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07/18/2022
Deeply-Learned Generalized Linear Models with Missing Data
Deep Learning (DL) methods have dramatically increased in popularity in ...
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06/30/2022
Distribution-based Sketching of Single-Cell Samples
Modern high-throughput single-cell immune profiling technologies, such a...
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01/28/2022
Any Variational Autoencoder Can Do Arbitrary Conditioning
Arbitrary conditioning is an important problem in unsupervised learning,...
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02/08/2021
Arbitrary Conditional Distributions with Energy
Modeling distributions of covariates, or density estimation, is a core c...
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02/05/2021
NRTSI: Non-Recurrent Time Series Imputation for Irregularly-sampled Data
Time series imputation is a fundamental task for understanding time seri...
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01/18/2021
Handling Non-ignorably Missing Features in Electronic Health Records Data Using Importance-Weighted Autoencoders
Electronic Health Records (EHRs) are commonly used to investigate relati...
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06/07/2020
Deep Goal-Oriented Clustering
Clustering and prediction are two primary tasks in the fields of unsuper...
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09/13/2019
Flow Models for Arbitrary Conditional Likelihoods
Understanding the dependencies among features of a dataset is at the cor...
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02/09/2019
Meta-Curvature
We propose to learn curvature information for better generalization and ...
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01/30/2018
Transformation Autoregressive Networks
The fundamental task of general density estimation has been of keen inte...
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05/30/2017
Recurrent Estimation of Distributions
This paper presents the recurrent estimation of distributions (RED) for ...
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03/01/2017
The Statistical Recurrent Unit
Sophisticated gated recurrent neural network architectures like LSTMs an...
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03/20/2016
Multi-fidelity Gaussian Process Bandit Optimisation
In many scientific and engineering applications, we are tasked with the ...
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11/13/2015
Deep Mean Maps
The use of distributions and high-level features from deep architecture ...
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09/24/2015