Manish Raghavan

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Jon KleinbergProfessor of Computer Science, Cornell UniversityVerified email at cs.cornell.edu
Solon BarocasPrincipal Researcher, Microsoft Research; Adjunct Assistant Professor, Information Science, CornellVerified email at cornell.edu
Sendhil MullainathanUniversity of ChicagoVerified email at chicagobooth.edu
Karen LevyCornell UniversityVerified email at cornell.edu
Geoff PleissColumbia UniversityVerified email at columbia.edu
Felix WuASAPPVerified email at asapp.com
Kilian WeinbergerCornell UniversityVerified email at cornell.edu
Zhiwei Steven WuCarnegie Mellon UniversityVerified email at andrew.cmu.edu
Aleksandrs SlivkinsMicrosoft Research NYCVerified email at microsoft.com
Sigal OrenBen Gurion UniversityVerified email at cs.bgu.ac.il
David G. RobinsonCornell Information Science; UpturnVerified email at robinsonian.com
Rediet AbebeAssistant Professor of EECS, UC Berkeley; Junior Fellow, Harvard Society of FellowsVerified email at eecs.berkeley.edu
Andrew SelbstUCLA School of LawVerified email at law.ucla.edu
Ana-Andreea StoicaGraduate Student, Columbia UniversityVerified email at columbia.edu
Stratis TsirtsisPhD Student, Max Planck Institute for Software SystemsVerified email at mpi-sws.org
Faidra MonachouPh.D. candidate, Stanford UniversityVerified email at stanford.edu
Jennifer Wortman VaughanSenior Principal Researcher, Microsoft Research, New York CityVerified email at microsoft.com
Gourab K. PatroIIT Kharagpur and Leibniz University HannoverVerified email at iitkgp.ac.in
Roland MaioPhD Student at Columbia UniversityVerified email at columbia.edu
Jessie FinocchiaroUniversity of Colorado BoulderVerified email at colorado.edu

Manish Raghavan

Harvard University

Verified email at seas.harvard.edu - Homepage


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Inherent trade-offs in the fair determination of risk scores J Kleinberg, S Mullainathan, M Raghavan Innovations in Theoretical Computer Science 67, 43:1--43:23, 2017	1242	2017
On fairness and calibration G Pleiss, M Raghavan, F Wu, J Kleinberg, KQ Weinberger Advances in neural information processing systems 30, 2017	530	2017
Mitigating bias in algorithmic hiring: Evaluating claims and practices M Raghavan, S Barocas, J Kleinberg, K Levy Proceedings of the 2020 conference on fairness, accountability, and …, 2020	244	2020
Roles for computing in social change R Abebe, S Barocas, J Kleinberg, K Levy, M Raghavan, DG Robinson Proceedings of the 2020 Conference on Fairness, Accountability, and …, 2020	133	2020
The hidden assumptions behind counterfactual explanations and principal reasons S Barocas, AD Selbst, M Raghavan Proceedings of the 2020 Conference on Fairness, Accountability, and …, 2020	115	2020
How do classifiers induce agents to invest effort strategically? J Kleinberg, M Raghavan ACM Transactions on Economics and Computation (TEAC) 8 (4), 1-23, 2020	89	2020
Selection problems in the presence of implicit bias J Kleinberg, M Raghavan 9th Innovations in Theoretical Computer Science Conference (ITCS 2018) 94 …, 2018	56	2018
The Externalities of Exploration and How Data Diversity Helps Exploitation M Raghavan, A Slivkins, JW Vaughan, ZS Wu Conference on Learning Theory, 2018	47	2018
Deduplicating a places database N Dalvi, M Olteanu, M Raghavan, P Bohannon Proceedings of the 23rd international conference on World wide web, 409-418, 2014	31	2014
Planning problems for sophisticated agents with present bias J Kleinberg, S Oren, M Raghavan Proceedings of the 2016 ACM Conference on Economics and Computation, 343-360, 2016	30	2016
Bridging machine learning and mechanism design towards algorithmic fairness J Finocchiaro, R Maio, F Monachou, GK Patro, M Raghavan, AA Stoica, ... Proceedings of the 2021 ACM Conference on Fairness, Accountability, and …, 2021	28	2021
Planning with multiple biases J Kleinberg, S Oren, M Raghavan Proceedings of the 2017 ACM Conference on Economics and Computation, 567-584, 2017	20	2017
Challenges for mitigating bias in algorithmic hiring M Raghavan, S Barocas Brookings. Retrieved February 25, 2020, 2019	10	2019
Greedy algorithm almost dominates in smoothed contextual bandits M Raghavan, A Slivkins, JW Vaughan, ZS Wu arXiv preprint arXiv:2005.10624, 2020	9	2020
Algorithmic monoculture and social welfare J Kleinberg, M Raghavan Proceedings of the National Academy of Sciences 118 (22), e2018340118, 2021	8	2021
Fairness on the ground: Applying algorithmic fairness approaches to production systems C Bakalar, R Barreto, S Bergman, M Bogen, B Chern, S Corbett-Davies, ... arXiv preprint arXiv:2103.06172, 2021	8	2021
Hiring Under Uncertainty M Purohit, S Gollapudi, M Raghavan International Conference on Machine Learning, 5181-5189, 2019	5	2019
Mapping the invocation structure of online political interaction M Raghavan, A Anderson, J Kleinberg Proceedings of the 2018 World Wide Web Conference, 629-638, 2018	3	2018
The Challenge of Understanding What Users Want: Inconsistent Preferences and Engagement Optimization J Kleinberg, S Mullainathan, M Raghavan arXiv preprint arXiv:2202.11776, 2022	1	2022
Stochastic model for sunk cost bias J Kleinberg, S Oren, M Raghavan, N Sklar Uncertainty in Artificial Intelligence, 1279-1288, 2021	1	2021

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