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A hybrid sampler for Poisson-Kingman mixture models

2015 · arXiv (Cornell University)

This paper concerns the introduction of a new Markov Chain Monte Carlo scheme for posterior sampling in Bayesian nonparametric mixture models with priors that belong to the general Poisson-Kingman class. We present a novel compact …

Probabilistic Symmetries and Invariant Neural Networks

2020 · Journal of Machine Learning Research

Treating neural network inputs and outputs as random variables, we characterize the structure of neural networks that can be used to model data that are invariant or equivariant under the action of a compact group. …

Bayesian Deep Ensembles via the Neural Tangent Kernel

2020 · arXiv (Cornell University)

We explore the link between deep ensembles and Gaussian processes (GPs) through the lens of the Neural Tangent Kernel (NTK): a recent development in understanding the training dynamics of wide neural networks (NNs). Previous work …

Distributed Bayesian Learning with Stochastic Natural-gradient\n Expectation Propagation and the Posterior Server

2015 · arXiv (Cornell University)

This paper makes two contributions to Bayesian machine learning algorithms.\nFirstly, we propose stochastic natural gradient expectation propagation (SNEP),\na novel alternative to expectation propagation (EP), a popular variational\ninference algorithm. SNEP is a black box variational algorithm, …