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Engineering and Technology

D-Index
36
Citations
4619
World Ranking
8772
National Ranking
2431

Overview

Clayton Scott is affiliated with the University of Michigan-Ann Arbor in the United States. Their research primarily lies within the field of Computer Science, with a strong focus on Artificial Intelligence. They have contributed extensively to subfields including Radiation, Statistics and Probability, Computer Vision and Pattern Recognition, and Radiological and Ultrasound Technology.

The scientist's work covers a range of topics that include Nuclear Physics and Applications, Radiation Detection and Scintillator Technologies, Statistical Methods and Inference, Machine Learning and Algorithms, Radioactivity and Radon Measurements, Machine Learning and Data Classification, and Face and Expression Recognition.

Clayton Scott has published numerous research papers in notable venues. These include:

  • Hybrid Stem Cell States: Insights Into the Relationship Between Mammary Development and Breast Cancer Using Single-Cell Transcriptomics, 2020, Frontiers in Cell and Developmental Biology
  • Consistent Estimation of Identifiable Nonparametric Mixture Models from Grouped Observations, 2020, arXiv (Cornell University)
  • Learning from Label Proportions: A Mutual Contamination Framework, 2020, arXiv (Cornell University)
  • The Development of a Feature-Driven Analytical Approach for Gamma-Ray Spectral Analysis, 2024, Annals of Nuclear Energy
  • Calibrated Surrogate Losses for Adversarially Robust Classification, 2020, arXiv (Cornell University)

Their frequent coauthors comprise Yilun Zhu, Darren E. Holland, Azaree T. Lintereur, Jianxin Zhang, and Yutong Wang.

Clayton Scott's work appears predominantly in the following publication venues:

  • arXiv (Cornell University)
  • Annals of Nuclear Energy
  • Frontiers in Cell and Developmental Biology
  • Journal of Nuclear Engineering
  • Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment

Best Publications

  • Robust kernel density estimation

    JooSeuk Kim;Clayton D. Scott

  • Generalizing from Several Related Classification Tasks to a New Unlabeled Sample

    Gilles Blanchard;Gyemin Lee;Clayton Scott

  • Semi-Supervised Novelty Detection

    Gilles Blanchard;Gyemin Lee;Clayton Scott

  • Classification with Asymmetric Label Noise: Consistency and Maximal Denoising

    Clayton Scott;Gilles Blanchard;Gregory Handy

  • A Neyman-Pearson approach to statistical learning

    C. Scott;R. Nowak

  • Learning Minimum Volume Sets

    Clayton D. Scott;Robert D. Nowak

  • EM algorithms for multivariate Gaussian mixture models with truncated and censored data

    Gyemin Lee;Clayton Scott

  • Domain Generalization by Marginal Transfer Learning

    Gilles Blanchard;Aniket Anand Deshmukh;Urun Dogan;Gyemin Lee

  • Minimax-optimal classification with dyadic decision trees

    C. Scott;R.D. Nowak

  • A rate of convergence for mixture proportion estimation, with application to learning from noisy labels

    Clayton Scott

  • Robust contour matching via the order-preserving assignment problem

    C. Scott;R. Nowak

  • Mixture proportion estimation via kernel embedding of distributions

    Harish G. Ramaswamy;Clayton Scott;Ambuj Tewari

  • Mean Values of Dedekind Sums

    J.B. Conrey;Eric Fransen;Robert Klein;Clayton Scott

  • Performance Measures for Neyman–Pearson Classification

    C. Scott

  • Calibrated asymmetric surrogate losses

    Clayton Scott

  • Distributed Spatial Anomaly Detection

    P. Chhabra;C. Scott;E.D. Kolaczyk;M. Crovella

  • Tuning Support Vector Machines for Minimax and Neyman-Pearson Classification

    M A Davenport;R G Baraniuk;C D Scott

  • The value of defibrillator electrograms for recognition of clinical ventricular tachycardias and for pace mapping of post-infarction ventricular tachycardia.

    Kentaro Yoshida;Tzu Yu Liu;Clayton Scott;Alfred Hero

  • ADAPTIVE HAUSDORFF ESTIMATION OF DENSITY LEVEL SETS

    Aarti Singh;Clayton Scott;Robert Nowak

  • Novelty detection: Unlabeled data definitely help

    Clayton Scott;Gilles Blanchard

  • Classification with asymmetric label noise: Consistency and maximal denoising

    Gilles Blanchard;Marek Flaska;Gregory Handy;Sara Pozzi

Frequent Co-Authors

Robert Nowak
Robert Nowak University of Wisconsin–Madison
Jeffrey A. Fessler
Jeffrey A. Fessler University of Michigan–Ann Arbor
Richard G. Baraniuk
Richard G. Baraniuk Rice University
Mark A. Davenport
Mark A. Davenport Georgia Institute of Technology
Chandra Sripada
Chandra Sripada University of Michigan–Ann Arbor
Mike Angstadt
Mike Angstadt University of Michigan–Ann Arbor
Rebecca Willett
Rebecca Willett University of Chicago
Mark B. Moldwin
Mark B. Moldwin University of Michigan–Ann Arbor
Aarti Singh
Aarti Singh Carnegie Mellon University
Alfred O. Hero
Alfred O. Hero University of Michigan–Ann Arbor

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