World's Best Scientists 2026 revealed!
Andrew Gordon Wilson

Andrew Gordon Wilson

D-Index & Metrics

Computer Science

D-Index
46
Citations
9522
World Ranking
6797
National Ranking
2990

Overview

Andrew Gordon Wilson is affiliated with New York University in the United States. Their research primarily falls within the field of Computer Science, with a focus on several subfields and topics related to machine learning and data analysis.

The scientist's extensive work in Artificial Intelligence encompasses 110 publications. They have also contributed to fields such as Computer Vision and Pattern Recognition, Materials Chemistry, Molecular Biology, and Computational Theory and Mathematics.

The main topics covered in their research include:

  • Gaussian Processes and Bayesian Inference
  • Machine Learning and Data Classification
  • Domain Adaptation and Few-Shot Learning
  • Model Reduction and Neural Networks
  • Machine Learning in Materials Science
  • Neural Networks and Applications
  • Generative Adversarial Networks and Image Synthesis

Their recent papers demonstrate involvement in several key areas. Notable publications include:

  • "Bayesian Deep Learning and a Probabilistic Perspective of Generalization" (2020), published in arXiv (Cornell University)
  • "A Cookbook of Self-Supervised Learning" (2023), published in arXiv (Cornell University)
  • "Large Language Models Are Zero-Shot Time Series Forecasters" (2023), published in arXiv (Cornell University)
  • "Generalizing Convolutional Neural Networks for Equivariance to Lie Groups on Arbitrary Continuous Data" (2020), published in arXiv (Cornell University)
  • "What Are Bayesian Neural Network Posteriors Really Like?" (2021), published in arXiv (Cornell University)

Andrew Gordon Wilson has frequently published in arXiv (Cornell University), with a total of 83 publications there. Other venues where the scientist has contributed include Field Robotics, Proceedings of the National Academy of Sciences, Monthly Notices of the Royal Astronomical Society, and American Journal of Critical Care.

The scientist has collaborated extensively with several coauthors, including:

  • Micah Goldblum (21 collaborations)
  • Marc Finzi (20 collaborations)
  • Wesley J. Maddox (15 collaborations)
  • Pavel Izmailov (14 collaborations)
  • Nate Gruver (9 collaborations)

Best Publications

  • Averaging Weights Leads to Wider Optima and Better Generalization

    Pavel Izmailov;Dmitrii Podoprikhin;Dmitrii Podoprikhin;Timur Garipov;Timur Garipov;Dmitry P. Vetrov;Dmitry P. Vetrov

  • GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration.

    Jacob R. Gardner;Geoff Pleiss;David Bindel;Kilian Q. Weinberger

  • Gaussian Process Kernels for Pattern Discovery and Extrapolation

    Andrew Wilson;Ryan Adams

  • Deep Kernel Learning

    Andrew Gordon Wilson;Zhiting Hu;Ruslan Salakhutdinov;Eric P. Xing

  • BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization

    Maximilian Balandat;Brian Karrer;Daniel R. Jiang;Samuel Daulton

  • A Simple Baseline for Bayesian Uncertainty in Deep Learning

    Wesley J. Maddox;Pavel Izmailov;Timur Garipov;Dmitry P. Vetrov;Dmitry P. Vetrov

  • Kernel Interpolation for Scalable Structured Gaussian Processes (KISS-GP)

    Andrew Wilson;Hannes Nickisch

  • Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs

    Timur Garipov;Timur Garipov;Pavel Izmailov;Dmitrii Podoprikhin;Dmitry P. Vetrov

  • GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration

    Jacob R. Gardner;Geoff Pleiss;Kilian Q. Weinberger;David Bindel

  • Simple Black-box Adversarial Attacks

    Chuan Guo;Jacob R. Gardner;Yurong You;Andrew Gordon Wilson

  • A Simple Baseline for Bayesian Uncertainty in Deep Learning

    Wesley Maddox;Timur Garipov;Pavel Izmailov;Dmitry Vetrov

  • Bayesian Deep Learning and a Probabilistic Perspective of Generalization

    Andrew Gordon Wilson;Pavel Izmailov

  • There Are Many Consistent Explanations of Unlabeled Data: Why You Should Average.

    Ben Athiwaratkun;Marc Finzi;Pavel Izmailov;Andrew Gordon Wilson

  • {Student-t Processes as Alternatives to Gaussian Processes}

    Amar Shah;Andrew Gordon Wilson;Zoubin Ghahramani

  • Stochastic variational deep kernel learning

    Andrew Gordon Wilson;Zhiting Hu;Ruslan Salakhutdinov;Eric P. Xing

  • Gaussian Process Regression Networks

    Andrew Wilson;Zoubin Ghahramani;David A. Knowles

  • Bayesian Optimization with Gradients

    Jian Wu;Matthias Poloczek;Andrew Gordon Wilson;Peter I. Frazier

  • Fast Kernel Learning for Multidimensional Pattern Extrapolation

    Andrew Wilson;Elad Gilboa;John P Cunningham;Arye Nehorai

  • Probabilistic fasttext for multi-sense word embeddings

    Ben Athiwaratkun;Andrew Gordon Wilson;Anima Anandkumar

  • A la carte | learning fast kernels

    Zichao Yang;Andrew Gordon Wilson;Alexander J. Smola;Le Song

  • Exact Gaussian processes on a million data points

    Ke Alexander Wang;Geoff Pleiss;Jacob R. Gardner;Stephen Tyree

  • Proceedings for 20th Annual Conference on Neural Information Processing Systems (NIPS)

    Andrew Wilson;Christoph Dann;Christopher Lucas;Eric P. Xing

Frequent Co-Authors

Eric P. Xing
Eric P. Xing Mohamed bin Zayed University of Artificial Intelligence
Kilian Q. Weinberger
Kilian Q. Weinberger Cornell University
Zoubin Ghahramani
Zoubin Ghahramani University of Cambridge
Zhiting Hu
Zhiting Hu University of California, San Diego
Alexander J. Smola
Alexander J. Smola Amazon (United States)
Peter I. Frazier
Peter I. Frazier Cornell University
Ruslan Salakhutdinov
Ruslan Salakhutdinov Carnegie Mellon University
Ryan P. Adams
Ryan P. Adams Princeton University
Barnabás Póczos
Barnabás Póczos Carnegie Mellon University
Arye Nehorai
Arye Nehorai Washington University in St. Louis

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