Publications

Manuscripts

M2

Explainable and Efficient Randomized Voting Rules By Soroush Ebadian, Aris Filos-Ratsikas, Mohamad Latifian, and Nisarg Shah. [Manuscript]

M1

What is Best for Students, Numerical Scores or Letter Grades? By Evi Micha, Shreyas Sekar, and Nisarg Shah. [Manuscript]

2023+Forthcoming

J16

Best of Both Worlds: Ex-Ante and Ex-Post Fairness in Resource Allocation. By Haris Aziz, Rupert Freeman, Nisarg Shah, and Rohit Vaish. In Operations Research, 2023. Forthcoming. Combines selected results of the EC-20 paper below with a WINE-20 paper by Haris Aziz. [paper]

C69

Best of Both Distortion Worlds. By Vasilis Gkatzelis, Mohamad Latifian, and Nisarg Shah. In EC-23: Proc. of 24th ACM Conference on Economics and Computation, 2023. Forthcoming. [paper]

C68

Proportionally Fair Online Allocation of Public Goods with Predictions. By Siddhartha Banerjee, Vasilis Gkatzelis, Safwan Hossain, Billy Jin, Evi Micha, and Nisarg Shah. In IJCAI-23: Proc. of 32nd International Joint Conference on Artificial Intelligence, 2023. Forthcoming. [manuscript]

C67

The Distortion of Approval Voting with Runoff. By Soroush Ebadian, Mohamad Latifian, and Nisarg Shah. In AAMAS-23: Proc. of 22nd International Conference on Autonomous Agents and Multi-Agent Systems, 2023. Forthcoming. [paper]

C66

Group Fairness in Peer Review. By Haris Aziz, Evi Micha, and Nisarg Shah. In AAMAS-23: Proc. of 22nd International Conference on Autonomous Agents and Multi-Agent Systems (Extended Abstract), 2023. Forthcoming. [paper]

C65

Class Fairness in Online Matching. By Hadi Hosseini, Zhiyi Huang, Ayumi Igarashi, and Nisarg Shah. In AAAI-23: Proc. of 37th AAAI Conference on Artificial Intelligence, 2023. Forthcoming. [Manuscript]

C64

Voting with Preference Intensities. By Anson Kahng, Mohamad Latifian, and Nisarg Shah. In AAAI-23: Proc. of 37th AAAI Conference on Artificial Intelligence, 2023. Forthcoming. [Manuscript]

C63

Partitioning Friends Fairly. By Lily Li, Evi Micha, Aleksandar Nikolov, and Nisarg Shah. In AAAI-23: Proc. of 37th AAAI Conference on Artificial Intelligence, 2023. Forthcoming. [Manuscript]

2022

J15

The Metric Distortion of Multiwinner Voting. By Ioannis Caragiannis, Nisarg Shah, and Alexandros A. Voudouris. In Artificial Intelligence, Volume 313, pp. 103802, 2022. Supercedes the AAAI-22 paper below. [paper]

C62

Is Sortition Both Representative and Fair? By Soroush Ebadian, Gregory Kehne, Evi Micha, Ariel D. Procaccia, and Nisarg Shah. In NeurIPS-22: Proc. of 36th Annual Conference on Neural Information Processing Systems, pp. 3431-3443, 2022. [paper]

C61

Optimized Distortion and Proportional Fairness in Voting. By Soroush Ebadian, Anson Kahng, Dominik Peters, and Nisarg Shah. In EC-22: Proc. of 23rd ACM Conference on Economics and Computation, pp. 563–600, 2022. [paper]

C60

Distortion in Voting with Top-t Preferences. By Allan Borodin, Daniel Halpern, Mohamad Latifian, and Nisarg Shah. In IJCAI-22: Proc. of 31st International Joint Conference on Artificial Intelligence, pp. 116-122, 2022. [paper]

C59

Efficient Resource Allocation with Secretive Agents. By Soroush Ebadian, Rupert Freeman, and Nisarg Shah. In IJCAI-22: Proc. of 31st International Joint Conference on Artificial Intelligence, pp. 272-278, 2022. [paper]

C58

How to Fairly Allocate Easy and Difficult Chores. By Soroush Ebadian, Dominik Peters, and Nisarg Shah. In AAMAS-22: Proc. of 21st International Conference on Autonomous Agents and Multi-Agent Systems, pp. 372-380, 2022. [paper]

C57

Little House (Seat) on the Prairie: Compactness, Gerrymandering, and Population Distribution. By Allan Borodin, Omer Lev, Nisarg Shah, and Tyrone Strangway. In AAMAS-22: Proc. of 21st International Conference on Autonomous Agents and Multi-Agent Systems, pp. 154-162, 2022. [paper]

C56

The Metric Distortion of Multiwinner Voting. By Ioannis Caragiannis, Nisarg Shah, and Alexandros A. Voudouris. In AAAI-22: Proc. of 36th AAAI Conference on Artificial Intelligence, pp. 4900-4907, 2022. Superceded by the AIJ paper above.

