Sinead A. Williamson

25 papers A* 1A 2Journal 19Unranked 3
YearRankTypeTitle / Venue / Authors
2024 A conf
Posterior Uncertainty Quantification in Neural Networks using Data Augmentation.
AISTATS
Luhuan Wu, Sinead A. Williamson
2023 J jnl
Sequential Gaussian Processes for Online Learning of Nonstationary Functions.
IEEE Trans. Signal Process.
Michael Minyi Zhang, Bianca Dumitrascu, Sinead A. Williamson, Barbara E. Engelhardt
2022 J jnl
Accelerated parallel non-conjugate sampling for Bayesian non-parametric models.
Stat. Comput.
Michael Minyi Zhang, Sinead A. Williamson, Fernando Pérez-Cruz
2022 J jnl
Denoising neural networks for magnetic resonance spectroscopy.
CoRR
Natalie Klein, Amber J. Day, Harris Mason, Michael W. Malone, Sinead A. Williamson
2021 J jnl
Understanding Collections of Related Datasets Using Dependent MMD Coresets.
Inf.
Sinead A. Williamson, Jette Henderson
2020 J jnl
A New Class of Time Dependent Latent Factor Models with Applications.
J. Mach. Learn. Res.
Sinead A. Williamson, Michael Minyi Zhang, Paul Damien
2020 J jnl
ANOVA exemplars for understanding data drift.
CoRR
Sinead A. Williamson
2020 J jnl
Distributed, partially collapsed MCMC for Bayesian Nonparametrics.
CoRR
Avinava Dubey, Michael Minyi Zhang, Eric P. Xing, Sinead A. Williamson
2019 J jnl
A New Class of Time Dependent Latent Factor Models with Applications.
CoRR
Sinead A. Williamson, Michael Minyi Zhang, Paul Damien
2019 J jnl
A Nonparametric Bayesian Model for Sparse Temporal Multigraphs.
CoRR
Elahe Ghalebi, Hamidreza Mahyar, Radu Grosu, Graham W. Taylor, Sinead A. Williamson
2019 J jnl
Avoiding Resentment Via Monotonic Fairness.
CoRR
Guy W. Cole, Sinead A. Williamson
2019 J jnl
Embarrassingly Parallel Inference for Gaussian Processes.
J. Mach. Learn. Res.
Michael Minyi Zhang, Sinead A. Williamson
2019 — conf
Importance Weighted Generative Networks.
ECML/PKDD (2)
Maurice Diesendruck, Ethan R. Elenberg, Rajat Sen, Guy W. Cole, Sanjay Shakkottai, Sinead A. Williamson
2019 J jnl
Large-scale Collaborative Filtering with Product Embeddings.
CoRR
Thom Lake, Sinead A. Williamson, Alexander T. Hawk, Christopher C. Johnson, Benjamin P. Wing
2019 A conf
Random Clique Covers for Graphs with Local Density and Global Sparsity.
UAI
Sinead A. Williamson, Mauricio Tec
2019 A* conf
Rethinking Sparsity in Performance Modeling for Analog and Mixed Circuits using Spike and Slab Models.
DAC
Mohamed Baker Alawieh, Sinead A. Williamson, David Z. Pan
2019 J jnl
Sequential Gaussian Processes for Online Learning of Nonstationary Functions.
CoRR
Michael Minyi Zhang, Bianca Dumitrascu, Sinead A. Williamson, Barbara E. Engelhardt
2019 J jnl
Stochastic Blockmodels with Edge Information.
CoRR
Guy W. Cole, Sinead A. Williamson
2018 J jnl
A scalable preference model for autonomous decision-making.
Mach. Learn.
Markus Peters, Maytal Saar-Tsechansky, Wolfgang Ketter, Sinead A. Williamson, Perry Groot, Tom Heskes
2018 J jnl
Importance weighted generative networks.
CoRR
Maurice Diesendruck, Ethan R. Elenberg, Rajat Sen, Guy W. Cole, Sanjay Shakkottai, Sinead A. Williamson
2017 J jnl
Restricted Indian buffet processes.
Stat. Comput.
Finale Doshi-Velez, Sinead A. Williamson
2016 J jnl
Nonparametric Network Models for Link Prediction.
J. Mach. Learn. Res.
Sinead A. Williamson
2016 — conf
Variance Reduction in Stochastic Gradient Langevin Dynamics.
NIPS
Kumar Avinava Dubey, Sashank J. Reddi, Sinead A. Williamson, Barnabás Póczos, Alexander J. Smola, Eric P. Xing
2015 J jnl
A Survey of Non-Exchangeable Priors for Bayesian Nonparametric Models.
IEEE Trans. Pattern Anal. Mach. Intell.
Nicholas J. Foti, Sinead A. Williamson
2014 — conf
Dependent nonparametric trees for dynamic hierarchical clustering.
NIPS
Kumar Avinava Dubey, Qirong Ho, Sinead A. Williamson, Eric P. Xing