Sinead A. Williamson

Publication Activity (10 Years)

Years Active: 2014-2024
Publications (10 Years): 23

Top Topics

Gaussian Processes

Top Venues

J. Mach. Learn. Res.

Publications

Luhuan Wu, Sinead A. Williamson
Posterior Uncertainty Quantification in Neural Networks using Data Augmentation. AISTATS (2024)
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 (2023)
Natalie Klein, Amber J. Day, Harris Mason, Michael W. Malone, Sinead A. Williamson
Denoising neural networks for magnetic resonance spectroscopy. CoRR (2022)
Michael Minyi Zhang, Sinead A. Williamson, Fernando Pérez-Cruz
Accelerated parallel non-conjugate sampling for Bayesian non-parametric models. Stat. Comput. 32 (3) (2022)
Sinead A. Williamson, Jette Henderson
Understanding Collections of Related Datasets Using Dependent MMD Coresets. Inf. 12 (10) (2021)
Sinead A. Williamson
ANOVA exemplars for understanding data drift. CoRR (2020)
Avinava Dubey, Michael Minyi Zhang, Eric P. Xing, Sinead A. Williamson
Distributed, partially collapsed MCMC for Bayesian Nonparametrics. CoRR (2020)
Sinead A. Williamson, Michael Minyi Zhang, Paul Damien
A New Class of Time Dependent Latent Factor Models with Applications. J. Mach. Learn. Res. 21 (2020)
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)
Michael Minyi Zhang, Bianca Dumitrascu, Sinead A. Williamson, Barbara E. Engelhardt
Sequential Gaussian Processes for Online Learning of Nonstationary Functions. CoRR (2019)
Sinead A. Williamson, Michael Minyi Zhang, Paul Damien
A New Class of Time Dependent Latent Factor Models with Applications. CoRR (2019)
Guy W. Cole, Sinead A. Williamson
Stochastic Blockmodels with Edge Information. CoRR (2019)
Thom Lake, Sinead A. Williamson, Alexander T. Hawk, Christopher C. Johnson, Benjamin P. Wing
Large-scale Collaborative Filtering with Product Embeddings. CoRR (2019)
Sinead A. Williamson, Mauricio Tec
Random Clique Covers for Graphs with Local Density and Global Sparsity. UAI (2019)
Elahe Ghalebi, Hamidreza Mahyar, Radu Grosu, Graham W. Taylor, Sinead A. Williamson
A Nonparametric Bayesian Model for Sparse Temporal Multigraphs. CoRR (2019)
Guy W. Cole, Sinead A. Williamson
Avoiding Resentment Via Monotonic Fairness. CoRR (2019)
Michael Minyi Zhang, Sinead A. Williamson
Embarrassingly Parallel Inference for Gaussian Processes. J. Mach. Learn. Res. 20 (2019)
Maurice Diesendruck, Ethan R. Elenberg, Rajat Sen, Guy W. Cole, Sanjay Shakkottai, Sinead A. Williamson
Importance Weighted Generative Networks. ECML/PKDD (2) (2019)
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) (2018)
Maurice Diesendruck, Ethan R. Elenberg, Rajat Sen, Guy W. Cole, Sanjay Shakkottai, Sinead A. Williamson
Importance weighted generative networks. CoRR (2018)
Finale Doshi-Velez, Sinead A. Williamson
Restricted Indian buffet processes. Stat. Comput. 27 (5) (2017)
Sinead A. Williamson
Nonparametric Network Models for Link Prediction. J. Mach. Learn. Res. 17 (2016)
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)
Nicholas J. Foti, Sinead A. Williamson
A Survey of Non-Exchangeable Priors for Bayesian Nonparametric Models. IEEE Trans. Pattern Anal. Mach. Intell. 37 (2) (2015)
Kumar Avinava Dubey, Qirong Ho, Sinead A. Williamson, Eric P. Xing
Dependent nonparametric trees for dynamic hierarchical clustering. NIPS (2014)