Publications

Manuscripts


M5
Pure Nash Equilibria in Linear Regression.
By Safwan Hossain and Nisarg Shah.
[Manuscript]

M4
Efficient and Thrifty Voting by Any Means Necessary.
By Debmalya Mandal, Ariel D. Procaccia, Nisarg Shah, and David P. Woodruff.
[Manuscript]

M3
Can We Predict the Election Outcome from Sampled Votes?
By Evi Micha and Nisarg Shah.
[Manuscript]

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

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

2019


J12
Peer Prediction with Heterogeneous Users.
By Arpit Agarwal, Debmalya Mandal, David C. Parkes, and Nisarg Shah.
In ACM Transactions on Economics and Computation. Forthcoming. Supercedes the EC-17 paper below.
Invited for the special issue on selected papers from EC-17.
[TEAC paper | EC paper]

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. Forthcoming. Supercedes the EC-16 paper below.
Invited for the special issue on selected papers from EC-16.
[Full version | EC version]

J10
Leximin Allocations in the Real World.
By David Kurokawa, Ariel D. Procaccia, and Nisarg Shah.
In ACM Transactions on Economics and Computation. Forthcoming. Supercedes the EC-15 paper below.
Invited for the special issue on selected papers from EC-15.
[Full version | EC version]

C35
Fair Division with Subsidy.
By Daniel Halpern and Nisarg Shah.
In SAGT-19: Proc. 12th International Symposium on Algorithmic Game Theory, 2019. To appear.
[Full paper | SAGT 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. 33rd AAAI Conference on Artificial Intelligence, 2019. To appear.
[Full paper | AAAI paper]

C33
Primarily about Primaries.
By Allan Borodin, Omer Lev, Nisarg Shah, and Tyrone Strangway.
In AAAI-19: Proc. 33rd AAAI Conference on Artificial Intelligence, 2019. To appear.
[Full paper | AAAI 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. 33rd AAAI Conference on Artificial Intelligence, 2019. To appear.
[paper]

2018


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.
[Exchanges Letter]

BC1
Reverting to Simplicity in Social Choice.
By Nisarg Shah.
In The Future of Economic Design (eds. Laslier, Moulin, Sanver, and Zwicker), 2018 (forthcoming).
[chapter]

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

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

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. 27th Intl. Joint Conference on Artificial Intelligence, pp. 98-104, 2018.
[Full paper (presented at COMSOC-18) | IJCAI version]

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.
[PDF | HTML]

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

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

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.
[JAIR version | IJCAI version]

C26
Preference Elicitation for Participatory Budgeting.
By Gerdus Benade, Ariel D. Procaccia, Swaprava Nath, and Nisarg Shah.
In AAAI-17: Proc. 31st AAAI Conference on Artificial Intelligence, pp. 376-382, 2017.
[Full paper | AAAI version]

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.
[TEAC version | EC version | Presentation]

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.
[AIJ version | AAAI version | Presentation]

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. 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. 33rd Intl. Conference on Machine Learning, pp. 127-135, 2016.
[ICML version]

C23
Subset Selection Via Implicit Utilitarian Voting.
By Ioannis Caragiannis, Swaprava Nath, Ariel D. Procaccia, and Nisarg Shah.
In IJCAI-16: Proc. 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. 15th Intl. Joint Conference on Autonomous Agents and Multiagent Systems, pp. 332-340, 2016.
[Full version | AAMAS version]

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

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
[TEAC version | EC version | Presentation]

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

C19
Leximin Allocations in the Real World.
By David Kurokawa, Ariel D. Procaccia, and Nisarg Shah.
In EC-15: Proc. 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. 24th Intl. Joint Conference on Artificial Intelligence, pp. 2040-2046, 2015.
[Full version | IJCAI version]

C17
Voting Rules as Error-Correcting Codes.
By Ariel D. Procaccia, Nisarg Shah, and Yair Zick.
In AAAI-15: Proc. 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.
[TCS version | FSTTCS version] 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.
[JAIR version | AAMAS version | Presentation]

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. 28th Annual Conference on Neural Information Processing Systems, pp. 2573-2581, 2014.
[Full version | NIPS version]

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

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

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

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. 28th AAAI Conference on Artificial Intelligence, pp. 735-741, 2014.
[AAAI version | Presentation]

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

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

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.
[Full version | CGF version]

2013


C9
When Do Noisy Votes Reveal the Truth?
By Ioannis Caragiannis, Ariel D. Procaccia, and Nisarg Shah.
In EC-13: Proc. 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. 23rd Intl. Joint Conference on Artificial Intelligence, pp. 220-226, 2013.
[IJCAI version | Presentation]

C7
No Agent Left Behind: Dynamic Fair Division of Multiple Resources.
By Ian Kash, Ariel D. Procaccia, and Nisarg Shah.
In AAMAS-13: Proc. 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. 6th International Symposium on Algorithmic Game Theory, pp. 38-49, 2013.
[Full version | SAGT version | Presentation]

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.
[FMSD version | CAV version | Project homepage] Supercedes the CAV-11 paper below.

2012


C5
Beyond Dominant Resource Fairness: Extensions, Limitations, and Indivisibilities.
By David C. Parkes, Ariel D. Procaccia, and Nisarg Shah.
In EC-12: Proc. 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. 28th Conference on Uncertainty in Artificial Intelligence, pp. 695-704, 2012.
[Full version | UAI version | Poster]

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

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. 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.
[DM version | Presentation]

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. 23rd International Conference on Computer Aided Verification, pp. 260-276, 2011. Superceded by the FMSD paper above.