Publications

Manuscripts


M8
Market-Based Explanations of Collective Decisions.
By Dominik Peters, Grzegorz Pierczyński, Nisarg Shah, and Piotr Skowron.
[Manuscript]

M7
Aggregating Binary Judgments Ranked By Accuracy.
By Daniel Halpern, Greg Kehne, Dominik Peters, Ariel D. Procaccia, Nisarg Shah, and Piotr Skowron.
[Manuscript]

M6
Two-Sided Matching Meets Fair Division.
By Rupert Freeman, Evi Micha, and Nisarg Shah.
[Manuscript]

M5
Distortion in Fair Division.
By Daniel Halpern and Nisarg Shah.
[Manuscript]

M4
Fair Algorithms for Multi-Agent Multi-Armed Bandits.
By Safwan Hossain, Evi Micha, and Nisarg Shah.
[Manuscript]

M3
Learning Desirable Matchings From Partial Preferences.
By Hadi Hosseini, Vijay Menon, Nisarg Shah, and Sujoy Sikdar.
[Manuscript]

M2
Efficiency and Usability of Participatory Budgeting Methods.
By Gerdus Benadè, Ya’akov (Kobi) Gal, Nevo Itzhak, Ariel D. Procaccia, and Nisarg Shah.
[Manuscript, 2018]

M1
Ignorance is Often Bliss: Envy with Incomplete Information.
By Yiling Chen, and Nisarg Shah.
[Manuscript, 2017]

2020 + Forthcoming


BC2
Participatory Budgeting: Models and Approaches.
By Haris Aziz and Nisarg Shah.
In Pathways Between Social Science and Computational Social Science: Theories, Methods, and Interpretations (eds. Rudas and Gábor), Springer, 2020. Forthcoming.
Invited chapter.
[Book Chapter]

BC1
Reverting to Simplicity in Social Choice.
By Nisarg Shah.
In The Future of Economic Design (eds. Laslier, Moulin, Sanver, and Zwicker), pp. 39-44, Springer, 2020.
Invited chapter.
[Book Chapter]

J13
Preference Elicitation for Participatory Budgeting.
By Gerdus Benade, Ariel D. Procaccia, Swaprava Nath, and Nisarg Shah.
In Management Science, Forthcoming. Supercedes the AAAI-17 paper below.
[Paper]

J12
Peer Prediction with Heterogeneous Users.
By Arpit Agarwal, Debmalya Mandal, David C. Parkes, and Nisarg Shah.
In ACM Transactions on Economics and Computation, Volume 8, Number 1, Article 2, 2020.Supercedes the EC-17 paper below.
Invited for the special issue on selected papers from EC-17.
[Paper]

C46
Optimal Bounds on the Price of Fairness for Indivisible Goods.
In WINE-20: Proc. of 16th Conference on Web and Internet Economics, 2020. Forthcoming.
By Siddharth Barman, Umang Bhaskar, and Nisarg Shah.
[Paper]

C45
Fair Division with Binary Valuations: One Rule to Rule Them All.
In WINE-20: Proc. of 16th Conference on Web and Internet Economics, 2020. Forthcoming.
By Daniel Halpern, Ariel D. Procaccia, Alexandros Psomas, and Nisarg Shah.
[Paper]

C44
Resolving the Optimal Metric Distortion Conjecture.
By Vasilis Gkatzelis, Daniel Halpern, and Nisarg Shah.
In FOCS-20: Proc. of 61st Annual IEEE Symposium on Foundations of Computer Science, 2020. Forthcoming.
[Paper]

C43
Best of Both Worlds: Ex-Ante and Ex-Post Fairness in Resource Allocation.
By Rupert Freeman, Nisarg Shah, and Rohit Vaish.
In EC-20: Proc. of 21st ACM Conference on Economics and Computation, pp. 21-22, 2020.
[Paper]

C42
Optimal Communication-Distortion Tradeoff in Voting.
By Debmalya Mandal, Nisarg Shah, and David P. Woodruff.
In EC-20: Proc. of 21st ACM Conference on Economics and Computation, pp. 795-813, 2020.
[Paper]

