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Found 5 papers, 2 papers with code
Fat-Tailed Variational Inference with Anisotropic Tail Adaptive Flows
While fat-tailed densities commonly arise as posterior and marginal distributions in robust models and scale mixtures, they present challenges when Gaussian-based variational inference fails to capture tail decay accurately.
Accelerating Metropolis-Hastings with Lightweight Inference Compilation
In order to construct accurate proposers for Metropolis-Hastings Markov Chain Monte Carlo, we integrate ideas from probabilistic graphical models and neural networks in an open-source framework we call Lightweight Inference Compilation (LIC).
Precise expressions for random projections: Low-rank approximation and randomized Newton
It is often desirable to reduce the dimensionality of a large dataset by projecting it onto a low-dimensional subspace.
Exact expressions for double descent and implicit regularization via surrogate random design
We provide the first exact non-asymptotic expressions for double descent of the minimum norm linear estimator.
Bayesian experimental design using regularized determinantal point processes
In experimental design, we are given $n$ vectors in $d$ dimensions, and our goal is to select $k\ll n$ of them to perform expensive measurements, e. g., to obtain labels/responses, for a linear regression task.
