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D-Index & Metrics

Computer Science

D-Index
31
Citations
4575
World Ranking
13579
National Ranking
5421

Overview

Maya R. Gupta is affiliated with Google in the United States and has contributed extensively to the field of computer science, particularly focusing on artificial intelligence and related subfields. Their research encompasses a variety of topics including machine learning, ethics in AI, algorithm development, and advanced optimization techniques.

Their recent published papers include:

  • Pairwise Fairness for Ranking and Regression (2020), Proceedings of the AAAI Conference on Artificial Intelligence
  • Robust Optimization for Fairness with Noisy Protected Groups (2020), arXiv (Cornell University)
  • Fast Linear Interpolation (2021), ACM Journal on Emerging Technologies in Computing Systems
  • Barriers to and facilitators of pet grooming among clients served by a subsidized grooming service program (2022), Frontiers in Veterinary Science
  • Deontological Ethics By Monotonicity Shape Constraints (2020), arXiv (Cornell University)

Their frequent collaborators reflect a range of coauthorship in related areas and include:

  • Harikrishna Narasimhan
  • Serena Wang
  • Andrew Cotter
  • Kevin Robert Canini

Gupta's work has appeared primarily in these venues:

  • arXiv (Cornell University)
  • ACM Journal on Emerging Technologies in Computing Systems
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Frontiers in Veterinary Science
  • Bioinformatics

The scientist's main field of study is computer science, with a focus on several subfields:

  • Artificial Intelligence
  • Safety Research
  • Immunology
  • Molecular Biology
  • Management Science and Operations Research

Their research topics cover a broad spectrum of machine learning and ethical implications of AI technologies, including:

  • Machine Learning and Data Classification
  • Ethics and Social Impacts of AI
  • Machine Learning and Algorithms
  • Advanced Bandit Algorithms Research
  • Bayesian Modeling and Causal Inference
  • Machine Learning in Materials Science
  • Medical Imaging Techniques and Applications

Best Publications

  • Recent advances in terahertz imaging

    D.M. Mittleman;M. Gupta;R. Neelamani;R.G. Baraniuk

  • Similarity-based Classification: Concepts and Algorithms

    Yihua Chen;Eric K. Garcia;Maya R. Gupta;Ali Rahimi

  • Theory and Use of the Em Algorithm

    Maya R. Gupta;Yihua Chen

  • To Trust Or Not To Trust A Classifier

    Heinrich Jiang;Been Kim;Melody Y. Guan;Maya R. Gupta

  • OCR binarization and image pre-processing for searching historical documents

    Maya R. Gupta;Nathaniel P. Jacobson;Eric K. Garcia

  • Bayesian Quadratic Discriminant Analysis

    Santosh Srivastava;Maya R. Gupta;Béla A. Frigyik

  • Design goals and solutions for display of hyperspectral images

    N.P. Jacobson;M.R. Gupta

  • How to Analyze Paired Comparison Data

    Kristi Tsukida;Maya R. Gupta

  • Satisfying real-world goals with dataset constraints

    Gabriel Goh;Andrew Cotter;Maya Gupta;Michael Friedlander

  • Monotonic calibrated interpolated look-up tables

    Maya Gupta;Andrew Cotter;Jan Pfeifer;Konstantin Voevodski

  • To Trust Or Not To Trust A Classifier.

    Heinrich Jiang;Been Kim;Melody Y. Guan;Maya Gupta

  • Training highly multiclass classifiers

    Maya R. Gupta;Samy Bengio;Jason Weston

  • Deep Lattice Networks and Partial Monotonic Functions

    Seungil You;David Ding;Kevin Robert Canini;Jan Pfeifer

  • Functional Bregman Divergence and Bayesian Estimation of Distributions

    B.A. Frigyik;S. Srivastava;M.R. Gupta

  • Linear Fusion of Image Sets for Display

    N.P. Jacobson;M.R. Gupta;J.B. Cole

  • Pairwise Fairness for Ranking and Regression.

    Harikrishna Narasimhan;Andy Cotter;Maya Gupta;Serena Lutong Wang

  • Learning kernels from indefinite similarities

    Yihua Chen;Maya R. Gupta;Benjamin Recht

  • Completely Lazy Learning

    Eric K Garcia;Sergey Feldman;Maya R Gupta;Santosh Srivastava

  • Satisfying Real-world Goals with Dataset Constraints

    Gabriel Goh;Andrew Cotter;Maya Gupta;Michael Friedlander

  • Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints

    Andrew Cotter;Maya R. Gupta;Heinrich Jiang;Nathan Srebro

  • Wavelet Principal Component Analysis and its Application to Hyperspectral Images

    Maya Gupta;Nathaniel Jacobson

  • Adaptive Local Linear Regression With Application to Printer Color Management

    M.R. Gupta;E.K. Garcia;E. Chin

  • Optimization with Non-Differentiable Constraints with Applications to Fairness, Recall, Churn, and Other Goals

    Andrew Cotter;Heinrich Jiang;Serena Wang;Taman Narayan

Frequent Co-Authors

Robert M. Gray
Robert M. Gray Stanford University
Karthik Sridharan
Karthik Sridharan Cornell University
Samy Bengio
Samy Bengio Apple (United States)
Been Kim
Been Kim Google (United States)
Michael I. Jordan
Michael I. Jordan University of California, Berkeley
Anna C. Gilbert
Anna C. Gilbert Yale University
Jason Weston
Jason Weston Facebook (United States)
Mari Ostendorf
Mari Ostendorf University of Washington
Richard G. Baraniuk
Richard G. Baraniuk Rice University
Daniel M. Mittleman
Daniel M. Mittleman Brown University

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