1 citations · 2 across the 24 of their papers we have counts for
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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…