Andrew Barron

About

Andrew Barron is a professor associated with Yale University, with a focus on statistical research and education. His work encompasses a broad range of topics within statistics, including density estimation, minimax optimality, neural network approximation, predictive density estimation, information theory, high-dimensional regression, penalized likelihoods, and algorithms for inference and optimization. Throughout his career, he has supervised numerous Ph.D. students and contributed to the development of advanced statistical methodologies. His research interests include the theoretical foundations of statistical estimation, the development of efficient algorithms for high-dimensional data analysis, and applications in areas such as finance, communications, and biostatistics. Barron has a notable record of mentoring students who have gone on to prominent academic and industry positions, reflecting his influence in the field of statistics. His work is characterized by a rigorous approach to statistical theory and a commitment to advancing the understanding and application of statistical methods in complex data environments.

Research topics

• Political Science
• Nanotechnology
• Biochemical engineering
• Materials science
• Organic chemistry
• Engineering
• Chemistry

Selected publications

• Asymptotically Minimax Regret by Bayes Mixtures

  arXiv (Cornell University) · 2024-06-25

  preprintOpen accessSenior author

  We study the problems of data compression, gambling and prediction of a sequence $x^n=x_1x_2...x_n$ from an alphabet ${\cal X}$, in terms of regret and expected regret (redundancy) with respect to various smooth families of probability distributions. We evaluate the regret of Bayes mixture distributions compared to maximum likelihood, under the condition that the maximum likelihood estimate is in the interior of the parameter space. For general exponential families (including the non-i.i.d.\ cas…

  PublisherOA PDFDOI

• Breaking ground: ZAP-C3 revolutionises carbon capture and sequestration

  Research Features · 2024-01-01

  articleOpen access1st authorCorresponding
  PublisherDOI

• Log-Concave Coupling for Sampling Neural Net Posteriors

  arXiv (Cornell University) · 2024-07-26

  preprintOpen accessSenior author

  In this work, we present a sampling algorithm for single hidden layer neural networks. This algorithm is built upon a recursive series of Bayesian posteriors using a method we call Greedy Bayes. Sampling of the Bayesian posterior for neuron weight vectors $w$ of dimension $d$ is challenging because of its multimodality. Our algorithm to tackle this problem is based on a coupling of the posterior density for $w$ with an auxiliary random variable $ξ$. The resulting reverse conditional $w|ξ$ of neu…

  PublisherOA PDFDOI

• Improved MDL Estimators Using Fiber Bundle of Local Exponential Families for Non-exponential Families

  arXiv (Cornell University) · 2023-11-07

  preprintOpen access

  Minimum Description Length (MDL) estimators, using two-part codes for universal coding, are analyzed. For general parametric families under certain regularity conditions, we introduce a two-part code whose regret is close to the minimax regret, where regret of a code with respect to a target family M is the difference between the code length of the code and the ideal code length achieved by an element in M. This is a generalization of the result for exponential families by Grünwald. Our code is…

  PublisherOA PDFDOI

Recent grants

• SWNT Amplification: A Route to Specific Structure Nanomanufacturing

  NSF · $120k · 2007–2008

Frequent coauthors

• Enrico Andreoli

  Energy Safety Research Institute

  228 shared

• Simon G. Bott

  205 shared

• Shirin Alexander

  Swansea University

  139 shared

• Alvin Orbaek White

  Swansea University

  118 shared

• Sajad Kiani

  Swansea University

  102 shared

• Louise B. Hamdy

  85 shared

• Marco Taddei

  University of Pisa

  84 shared

• Charles W. Dunnill

  82 shared

Labs

• Andrew Barron's LabPI

  Not provided

Similar researchers at Yale University

• Mark Gersteinh-217
• Huibin Zhouh-55
• Yihong Wuh-37