Sushant Agarwal

202020212022202320242 8 51 71 21

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Shai Ben-DavidUniverity of Waterloo, CanadaVerified email at uwaterloo.ca
Nivasini AnanthakrishnanUC BerkeleyVerified email at berkeley.edu
Tosca LechnerPhD student, University of WaterlooVerified email at uwaterloo.ca
Ruth UrnerAssociate Professor, York University, TorontoVerified email at eecs.yorku.ca
Shahin JabbariDrexel UniversityVerified email at drexel.edu
Zhiwei Steven WuCarnegie Mellon UniversityVerified email at andrew.cmu.edu
Chirag AgarwalPostdoctoral Research Fellow, HarvardVerified email at hbs.edu
Sohini UpadhyayHarvard UniversityVerified email at g.harvard.edu
Himabindu LakkarajuAssistant Professor, Harvard UniversityVerified email at seas.harvard.edu
Amit DeshpandeMicrosoft ResearchVerified email at microsoft.com

Sushant Agarwal

Northeastern University

Verified email at northeastern.edu - Homepage


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Towards the unification and robustness of perturbation and gradient based explanations S Agarwal, S Jabbari, C Agarwal, S Upadhyay, S Wu, H Lakkaraju International Conference on Machine Learning, 110-119, 2021	48	2021
Trade-offs between fairness and privacy in machine learning S Agarwal IJCAI 2021 Workshop on AI for Social Good, 2021	26	2021
On Trade-Offs between Fairness, Interpretability, and Privacy in Classification S Agarwal University of Waterloo, 2020	20*	2020
On Learnability with Computable Learners S Agarwal, N Ananthakrishnan, S Ben-David, T Lechner, R Urner Algorithmic Learning Theory, 48-60, 2020	20	2020
Trade-offs between fairness and interpretability in machine learning S Agarwal IJCAI 2021 Workshop on AI for Social Good, 1-6, 2021	17	2021
Impossibility Results for Fair Representations T Lechner, S Ben-David, S Agarwal, N Ananthakrishnan arXiv preprint arXiv:2107.03483, 2021	11	2021
On the Power of Randomization in Fair Classification and Representation S Agarwal, A Deshpande 2022 ACM Conference on Fairness, Accountability, and Transparency, 1542-1551, 2022	6	2022
Open Problem: Are all VC-classes CPAC Learnable? S Agarwal, N Ananthakrishnan, S Ben-David, T Lechner, R Urner Conference on Learning Theory, 4636-4641, 2021	6	2021

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