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Computer Science

D-Index
56
Citations
16497
World Ranking
3995
National Ranking
1902

Research.com Recognitions

  • 2015 - Fellow of Alfred P. Sloan Foundation

Overview

Aaron Roth is affiliated with the University of Pennsylvania in the United States. Their research primarily spans the field of Computer Science, with a significant focus on Artificial Intelligence and related subfields. Their work also intersects with Management Science and Operations Research, Economics and Econometrics, Sociology and Political Science, and Computational Theory and Mathematics.

Roth's recent published papers include:

  • A snapshot of the frontiers of fairness in machine learning, 2020, Communications of the ACM
  • The Ethical Algorithm: The Science of Socially Aware Algorithm Design, 2021, Perspectives on Science and Christian Faith
  • Descent-to-Delete: Gradient-Based Methods for Machine Unlearning, 2020, arXiv (Cornell University)
  • Adaptive Machine Unlearning, 2021, arXiv (Cornell University)
  • Local Differential Privacy for Evolving Data, 2020, Journal of Privacy and Confidentiality

They have collaborated frequently with several researchers, including Michael Kearns, Zhiwei Steven Wu, Natalie Collina, Saeed Sharifi-Malvajerdi, and Georgy Noarov.

Roth's work has been published extensively in various venues, some of the most frequent being:

  • arXiv (Cornell University)
  • Leibniz-Zentrum für Informatik (Schloss Dagstuhl)
  • 2022 ACM Conference on Fairness, Accountability, and Transparency
  • Proceedings of the National Academy of Sciences
  • Journal of the Royal Statistical Society Series B (Statistical Methodology)

Their research topics include:

  • Privacy-Preserving Technologies in Data
  • Adversarial Robustness in Machine Learning
  • Advanced Bandit Algorithms Research
  • Machine Learning and Algorithms
  • Stochastic Gradient Optimization Techniques
  • Cryptography and Data Security
  • Explainable Artificial Intelligence (XAI)

Aaron Roth has been recognized as a Fellow of the Alfred P. Sloan Foundation in 2015.

Best Publications

  • The Algorithmic Foundations of Differential Privacy

    Cynthia Dwork;Aaron Roth

  • A learning theory approach to noninteractive database privacy

    Avrim Blum;Katrina Ligett;Aaron Roth

  • A learning theory approach to non-interactive database privacy

    Avrim Blum;Katrina Ligett;Aaron Roth

  • Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness

    Michael J. Kearns;Seth Neel;Aaron Roth;Zhiwei Steven Wu

  • Selling privacy at auction

    Arpita Ghosh;Aaron Roth

  • The reusable holdout: Preserving validity in adaptive data analysis

    Cynthia Dwork;Vitaly Feldman;Moritz Hardt;Toniann Pitassi

  • Fairness in learning: classic and contextual bandits

    Matthew Joseph;Michael Kearns;Jamie Morgenstern;Aaron Roth

  • Preserving Statistical Validity in Adaptive Data Analysis

    Cynthia Dwork;Vitaly Feldman;Moritz Hardt;Toniann Pitassi

  • A snapshot of the frontiers of fairness in machine learning

    Alexandra Chouldechova;Aaron Roth

  • Differential Privacy: An Economic Method for Choosing Epsilon

    Justin Hsu;Marco Gaboardi;Andreas Haeberlen;Sanjeev Khanna

  • The Frontiers of Fairness in Machine Learning

    Alexandra Chouldechova;Aaron Roth

  • Interactive privacy via the median mechanism

    Aaron Roth;Tim Roughgarden

  • Privately Releasing Conjunctions and the Statistical Query Barrier

    Anupam Gupta;Moritz Hardt;Aaron Roth;Jonathan R. Ullman

  • A Convex Framework for Fair Regression

    Richard Berk;Hoda Heidari;Shahin Jabbari;Matthew Joseph

  • Constrained non-monotone submodular maximization: offline and secretary algorithms

    Anupam Gupta;Aaron Roth;Grant Schoenebeck;Kunal Talwar

  • Iterative constructions and private data release

    Anupam Gupta;Aaron Roth;Jonathan Ullman

  • Generalization in adaptive data analysis and holdout reuse

    Cynthia Dwork;Vitaly Feldman;Moritz Hardt;Toniann Pitassi

  • Regret minimization and the price of total anarchy

    Avrim Blum;MohammadTaghi Hajiaghayi;Katrina Ligett;Aaron Roth

  • Differentially private combinatorial optimization

    Anupam Gupta;Katrina Ligett;Frank McSherry;Aaron Roth

  • An Empirical Study of Rich Subgroup Fairness for Machine Learning

    Michael Kearns;Seth Neel;Aaron Roth;Zhiwei Steven Wu

Frequent Co-Authors

Michael Kearns
Michael Kearns University of Pennsylvania
Jonathan Ullman
Jonathan Ullman Northeastern University
Sampath Kannan
Sampath Kannan University of Pennsylvania
Anupam Gupta
Anupam Gupta Carnegie Mellon University
Moritz Hardt
Moritz Hardt Max Planck Institute for Intelligent Systems
Cynthia Dwork
Cynthia Dwork Harvard University
Kunal Talwar
Kunal Talwar Apple (United States)
Rakesh Vohra
Rakesh Vohra University of Pennsylvania
Omer Reingold
Omer Reingold Stanford University
Andreas Haeberlen
Andreas Haeberlen University of Pennsylvania

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