32 citations · 37 across the 6 of their papers we have counts for
6 papers
A Heavy-Tailed Algebra for Probabilistic Programming
Feynman Liang, Liam Hodgkinson, Michael W. Mahoney
Despite the successes of probabilistic models based on passing noise through neural networks, recent work has identified that such methods often fail to capture tail behavior accur…
Fat-Tailed Variational Inference with Anisotropic Tail Adaptive Flows
Feynman Liang, Liam Hodgkinson, Michael W. Mahoney
While fat-tailed densities commonly arise as posterior and marginal distributions in robust models and scale mixtures, they present challenges when Gaussian-based variational infer…
Accelerating Metropolis-Hastings with Lightweight Inference Compilation
Feynman Liang, Nimar Arora, Nazanin Tehrani +3
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-sou…
Precise expressions for random projections: Low-rank approximation and randomized Newton
Michał Dereziński, Feynman Liang, Zhenyu Liao +1
It is often desirable to reduce the dimensionality of a large dataset by projecting it onto a low-dimensional subspace. Matrix sketching has emerged as a powerful technique for per…
Exact expressions for double descent and implicit regularization via surrogate random design
Michał Dereziński, Feynman Liang, Michael W. Mahoney
Double descent refers to the phase transition that is exhibited by the generalization error of unregularized learning models when varying the ratio between the number of parameters…
Bayesian experimental design using regularized determinantal point processes
Michał Dereziński, Feynman Liang, Michael W. Mahoney
In experimental design, we are given vectors in dimensions, and our goal is to select of them to perform expensive measurements, e.g., to obtain labels/responses,…