
Combining Human Predictions with Model Probabilities via Confusion Matrices and Calibration
An increasingly common use case for machine learning models is augmentin...
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Automating Data Science: Prospects and Challenges
Given the complexity of typical data science projects and the associated...
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Joint Fairness Model with Applications to Risk Predictions for Underrepresented Populations
Underrepresentation of certain populations, based on gender, race/ethni...
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Variational Beam Search for Online Learning with Distribution Shifts
We consider the problem of online learning in the presence of sudden dis...
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UserDependent Neural Sequence Models for ContinuousTime Event Data
Continuoustime event data are common in applications such as individual...
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Can I Trust My Fairness Metric? Assessing Fairness with Unlabeled Data and Bayesian Inference
We investigate the problem of reliably assessing group fairness when lab...
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Active Bayesian Assessment for BlackBox Classifiers
Recent advances in machine learning have led to increased deployment of ...
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Unifying the Dropout Family Through Structured Shrinkage Priors
Dropout regularization of deep neural networks has been a mysterious yet...
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Mondrian Processes for Flow Cytometry Analysis
Analysis of flow cytometry data is an essential tool for clinical diagno...
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Learning Approximately Objective Priors
Informative Bayesian priors are often difficult to elicit, and when this...
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Bayesian NonHomogeneous Markov Models via PolyaGamma Data Augmentation with Applications to Rainfall Modeling
Discretetime hidden Markov models are a broadly useful class of latent...
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StickBreaking Variational Autoencoders
We extend Stochastic Gradient Variational Bayes to perform posterior inf...
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A Scale Mixture Perspective of Multiplicative Noise in Neural Networks
Corrupting the input and hidden layers of deep neural networks (DNNs) wi...
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Probabilistic Models for Query Approximation with Large Sparse Binary Datasets
Large sparse sets of binary transaction data with millions of records an...
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The AuthorTopic Model for Authors and Documents
We introduce the authortopic model, a generative model for documents th...
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Conditional ChowLiu Tree Structures for Modeling DiscreteValued Vector Time Series
We consider the problem of modeling discretevalued vector time series d...
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Gibbs Sampling for (Coupled) Infinite Mixture Models in the Stick Breaking Representation
Nonparametric Bayesian approaches to clustering, information retrieval, ...
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On Smoothing and Inference for Topic Models
Latent Dirichlet analysis, or topic modeling, is a flexible latent varia...
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Statistical Topic Models for MultiLabel Document Classification
Machine learning approaches to multilabel document classification have ...
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Padhraic Smyth
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Director, UCI Data Science Initiative, Associate Director, Center for Machine Learning and Intelligent Systems, Professor at University of California Irvine, Member of Techical Staff at Jet Propulsion Laboratory from 19881996