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10/31/2022
Denoising neural networks for magnetic resonance spectroscopy
In many scientific applications, measured time series are corrupted by n...
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06/24/2020
ANOVA exemplars for understanding data drift
The distributions underlying complex datasets, such as images, text or t...
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01/15/2020
Distributed, partially collapsed MCMC for Bayesian Nonparametrics
Bayesian nonparametric (BNP) models provide elegant methods for discover...
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10/11/2019
A Nonparametric Bayesian Model for Sparse Temporal Multigraphs
As the availability and importance of temporal interaction data–such as ...
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09/03/2019
Avoiding Resentment Via Monotonic Fairness
Classifiers that achieve demographic balance by explicitly using protect...
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05/24/2019
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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04/18/2019
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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04/03/2019
Stochastic Blockmodels with Edge Information
Stochastic blockmodels allow us to represent networks in terms of a late...
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01/11/2019
Large-scale Collaborative Filtering with Product Embeddings
The application of machine learning techniques to large-scale personaliz...
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10/15/2018
Random clique covers for graphs with local density and global sparsity
Large real-world graphs tend to be sparse, but they often contain densel...
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06/07/2018
Importance weighted generative networks
Deep generative networks can simulate from a complex target distribution...
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03/09/2017
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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02/27/2017
Embarrassingly parallel inference for Gaussian processes
Training Gaussian process (GP)-based models typically involves an O(N^3...
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11/29/2012