11 citations · 70 across the 22 of their papers we have counts for
25 papers
Denoising neural networks for magnetic resonance spectroscopy
Natalie Klein, Amber J. Day, Harris Mason +2
In many scientific applications, measured time series are corrupted by noise or distortions. Traditional denoising techniques often fail to recover the signal of interest, particul…
Understanding collections of related datasets using dependent MMD coresets
Sinead A. Williamson, Jette Henderson
Understanding how two datasets differ can help us determine whether one dataset under-represents certain sub-populations, and provides insights into how well models will generalize…
Balance is key: Private median splits yield high-utility random trees
Shorya Consul, Sinead A. Williamson
Random forests are a popular method for classification and regression due to their versatility. However, this flexibility can come at the cost of user privacy, since training rando…
Federating Recommendations Using Differentially Private Prototypes
Mónica Ribero, Jette Henderson, Sinead Williamson +1
Machine learning methods allow us to make recommendations to users in applications across fields including entertainment, dating, and commerce, by exploiting similarities in users'…
Distributed, partially collapsed MCMC for Bayesian Nonparametrics
Avinava Dubey, Michael Minyi Zhang, Eric P. Xing +1
Bayesian nonparametric (BNP) models provide elegant methods for discovering underlying latent features within a data set, but inference in such models can be slow. We exploit the f…
A Nonparametric Bayesian Model for Sparse Dynamic Multigraphs
Elahe Ghalebi, Hamidreza Mahyar, Radu Grosu +2
As the availability and importance of temporal interaction data--such as email communication--increases, it becomes increasingly important to understand the underlying structure th…
Avoiding Resentment Via Monotonic Fairness
Guy W. Cole, Sinead A. Williamson
Classifiers that achieve demographic balance by explicitly using protected attributes such as race or gender are often politically or culturally controversial due to their lack of…
Sequential Edge Clustering in Temporal Multigraphs
Elahe Ghalebi, Hamidreza Mahyar, Radu Grosu +2
Interaction graphs, such as those recording emails between individuals or transactions between institutions, tend to be sparse yet structured, and often grow in an unbounded manner…
Sequential Gaussian Processes for Online Learning of Nonstationary Functions
Michael Minyi Zhang, Bianca Dumitrascu, Sinead A. Williamson +1
Many machine learning problems can be framed in the context of estimating functions, and often these are time-dependent functions that are estimated in real-time as observations ar…
A New Class of Time Dependent Latent Factor Models with Applications
Sinead A. Williamson, Michael Minyi Zhang, Paul Damien
In many applications, observed data are influenced by some combination of latent causes. For example, suppose sensors are placed inside a building to record responses such as tempe…
Stochastic Blockmodels with Edge Information
Guy W. Cole, Sinead A. Williamson
Stochastic blockmodels allow us to represent networks in terms of a latent community structure, often yielding intuitions about the underlying social structure. Typically, this str…
Large-scale Collaborative Filtering with Product Embeddings
Thom Lake, Sinead A. Williamson, Alexander T. Hawk +2
The application of machine learning techniques to large-scale personalized recommendation problems is a challenging task. Such systems must make sense of enormous amounts of implic…
Random clique covers for graphs with local density and global sparsity
Sinead A. Williamson, Mauricio Tec
Large real-world graphs tend to be sparse, but they often contain many densely connected subgraphs and exhibit high clustering coefficients. While recent random graph models can ca…
Scalable Bayesian Nonparametric Clustering and Classification
Yang Ni, Peter Müller, Maurice Diesendruck +3
We develop a scalable multi-step Monte Carlo algorithm for inference under a large class of nonparametric Bayesian models for clustering and classification. Each step is "embarrass…
Importance Weighted Generative Networks
Maurice Diesendruck, Ethan R. Elenberg, Rajat Sen +3
Deep generative networks can simulate from a complex target distribution, by minimizing a loss with respect to samples from that distribution. However, often we do not have direct…
Accelerated Parallel Non-conjugate Sampling for Bayesian Non-parametric Models
Michael Minyi Zhang, Sinead A. Williamson, Fernando Perez-Cruz
Inference of latent feature models in the Bayesian nonparametric setting is generally difficult, especially in high dimensional settings, because it usually requires proposing feat…
Parallel Markov Chain Monte Carlo for the Indian Buffet Process
Michael M. Zhang, Avinava Dubey, Sinead A. Williamson
Indian Buffet Process based models are an elegant way for discovering underlying features within a data set, but inference in such models can be slow. Inferring underlying features…
Embarrassingly Parallel Inference for Gaussian Processes
Michael Minyi Zhang, Sinead A. Williamson
Training Gaussian process-based models typically involves an computational bottleneck due to inverting the covariance matrix. Popular methods for overcoming this matrix i…
Restricted Indian Buffet Processes
Finale Doshi-Velez, Sinead A. Williamson
Latent feature models are a powerful tool for modeling data with globally-shared features. Nonparametric exchangeable models such as the Indian Buffet Process offer modeling flexib…
Exact and Efficient Parallel Inference for Nonparametric Mixture Models
Sinead A. Williamson, Avinava Dubey, Eric P. Xing
Nonparametric mixture models based on the Dirichlet process are an elegant alternative to finite models when the number of underlying components is unknown, but inference in such m…
A survey of non-exchangeable priors for Bayesian nonparametric models
Nicholas J. Foti, Sinead Williamson
Dependent nonparametric processes extend distributions over measures, such as the Dirichlet process and the beta process, to give distributions over collections of measures, typica…
A unifying representation for a class of dependent random measures
Nicholas J. Foti, Joseph D. Futoma, Daniel N. Rockmore +1
We present a general construction for dependent random measures based on thinning Poisson processes on an augmented space. The framework is not restricted to dependent versions of…
Restricting exchangeable nonparametric distributions
Sinead Williamson, Zoubin Ghahramani, Steven N. MacEachern +1
Distributions over exchangeable matrices with infinitely many columns, such as the Indian buffet process, are useful in constructing nonparametric latent variable models. However,…
A non-parametric mixture model for topic modeling over time
Avinava Dubey, Ahmed Hefny, Sinead Williamson +1
A single, stationary topic model such as latent Dirichlet allocation is inappropriate for modeling corpora that span long time periods, as the popularity of topics is likely to cha…