C41
Proportionally Fair Clustering Revisited.
By Evi Micha and Nisarg Shah.
In ICALP-20: Proc. of 47th International Colloquium on Automata, Languages and Programming, 85:1-85:16, 2020
[Paper]

C40
Designing Fairly Fair Classifiers Via Economic Fairness Notions.
By Safwan Hossain, Andjela Mladenovic, and Nisarg Shah.
In TheWebConf/WWW-20: Proc. of 29th International World Wide Web Conference, pp. 1559-1569, 2020.
[Paper]

C39
The Effect of Strategic Noise in Linear Regression.
By Safwan Hossain and Nisarg Shah.
In AAMAS-20: Proc. of 19th International Conference on Autonomous Agents and Multi-Agent Systems, pp. 511-519, 2020.
[Paper]

C38
Can We Predict the Election Outcome from Sampled Votes?
By Evi Micha and Nisarg Shah.
In AAAI-20: Proc. of 34th AAAI Conference on Artificial Intelligence, pp. 2176-2183, 2020.
[Paper]

C37
The Surprising Power of Hiding Information in Facility Location.
By Safwan Hossain, Evi Micha, and Nisarg Shah.
In AAAI-20: Proc. of 34th AAAI Conference on Artificial Intelligence, pp. 2168-2175, 2020.
[Paper]

2019


J11
The Unreasonable Fairness of Maximum Nash Welfare.
By Ioannis Caragiannis, David Kurokawa, Hervé Moulin, Ariel D. Procaccia, Nisarg Shah, and Junxing Wang.
In ACM Transactions on Economics and Computation, Volume 7, Issue 3, Article 12, 2019. Supercedes the EC-16 paper below.
Invited for the special issue on selected papers from EC-16.
[Paper]

C36
Efficient and Thrifty Voting by Any Means Necessary.
By Debmalya Mandal, Ariel D. Procaccia, Nisarg Shah, and David P. Woodruff.
In NeurIPS-19: Proc. of 33rd Annual Conference on Neural Information Processing Systems, pp. 7178-7189, 2019.
Oral presentation (0.5% of submissions).
[Paper]

C35
Fair Division with Subsidy.
By Daniel Halpern and Nisarg Shah.
In SAGT-19: Proc. of 12th International Symposium on Algorithmic Game Theory, pp. 374-389, 2019.
[Paper]

C34
Group Fairness for the Allocation of Indivisible Goods.
By Vincent Conitzer, Rupert Freeman, Nisarg Shah, and Jennifer Wortman Vaughan.
In AAAI-19: Proc. of 33rd AAAI Conference on Artificial Intelligence, pp. 1853-1860, 2019.
[Paper]

C33
Primarily about Primaries.
By Allan Borodin, Omer Lev, Nisarg Shah, and Tyrone Strangway.
In AAAI-19: Proc. of 33rd AAAI Conference on Artificial Intelligence, pp. 1804-1811, 2019.
[Paper]

C32
The Pure Price of Anarchy of Pool BlockWithholding Attacks in Bitcoin Mining.
By Colleen Alkalay-Houlihan and Nisarg Shah.
In AAAI-19: Proc. of 33rd AAAI Conference on Artificial Intelligence, pp. 1724-1731, 2019.
[Paper]

2018


J10
Leximin Allocations in the Real World.
By David Kurokawa, Ariel D. Procaccia, and Nisarg Shah.
In ACM Transactions on Economics and Computation, Volume 6, Issue 3-4, Article 11, 2018. Supercedes the EC-15 paper below.
Invited for the special issue on selected papers from EC-15.
[Paper]

A2
Strategyproof Linear Regression in High Dimensions: An Overview.
By Yiling Chen, Chara Podimata, Ariel D. Procaccia, and Nisarg Shah.
In SIGecom Exchanges 17(1):54-60, Nov 2018. About the EC-18 paper below.
Invited letter.
[Letter]

C31
Fair Allocation of Indivisible Public Goods.
By Brandon Fain, Kamesh Munagala, and Nisarg Shah.
In EC-18: Proc. of 19th ACM Conference on Economics and Computation, pp. 575-592, 2018.
[Paper]

