D-Index & Metrics Best Publications

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Computer Science D-index 34 Citations 10,300 78 World Ranking 6112 National Ranking 2941

Research.com Recognitions

Awards & Achievements

2015 - Fellow of Alfred P. Sloan Foundation

Overview

What is he best known for?

The fields of study he is best known for:

  • Statistics
  • Algebra
  • Algorithm

The scientist’s investigation covers issues in Differential privacy, Theoretical computer science, Machine learning, Artificial intelligence and Mathematical economics. His Differential privacy study deals with the bigger picture of Algorithm. The study incorporates disciplines such as Learning theory, Classifier, Time complexity, Synthetic data and Computational problem in addition to Theoretical computer science.

The concepts of his Machine learning study are interwoven with issues in Field and Statistical inference. His study looks at the relationship between Mathematical economics and fields such as Regret, as well as how they intersect with chemical problems. His Privacy software research includes elements of Computational complexity theory and Data stream mining.

His most cited work include:

  • The Algorithmic Foundations of Differential Privacy (2208 citations)
  • A learning theory approach to non-interactive database privacy (495 citations)
  • Fairness in Criminal Justice Risk Assessments: The State of the Art (263 citations)

What are the main themes of his work throughout his whole career to date?

Aaron Roth mainly focuses on Differential privacy, Theoretical computer science, Mathematical optimization, Mathematical economics and Algorithm. His Differential privacy study is associated with Data mining. His work carried out in the field of Theoretical computer science brings together such families of science as Time complexity, Polynomial and Approximation algorithm.

His work on Maximization as part of general Mathematical optimization study is frequently connected to Bounded function, Set and Task, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them. His work is dedicated to discovering how Mathematical economics, Regret are connected with Artificial intelligence and other disciplines. As a member of one scientific family, Aaron Roth mostly works in the field of Mechanism design, focusing on Common value auction and, on occasion, Payment.

He most often published in these fields:

  • Differential privacy (44.69%)
  • Theoretical computer science (24.78%)
  • Mathematical optimization (21.68%)

What were the highlights of his more recent work (between 2018-2021)?

  • Differential privacy (44.69%)
  • Theoretical computer science (24.78%)
  • Algorithm (11.06%)

In recent papers he was focusing on the following fields of study:

Aaron Roth mostly deals with Differential privacy, Theoretical computer science, Algorithm, Generalization and Mathematical optimization. The subject of his Differential privacy research is within the realm of Data mining. His work deals with themes such as Statistic and Synthetic data, which intersect with Data mining.

Aaron Roth has researched Theoretical computer science in several fields, including Interactivity, Mathematical proof, SIMPLE and Benchmark. His research integrates issues of Group, State, Minimax, Confidence interval and Point estimation in his study of Algorithm. As part of the same scientific family, he usually focuses on Mathematical optimization, concentrating on Oracle and intersecting with Regret and Distribution.

Between 2018 and 2021, his most popular works were:

  • Fairness in Criminal Justice Risk Assessments: The State of the Art (263 citations)
  • An Empirical Study of Rich Subgroup Fairness for Machine Learning (58 citations)
  • A snapshot of the frontiers of fairness in machine learning (46 citations)

In his most recent research, the most cited papers focused on:

  • Statistics
  • Algebra
  • Algorithm

His main research concerns Theoretical computer science, Differential privacy, Constraint, Generalization and Sample. His studies in Theoretical computer science integrate themes in fields like Heuristics and Pointer. He undertakes interdisciplinary study in the fields of Differential privacy and Audit through his research.

His study in Constraint is interdisciplinary in nature, drawing from both Regret, Mathematical optimization and Distribution. His Sample research is multidisciplinary, incorporating perspectives in Mathematical economics, Task and Contrast. His Task research is multidisciplinary, incorporating elements of Algorithm and Heuristic.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

The Algorithmic Foundations of Differential Privacy

Cynthia Dwork;Aaron Roth.
(2014)

2482 Citations

A learning theory approach to non-interactive database privacy

Avrim Blum;Katrina Ligett;Aaron Roth.
symposium on the theory of computing (2008)

707 Citations

A learning theory approach to noninteractive database privacy

Avrim Blum;Katrina Ligett;Aaron Roth.
Journal of the ACM (2013)

670 Citations

Fairness in Criminal Justice Risk Assessments: The State of the Art

Richard Berk;Hoda Heidari;Shahin Jabbari;Michael Kearns.
Sociological Methods & Research (2021)

342 Citations

The reusable holdout: Preserving validity in adaptive data analysis

Cynthia Dwork;Vitaly Feldman;Moritz Hardt;Toniann Pitassi.
Science (2015)

280 Citations

Interactive privacy via the median mechanism

Aaron Roth;Tim Roughgarden.
symposium on the theory of computing (2010)

261 Citations

Preserving Statistical Validity in Adaptive Data Analysis

Cynthia Dwork;Vitaly Feldman;Moritz Hardt;Toniann Pitassi.
symposium on the theory of computing (2015)

257 Citations

Differential Privacy: An Economic Method for Choosing Epsilon

Justin Hsu;Marco Gaboardi;Andreas Haeberlen;Sanjeev Khanna.
ieee computer security foundations symposium (2014)

217 Citations

The Frontiers of Fairness in Machine Learning.

Alexandra Chouldechova;Aaron Roth.
arXiv: Learning (2018)

169 Citations

Constrained non-monotone submodular maximization: offline and secretary algorithms

Anupam Gupta;Aaron Roth;Grant Schoenebeck;Kunal Talwar.
workshop on internet and network economics (2010)

164 Citations

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