
ANOVA exemplars for understanding data drift
The distributions underlying complex datasets, such as images, text or t...
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Distributed, partially collapsed MCMC for Bayesian Nonparametrics
Bayesian nonparametric (BNP) models provide elegant methods for discover...
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A Nonparametric Bayesian Model for Sparse Temporal Multigraphs
As the availability and importance of temporal interaction data–such as ...
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Avoiding Resentment Via Monotonic Fairness
Classifiers that achieve demographic balance by explicitly using protect...
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Sequential Gaussian Processes for Online Learning of Nonstationary Functions
Many machine learning problems can be framed in the context of estimatin...
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A New Class of Time Dependent Latent Factor Models with Applications
In many applications, observed data are influenced by some combination o...
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Stochastic Blockmodels with Edge Information
Stochastic blockmodels allow us to represent networks in terms of a late...
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Largescale Collaborative Filtering with Product Embeddings
The application of machine learning techniques to largescale personaliz...
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Random clique covers for graphs with local density and global sparsity
Large realworld graphs tend to be sparse, but they often contain densel...
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Importance weighted generative networks
Deep generative networks can simulate from a complex target distribution...
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Parallel Markov Chain Monte Carlo for the Indian Buffet Process
Indian Buffet Process based models are an elegant way for discovering un...
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Embarrassingly parallel inference for Gaussian processes
Training Gaussian process (GP)based models typically involves an O(N^3...
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Exact and Efficient Parallel Inference for Nonparametric Mixture Models
Nonparametric mixture models based on the Dirichlet process are an elega...
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Sinead A. Williamson
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