C30
Strategyproof Linear Regression in High Dimensions.
By Yiling Chen, Chara Podimata, Ariel D. Procaccia, and Nisarg Shah.
In EC-18: Proc. of 19th ACM Conference on Economics and Computation, pp. 9-26, 2018.
[Paper]

C29
Big City vs. the Great Outdoors: Voter Distribution and How it Affects Gerrymandering.
By Allan Borodin, Omer Lev, Nisarg Shah, and Tyrone Strangway.
In IJCAI-18: Proc. of 27th Intl. Joint Conference on Artificial Intelligence, pp. 98-104, 2018.
Also presented at COMSOC-18.
[Paper]

2017


A1
Making the World Fairer.
By Nisarg Shah.
In XRDS: XRDS: Crossroads, The ACM Magazine for Students, Volume 24, Issue 1, pp. 24-28, Fall 2017.
[Article | Web Version]

J9
Subset Selection Via Implicit Utilitarian Voting.
By Ioannis Caragiannis, Swaprava Nath, Ariel D. Procaccia, and Nisarg Shah.
In Journal of Artificial Intelligence Research, Volume 58, pp. 123-152, 2017. Supercedes the IJCAI-16 paper below.
[Paper]

C28
Fair Public Decision Making.
By Vincent Conitzer, Rupert Freeman, and Nisarg Shah.
In EC-17: Proc. of 18th ACM Conference on Economics and Computation, pp. 629-646, 2017.
[Paper]

C27
Peer Prediction with Heterogeneous Users.
By Arpit Agarwal, Debmalya Mandal, David C. Parkes, and Nisarg Shah.
In EC-17: Proc. of 18th ACM Conference on Economics and Computation, pp. 81-98, 2017. Superceded by the TEAC-19 paper above.

C26
Preference Elicitation for Participatory Budgeting.
By Gerdus Benade, Ariel D. Procaccia, Swaprava Nath, and Nisarg Shah.
In AAAI-17: Proc. of 31st AAAI Conference on Artificial Intelligence, pp. 376-382, 2017. Superceded by the MS paper above.

2016


T1
Optimal Social Decision Making.
By Nisarg Shah.
Ph.D. Thesis, Carnegie Mellon University, 2016.
Ph.D. Thesis Committee: Prof. Ariel D. Procaccia (advisor, CMU), Prof. Maria-Florina Balcan (CMU), Prof. Avrim Blum (CMU), Prof. Vincent Conitzer (Duke U), Prof. Tuomas Sandholm (CMU).
[Thesis]

J8
When Do Noisy Votes Reveal the Truth?
By Ioannis Caragiannis, Ariel D. Procaccia, and Nisarg Shah.
In ACM Transactions on Economics and Computation, Volume 4, Number 3, Article 15, February 2016. Supercedes the EC-13 paper below.
Invited for the special issue on selected papers from EC-13.
[Paper]

J7
Voting Rules as Error-Correcting Codes.
By Ariel D. Procaccia, Nisarg Shah, and Yair Zick.
In Artificial Intelligence, Volume 231, pp. 1-16, February 2016. Supercedes the AAAI-15 paper below.
[Paper]

C25
The Unreasonable Fairness of Maximum Nash Welfare.
By Ioannis Caragiannis, David Kurokawa, Hervé Moulin, Ariel D. Procaccia, Nisarg Shah, and Junxing Wang.
In EC-16: Proc. of 17th ACM Conference on Economics and Computation, pp. 305-322, 2016. Superceded by the TEAC paper above.

C24
Truthful Univariate Estimators.
By Ioannis Caragiannis, Ariel D. Procaccia, and Nisarg Shah.
In ICML-16: Proc. of 33rd Intl. Conference on Machine Learning, pp. 127-135, 2016.
[Paper]

C23
Subset Selection Via Implicit Utilitarian Voting.
By Ioannis Caragiannis, Swaprava Nath, Ariel D. Procaccia, and Nisarg Shah.
In IJCAI-16: Proc. of 25th Intl. Joint Conference on Artificial Intelligence, pp. 151-157, 2016. Superceded by the JAIR paper above.

