3 citations · 4 across the 17 of their papers we have counts for
18 papers
Bayes, E-values and Testing
Nicholas G. Polson, Vadim Sokolov, Daniel Zantedeschi
E-values and E-processes (nonnegative supermartingales) provide anytime-valid evidence for sequential testing via Ville's inequality, yet their connection to Bayesian reasoning, re…
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…
Horseshoe Mixtures-of-Experts (HS-MoE)
Nick Polson, Vadim Sokolov
Horseshoe mixtures-of-experts (HS-MoE) models provide a Bayesian framework for sparse expert selection in mixture-of-experts architectures. We combine the horseshoe prior's adaptiv…
Conformal Prediction = Bayes?
Jyotishka Datta, Nicholas G. Polson, Vadim Sokolov +1
Conformal prediction (CP) is widely presented as distribution-free predictive inference with finite-sample marginal coverage under exchangeability. We argue that CP is best underst…
Generative Bayesian Hyperparameter Tuning
Hedibert Lopes, Nick Polson, Vadim Sokolov
\noindent Hyper-parameter selection is a central practical problem in modern machine learning, governing regularization strength, model capacity, and robustness choices. Cross-vali…
Bayesian Global-Local Regularization
Jyotishka Datta, Nick Polson, Vadim Sokolov
We propose a unified framework for global-local regularization that bridges the gap between classical techniques -- such as ridge regression and the nonnegative garotte -- and mode…
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 Quantile Bayesian Prediction
Maria Nareklishvili, Nick Polson, Vadim Sokolov
Prediction is a central task of machine learning. Our goal is to solve large scale prediction problems using Generative Quantile Bayesian Prediction (GQBP).By directly learning pre…
Bayesian Double Descent
Nick Polson, Vadim Sokolov
Double descent is a phenomenon of over-parameterized statistical models such as deep neural networks which have a re-descending property in their risk function. As the complexity o…
Generative Learner for Distributional Causal Effects
Maria Nareklishvili, Nicholas Polson, Vadim Sokolov
We propose a generative learner for estimating conditional average treatment effects and characterizing the full distribution of these effects. The learner takes the form of a mult…
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…
Kramnik vs Nakamura: A Chess Scandal
Shiva Maharaj, Nick Polson, Vadim Sokolov
We provide a statistical analysis of the recent controversy between Vladimir Kramnik (ex chess world champion) and Hikaru Nakamura. Hikaru Nakamura is a chess prodigy and a five-ti…
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…
Negative Probability
Nick Polson, Vadim Sokolov
Negative probabilities arise primarily in physics, statistical quantum mechanics and quantum computing. Negative probabilities arise as mixing distributions of unobserved latent va…
The Value of Chess Squares
Aditya Gupta, Shiva Maharaj, Nicholas Polson +1
We propose a neural network-based approach to calculate the value of a chess square-piece combination. Our model takes a triplet (Color, Piece, Square) as an input and calculates a…
On the Probability of Magnus Carlsen reaching 2900
Sohan Bendre, Shiva Maharaj, Nick Polson +1
How likely is it that Magnus Carlsen will achieve an Elo rating of ? This has been a goal of Magnus and is of great current interest to the chess community. Our paper uses pr…