Processes.

Ke Zhai, Yuening Hu, Jordan L. Boyd-Graber, Sinead Williamson

Proceedings of the 29th International Conference on Machine Learning, 2012

2010

Dependent Indian Buffet Processes.

Sinead Williamson, Peter Orbanz, Zoubin Ghahramani

Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010

The IBP Compound Dirichlet Process and its Application to Focused Topic Modeling.

Sinead Williamson, Chong Wang, Katherine A. Heller, David M. Blei

Proceedings of the 27th International Conference on Machine Learning (ICML-10), 2010

2008

Statistical models for partial membership.

Katherine A. Heller, Sinead Williamson, Zoubin Ghahramani

Proceedings of the Machine Learning, 2008