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On computable learning of continuous features
We introduce definitions of computable PAC learning for binary classification over computable metric spaces.
Deep Involutive Generative Models for Neural MCMC
We introduce deep involutive generative models, a new architecture for deep generative modeling, and use them to define Involutive Neural MCMC, a new approach to fast neural MCMC.
Priors on exchangeable directed graphs
Directed graphs occur throughout statistical modeling of networks, and exchangeability is a natural assumption when the ordering of vertices does not matter.
An iterative step-function estimator for graphons
Exchangeable graphs arise via a sampling procedure from measurable functions known as graphons.
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