C55

A Little Charity Guarantees Fair Connected Graph Partitioning. By Ioannis Caragiannis, Evi Micha, and Nisarg Shah. In AAAI-22: Proc. of 36th AAAI Conference on Artificial Intelligence, pp. 4908-4916, 2022. [paper]

2021

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 Péli), pp. 215-236, Springer, 2021. Invited chapter. [Book Chapter]

J14

The Effect of Strategic Noise in Linear Regression. By Safwan Hossain and Nisarg Shah. In Journal of Autonomous Agents and Multi-agent Systems (JAAMAS), Volume 35, Article 21, 2021. Supercedes the AAMAS-20 paper below. [Paper]

J13

Preference Elicitation for Participatory Budgeting. By Gerdus Benadè, Ariel D. Procaccia, Swaprava Nath, and Nisarg Shah. In Management Science, Volume 67, Number 5, pp. 2813-2827, 2021. Supercedes the AAAI-17 paper below. [Paper]

A3

Distortion in Social Choice Problems: An Annotated Reading List. By Elliot Anshelevich, Aris Filos-Ratsikas, Nisarg Shah, and Alexandros A. Voudouris. In SIGecom Exchanges, 19(1):12-14, Jun 2021. Based on the IJCAI-21 survey paper below. [newsletter article]

C54

Fair Algorithms for Multi-Agent Multi-Armed Bandits. By Safwan Hossain, Evi Micha, and Nisarg Shah. In NeurIPS-21: Proc. of 35th Annual Conference on Neural Information Processing Systems, pp.24005-24017, 2021. [paper]

C53

Two-Sided Matching Meets Fair Division. By Rupert Freeman, Evi Micha, and Nisarg Shah. In IJCAI-21: Proc. of 30th International Joint Conference on Artificial Intelligence, pp. 203-209, 2021. [paper]

C52

Fair and Efficient Resource Allocation with Partial Information. By Daniel Halpern and Nisarg Shah. In IJCAI-21: Proc. of 30th International Joint Conference on Artificial Intelligence, pp. 224-230, 2021. [paper]

C51

Surprisingly Popular Voting Recovers Rankings, Surprisingly! By Hadi Hosseini, Debmalya Mandal, Nisarg Shah, and Kevin Shi. In IJCAI-21: Proc. of 30th International Joint Conference on Artificial Intelligence, pp. 245-251, 2021. [paper]

C50

Distortion in Social Choice Problems: The First 15 Years and Beyond. By Elliot Anshelevich, Aris Filos-Ratsikas, Nisarg Shah, and Alexandros A. Voudouris. In IJCAI-21: Proc. of 30th International Joint Conference on Artificial Intelligence (Survey Track), pp. 4294-4301, 2021. [paper]

C49

Market-Based Explanations of Collective Decisions. By Dominik Peters, Grzegorz Pierczyński, Nisarg Shah, and Piotr Skowron. In AAAI-21: Proc. of 35th AAAI Conference on Artificial Intelligence, pp. 5656-5663, 2021. [paper]

C48

Aggregating Binary Judgments Ranked By Accuracy. By Daniel Halpern, Gregory Kehne, Dominik Peters, Ariel D. Procaccia, Nisarg Shah, and Piotr Skowron. In AAAI-21: Proc. of 35th AAAI Conference on Artificial Intelligence, pp. 5456-5463, 2021. [paper]

C47

Necessarily Optimal One-Sided Matchings. By Hadi Hosseini, Vijay Menon, Nisarg Shah, and Sujoy Sikdar. In AAAI-21: Proc. of 35th AAAI Conference on Artificial Intelligence, pp. 5481-5488, 2021. [paper]

2020

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]

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. By Siddharth Barman, Umang Bhaskar, and Nisarg Shah. In WINE-20: Proc. of 16th Conference on Web and Internet Economics, pp. 356-369, 2020. [Paper]

C45

Fair Division with Binary Valuations: One Rule to Rule Them All. By Daniel Halpern, Ariel D. Procaccia, Alexandros Psomas, and Nisarg Shah. In WINE-20: Proc. of 16th Conference on Web and Internet Economics, pp. 370-383, 2020. [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, pp. 1427-1438, 2020. [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. Selected results combined with the WINE-20 paper of Haris Aziz into the OR paper above. [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. Superceded by the JAAMAS paper above.

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 article. [newsletter article]

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 Benadè, 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: Ariel D. Procaccia (advisor, CMU), Maria-Florina Balcan (CMU), Avrim Blum (CMU), Vincent Conitzer (Duke U), 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. Supercedes the FSTTCS-12 paper below. [Paper]

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.