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08/29/2023
Mixed Variational Flows for Discrete Variables
Variational flows allow practitioners to learn complex continuous distri...
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07/28/2023
Non-parametric Hypothesis Tests for Distributional Group Symmetry
Symmetry plays a central role in the sciences, machine learning, and sta...
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06/02/2022
Indeterminacy in Latent Variable Models: Characterization and Strong Identifiability
Most modern latent variable and probabilistic generative models, such as...
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06/21/2021
Lossy Compression for Lossless Prediction
Most data is automatically collected and only ever "seen" by algorithms....
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10/08/2020
Uncertainty in Neural Processes
We explore the effects of architecture and training objective choice on ...
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05/01/2020
On the Benefits of Invariance in Neural Networks
Many real world data analysis problems exhibit invariant structure, and ...
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01/18/2019
Probabilistic symmetry and invariant neural networks
In an effort to improve the performance of deep neural networks in data-...
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07/13/2018
Sequential sampling of Gaussian process latent variable models
We consider the problem of inferring a latent function in a probabilisti...
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07/13/2018
Sequential sampling of Gaussian latent variable models
We consider the problem of inferring a latent function in a probabilisti...
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07/09/2018
Sampling and Inference for Beta Neutral-to-the-Left Models of Sparse Networks
Empirical evidence suggests that heavy-tailed degree distributions occur...
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12/19/2016