Nisarg Shah

Cited by

	All	Since 2016
Citations	1767	1426
h-index	20	18
i10-index	37	34

480

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20122013201420152016201720182019202020216 28 123 161 177 151 200 338 462 95

Co-authors

Ariel ProcacciaGordon McKay Professor of Computer Science, Harvard UniversityVerified email at seas.harvard.edu
Ioannis CaragiannisProfessor, Aarhus UniversityVerified email at cs.au.dk
Rupert FreemanMicrosoft ResearchVerified email at cs.duke.edu
David KurokawaComputer Science PhD Student, Carnegie Mellon UniversityVerified email at cs.cmu.edu
Vincent ConitzerDuke UniversityVerified email at cs.duke.edu
David C. ParkesGeorge F. Colony Professor of Computer Science, Harvard UniversityVerified email at eecs.harvard.edu
Ian KashUniversity of Illinois at ChicagoVerified email at uic.edu
Swaprava NathAssistant Professor, CSE, IIT KanpurVerified email at cse.iitk.ac.in
Herve MoulinUniversity of GlasgowVerified email at glasgow.ac.uk
Yoram BachrachDeepMindVerified email at google.com
Debmalya MandalColumbia UniversityVerified email at columbia.edu
Daniel HalpernHarvard UniversityVerified email at g.harvard.edu
Manas JoglekarPhD, Stanford UniversityVerified email at stanford.edu
Sashank J. ReddiResearch Scientist, Google ResearchVerified email at cs.cmu.edu
Albert X. JiangAssistant Professor of Computer Science, Trinity UniversityVerified email at trinity.edu
Milind TambeDirector CRCS Center @Harvard; Director "AI for Social Good" @Google India ResearchVerified email at harvard.edu
Gerdus BenadeInformation Systems at Boston UniversityVerified email at bu.edu
Safwan HossainMasters Student, University of TorontoVerified email at mail.utoronto.ca
Krishnendu ChatterjeeProfessor, IST AustriaVerified email at ist.ac.at
Chara PodimataHarvard UniversityVerified email at g.harvard.edu

Nisarg Shah

Assistant Professor, University of Toronto

Verified email at cs.toronto.edu - Homepage

Algorithmic Economics Artificial Intelligence Computational Social Choice Algorithmic Fairness


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The Unreasonable Fairness of Maximum Nash Welfare I Caragiannis, D Kurokawa, H Moulin, AD Procaccia, N Shah, J Wang ACM Transactions on Economics and Computation (TEAC) 7 (3), 1-32, 2019	261	2019
Beyond dominant resource fairness: Extensions, limitations, and indivisibilities DC Parkes, AD Procaccia, N Shah ACM Transactions on Economics and Computation (TEAC) 3 (1), 1-22, 2015	215	2015
No agent left behind: Dynamic fair division of multiple resources I Kash, AD Procaccia, N Shah Journal of Artificial Intelligence Research 51, 579-603, 2014	151	2014
When do noisy votes reveal the truth? I Caragiannis, AD Procaccia, N Shah ACM Transactions on Economics and Computation (TEAC) 4 (3), 1-30, 2016	133	2016
A Maximum Likelihood Approach For Selecting Sets of Alternatives AD Procaccia, S Reddy, N Shah Conference on Uncertainty in Artificial Intelligence, 695-704, 2012	78	2012
Preference elicitation for participatory budgeting G Benade, S Nath, AD Procaccia, N Shah Management Science, 2020	68	2020
Subset selection via implicit utilitarian voting I Caragiannis, S Nath, AD Procaccia, N Shah Journal of Artificial Intelligence Research 58, 123-152, 2017	66	2017
Fair Public Decision Making V Conitzer, R Freeman, N Shah Proceedings of the 18th ACM Conference on Electronic Commerce, 629-646, 2017	62	2017
Fair Allocation of Indivisible Public Goods B Fain, K Munagala, N Shah Proceedings of the 2018 ACM Conference on Economics and Computation, 575-592, 2018	50	2018
Diverse randomized agents vote to win AX Jiang, LS Marcolino, AD Procaccia, TW Sandholm, N Shah, M Tambe Carnegie Mellon University, 2014	47	2014
Leximin allocations in the real world D Kurokawa, AD Procaccia, N Shah Proceedings of the Sixteenth ACM Conference on Economics and Computation …, 2015	40	2015
Modal ranking: A uniquely robust voting rule I Caragiannis, A Procaccia, N Shah Proceedings of the AAAI Conference on Artificial Intelligence 28 (1), 2014	39	2014
Strategyproof Linear Regression in High Dimensions Y Chen, C Podimata, AD Procaccia, N Shah Proceedings of the 2018 ACM Conference on Economics and Computation, 9-26, 2018	38	2018
Group Fairness for the Allocation of Indivisible Goods V Conitzer, R Freeman, N Shah, JW Vaughan Proceedings of the AAAI Conference on Artificial Intelligence 33, 1853-1860, 2019	28*	2019
Defender (mis) coordination in security games AX Jiang, AD Procaccia, Y Qian, N Shah, M Tambe Carnegie Mellon University, 2013	28	2013
Voting rules as error-correcting codes AD Procaccia, N Shah, Y Zick Artificial Intelligence 231, 1-16, 2016	27	2016
Peer Prediction with Heterogeneous Users A Agarwal, D Mandal, DC Parkes, N Shah ACM Transactions on Economics and Computation (TEAC) 8 (1), 1-34, 2020	26	2020
Reliability Weighted Voting Games Y Bachrach, N Shah International Symposium on Algorithmic Game Theory (SAGT), 2013	26	2013
Electing the Most Probable Without Eliminating the Irrational: Voting Over Intransitive Domains. E Elkind, N Shah UAI, 182-191, 2014	25*	2014
Truthful univariate estimators I Caragiannis, A Procaccia, N Shah International Conference on Machine Learning, 127-135, 2016	23	2016

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