32 citations · 37 across the 6 of their papers we have counts for
4 papers · 1 filter
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,…