3 citations · 4 across the 17 of their papers we have counts for
6 papers · 1 filter
Fast Compute for ML Optimization
Nick Polson, Vadim Sokolov
We study optimization for losses that admit a variance-mean scale-mixture representation. Under this representation, each EM iteration is a weighted least squares update in which l…
Bayesian Methods for the Navier-Stokes Equations
Nicholas Polson, Vadim Sokolov
We develop a Bayesian methodology for numerical solution of the incompressible Navier--Stokes equations with quantified uncertainty. The central idea is to treat discretized Navier…
Mixed Integer and Linear Programming
Nick Polson, Vadim Sokolov
In this paper, we design algorithms for Mixed Integer and Linear Programming. By expressing a constrained optimisation as one of simulation from a Boltzmann distribution, we…
Generative Modeling: A Review
Maria Nareklishvili, Nick Polson, Vadim Sokolov
We organize the generative-modeling literature around three classes of generators, corresponding to three distinct inferential tasks: estimating counterfactual outcome distribution…
Generative Bayesian Computation for Maximum Expected Utility
Nick Polson, Fabrizio Ruggeri, Vadim Sokolov
Generative Bayesian Computation (GBC) methods are developed to provide an efficient computational solution for maximum expected utility (MEU). We propose a density-free generative…
Counting Queens
Nick Polson, Vadim Sokolov
Gauss proposed the problem of how to enumerate the number of solutions for placing queens on an chess board, so no two queens attack each other. The N-queen problem…