22 citations · 40 across the 7 of their papers we have counts for
11 papers
Efficient Guided Generation for Large Language Models
Brandon T. Willard, Rémi Louf
In this article we show how the problem of neural text generation can be constructively reformulated in terms of transitions between the states of a finite-state machine. This fram…
miniKanren as a Tool for Symbolic Computation in Python
Brandon T. Willard
In this article, we give a brief overview of the current state and future potential of symbolic computation within the Python statistical modeling and machine learning community. W…
Lasso Meets Horseshoe : A Survey
Anindya Bhadra, Jyotishka Datta, Nicholas G. Polson +1
The goal of this paper is to contrast and survey the major advances in two of the most commonly used high-dimensional techniques, namely, the Lasso and horseshoe regularization. La…
Horseshoe Regularization for Feature Subset Selection
Anindya Bhadra, Jyotishka Datta, Nicholas G. Polson +1
Feature subset selection arises in many high-dimensional applications of statistics, such as compressed sensing and genomics. The penalty is ideal for this task, the cavea…
Prediction risk for the horseshoe regression
Anindya Bhadra, Jyotishka Datta, Yunfan Li +2
We show that prediction performance for global-local shrinkage regression can overcome two major difficulties of global shrinkage regression: (i) the amount of relative shrinkage i…
Global-Local Mixtures
Anindya Bhadra, Jyotishka Datta, Nicholas G. Polson +1
Global-local mixtures are derived from the Cauchy-Schlomilch and Liouville integral transformation identities. We characterize well-known normal-scale mixture distributions includi…
Default Bayesian analysis with global-local shrinkage priors
Anindya Bhadra, Jyotishka Datta, Nicholas G. Polson +1
We provide a framework for assessing the default nature of a prior distribution using the property of regular variation, which we study for global-local shrinkage priors. In partic…
A Statistical Theory of Deep Learning via Proximal Splitting
Nicholas G. Polson, Brandon T. Willard, Massoud Heidari
In this paper we develop a statistical theory and an implementation of deep learning models. We show that an elegant variable splitting scheme for the alternating direction method…
Proximal Algorithms in Statistics and Machine Learning
Nicholas G. Polson, James G. Scott, Brandon T. Willard
In this paper we develop proximal methods for statistical learning. Proximal point algorithms are useful in statistics and machine learning for obtaining optimization solutions for…
The Horseshoe+ Estimator of Ultra-Sparse Signals
Anindya Bhadra, Jyotishka Datta, Nicholas G. Polson +1
We propose a new prior for ultra-sparse signal detection that we term the "horseshoe+ prior." The horseshoe+ prior is a natural extension of the horseshoe prior that has achieved s…
Real-time On and Off Road GPS Tracking
Brandon Willard
This document describes a GPS-based tracking model for position and velocity states on and off of a road network and it enables parallel, online learning of state-dependent paramet…