C22
False-Name-Proof Recommendations in Social Networks.
By Markus Brill, Vincent Conitzer, Rupert Freeman, and Nisarg Shah.
In AAMAS-16: Proc. of 15th Intl. Joint Conference on Autonomous Agents and Multiagent Systems, pp. 332-340, 2016.
[Paper]

C21
Optimal Aggregation of Uncertain Preferences.
By Ariel D. Procaccia, and Nisarg Shah.
In AAAI-16: Proc. of 30th AAAI Conference on Artificial Intelligence, pp. 608-614, 2016.
[Paper]

2015


J6
Beyond Dominant Resource Fairness: Extensions, Limitations, and Indivisibilities.
By David C. Parkes, Ariel D. Procaccia, and Nisarg Shah.
In ACM Transactions on Economics and Computation, Volume 3, Number 1, Article 3, March 2015. Supercedes the EC-12 paper below.
Invited for the special issue on selected papers from EC-12
[Paper]

C20
Is Approval Voting Optimal Given Approval Votes?
By Ariel D. Procaccia, and Nisarg Shah.
In NIPS-15: Proc. of 29th Annual Conference on Neural Information Processing Systems, pp. 1792-1800, 2015.
[Paper]

C19
Leximin Allocations in the Real World.
By David Kurokawa, Ariel D. Procaccia, and Nisarg Shah.
In EC-15: Proc. of 16th ACM Conference on Economics and Computation, pp. 345-362, 2015. Superceded by the TEAC paper above.

C18
Ranked Voting on Social Networks.
By Ariel D. Procaccia, Nisarg Shah, and Eric Sodomka.
In IJCAI-15: Proc. of 24th Intl. Joint Conference on Artificial Intelligence, pp. 2040-2046, 2015.
[Paper]

C17
Voting Rules as Error-Correcting Codes.
By Ariel D. Procaccia, Nisarg Shah, and Yair Zick.
In AAAI-15: Proc. of 29th AAAI Conference on Artificial Intelligence, pp. 1000-1006, 2015. Superceded by the AIJ paper above.

J5
Average Case Analysis of the Classical Algorithm for Markov Decision Processes with Büchi Objectives.
By Krishnendu Chatterjee, Manas Joglekar, and Nisarg Shah.
In Theoretical Computer Science (TCS), Volume 573, pp. 71-89, 2015.
[Paper] Supercedes the FSTTCS-12 paper below.

2014


J4
No Agent Left Behind: Dynamic Fair Division of Multiple Resources.
By Ian Kash, Ariel D. Procaccia, and Nisarg Shah.
In Journal of Artificial Intelligence Research, Volume 51, pp. 579-603, 2014. Supercedes the AAMAS-13 paper below.
[Paper]

C16
Diverse Randomized Agents Vote to Win.
By Albert Xin Jiang, Leandro Soriano Marcolino, Ariel D. Procaccia, Tuomas Sandholm, Nisarg Shah, and Milind Tambe.
In NIPS-14: Proc. of 28th Annual Conference on Neural Information Processing Systems, pp. 2573-2581, 2014.
[Paper]

C15
Electing the Most Probable Without Eliminating the Irrational: Voting Over Intransitive Domains
By Edith Elkind, and Nisarg Shah.
In UAI-14: Proc. of 30th Conference on Uncertainty in Artificial Intelligence, pp. 182-191, 2014.
[Paper]

C14
Neutrality and Geometry of Mean Voting.
By Sébastien Lahaie, and Nisarg Shah.
In EC-14: Proc. of 15th ACM Conference on Economics and Computation, pp. 333-350, 2014.
[Paper]

C13
Modal Ranking: A Uniquely Robust Voting Rule.
By Ioannis Caragiannis, Ariel D. Procaccia, and Nisarg Shah.
In AAAI-14: Proc. of 28th AAAI Conference on Artificial Intelligence, pp. 616-622, 2014.
[Paper]

C12
Betting Strategies, Market Selection, and the Wisdom of Crowds.
By Willemien Kets, David M. Pennock, Rajiv Sethi, and Nisarg Shah.
In AAAI-14: Proc. of 28th AAAI Conference on Artificial Intelligence, pp. 735-741, 2014.
[Paper]

