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05/26/2023
Functional Flow Matching
In this work, we propose Functional Flow Matching (FFM), a function-spac...
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05/15/2023
Capturing Humans' Mental Models of AI: An Item Response Theory Approach
Improving our understanding of how humans perceive AI teammates is an im...
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02/15/2023
Zero-Shot Anomaly Detection without Foundation Models
Anomaly detection (AD) tries to identify data instances that deviate fro...
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02/15/2023
Deep Anomaly Detection under Labeling Budget Constraints
Selecting informative data points for expert feedback can significantly ...
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12/01/2022
Diffusion Generative Models in Infinite Dimensions
Diffusion generative models have recently been applied to domains where ...
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11/15/2022
Probabilistic Querying of Continuous-Time Event Sequences
Continuous-time event sequences, i.e., sequences consisting of continuou...
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10/12/2022
Predictive Querying for Autoregressive Neural Sequence Models
In reasoning about sequential events it is natural to pose probabilistic...
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09/30/2022
Variable-Based Calibration for Machine Learning Classifiers
The deployment of machine learning classifiers in high-stakes domains re...
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06/18/2022
Fair Generalized Linear Models with a Convex Penalty
Despite recent advances in algorithmic fairness, methodologies for achie...
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09/29/2021
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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05/12/2021
Automating Data Science: Prospects and Challenges
Given the complexity of typical data science projects and the associated...
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05/10/2021
Joint Fairness Model with Applications to Risk Predictions for Under-represented Populations
Under-representation of certain populations, based on gender, race/ethni...
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12/15/2020
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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11/06/2020
User-Dependent Neural Sequence Models for Continuous-Time Event Data
Continuous-time event data are common in applications such as individual...
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10/19/2020
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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02/16/2020
Active Bayesian Assessment for Black-Box Classifiers
Recent advances in machine learning have led to increased deployment of ...
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10/09/2018
Unifying the Dropout Family Through Structured Shrinkage Priors
Dropout regularization of deep neural networks has been a mysterious yet...
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11/21/2017
Mondrian Processes for Flow Cytometry Analysis
Analysis of flow cytometry data is an essential tool for clinical diagno...
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04/04/2017
Learning Approximately Objective Priors
Informative Bayesian priors are often difficult to elicit, and when this...
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01/11/2017
Bayesian Non-Homogeneous Markov Models via Polya-Gamma Data Augmentation with Applications to Rainfall Modeling
Discrete-time hidden Markov models are a broadly useful class of latent-...
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05/20/2016
Stick-Breaking Variational Autoencoders
We extend Stochastic Gradient Variational Bayes to perform posterior inf...
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06/10/2015
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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01/16/2013
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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07/11/2012
The Author-Topic Model for Authors and Documents
We introduce the author-topic model, a generative model for documents th...
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07/11/2012
Conditional Chow-Liu Tree Structures for Modeling Discrete-Valued Vector Time Series
We consider the problem of modeling discrete-valued vector time series d...
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06/27/2012
Gibbs Sampling for (Coupled) Infinite Mixture Models in the Stick Breaking Representation
Nonparametric Bayesian approaches to clustering, information retrieval, ...
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05/09/2012
On Smoothing and Inference for Topic Models
Latent Dirichlet analysis, or topic modeling, is a flexible latent varia...
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07/13/2011