C11
On the Structure of Synergies in Cooperative Games.
By Ariel D. Procaccia, Nisarg Shah, and Max Lee Tucker.
In AAAI-14: Proc. of 28th AAAI Conference on Artificial Intelligence, pp. 763-769, 2014.
[Paper]

C10
Cooperative Max Games and Agent Failures.
By Yoram Bachrach, Rahul Savani, and Nisarg Shah.
In AAMAS-14: Proc. of 13th Intl. Joint Conference on Autonomous Agents and Multiagent Systems, pp. 29-36, 2014.
[Paper]

J3
Greedy Geometric Optimization Algorithms for Collection of Balls.
By Frédéric Cazals, Tom Dreyfus, Sushant Sachdeva and Nisarg Shah.
In Computer Graphics Forum (CGF), Volume 33, Issue 6, pp. 1-17, 2014.
[Paper]

2013


C9
When Do Noisy Votes Reveal the Truth?
By Ioannis Caragiannis, Ariel D. Procaccia, and Nisarg Shah.
In EC-13: Proc. of 14th ACM Conference on Electronic Commerce, pp. 143-160, 2013.
Superceded by the TEAC paper above.

C8
Defender (Mis)coordination in Security Games.
By Albert Xin Jiang, Ariel D. Procaccia, Yundi Qian, Nisarg Shah, and Milind Tambe.
In IJCAI-13: Proc. of 23rd Intl. Joint Conference on Artificial Intelligence, pp. 220-226, 2013.
[Paper]

C7
No Agent Left Behind: Dynamic Fair Division of Multiple Resources.
By Ian Kash, Ariel D. Procaccia, and Nisarg Shah.
In AAMAS-13: Proc. of 12th Intl. Joint Conference on Autonomous Agents and Multiagent Systems, pp. 351-358, 2013. Superceded by the JAIR paper above.

C6
Reliability Weighted Voting Games.
By Yoram Bachrach, and Nisarg Shah.
In SAGT-13: Proc. of 6th International Symposium on Algorithmic Game Theory, pp. 38-49, 2013.
[Paper]

J2
Symbolic Algorithms for Qualitative Analysis of Markov Decision Processes with Büchi Objectives.
By Krishnendu Chatterjee, Monika Henzinger, Manas Joglekar, and Nisarg Shah.
In Formal Methods in System Design (FMSD Journal), Volume 42, Issue 3, pp. 301-327, 2013. Supercedes the CAV-11 paper below.
[Paper]

2012


C5
Beyond Dominant Resource Fairness: Extensions, Limitations, and Indivisibilities.
By David C. Parkes, Ariel D. Procaccia, and Nisarg Shah.
In EC-12: Proc. of 13th ACM Conference on Electronic Commerce, pp. 808-825, 2012. Superceded by the TEAC paper above.

C4
A Maximum Likelihood Approach For Selecting Sets of Alternatives.
By Ariel D. Procaccia, Sashank J. Reddi, and Nisarg Shah.
In UAI-12: Proc. of 28th Conference on Uncertainty in Artificial Intelligence, pp. 695-704, 2012.
[Paper]

C3
Agent Failures in Totally Balanced Games and Convex Games.
By Yoram Bachrach, Ian Kash, and Nisarg Shah.
In WINE-12: Proc. of 8th Workshop on Internet & Network Economics, pp. 15-29, 2012.
[Paper]

C2
Average Case Analysis of the Classical Algorithm for Markov Decision Processes with Büchi Objectives.
By Krishnendu Chatterjee, Manas Joglekar, and Nisarg Shah.
In FSTTCS-12: Proc. of 32nd IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science, pp. 461-473, 2012. Superceded by the TCS paper above.

J1
Balanced group-labeled graphs.
By Manas Joglekar, Nisarg Shah and Ajit A. Diwan.
In Discrete Mathematics, Volume 312(9), pp 1542-1549, 2012.
[Paper]

2011


C1
Symbolic Algorithms for Qualitative Analysis of Markov Decision Processes with Büchi Objectives.
By Krishnendu Chatterjee, Monika Henzinger, Manas Joglekar, and Nisarg Shah.
In CAV-11: Proc. of 23rd International Conference on Computer Aided Verification, pp. 260-276, 2011. Superceded by the FMSD paper above.