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ORCIDNisarg Shah 0001
Person information
- affiliation: University of Toronto
- affiliation: Harvard University
- affiliation: Carnegie Mellon University
Other persons with the same name
- Nisarg Shah 0002 — Indian Institute of Technology, Kharagpur, West Bengal, India
- Nisarg Shah 0003 — Indian Institute of Technology Gandhinagar, Gujarat, India
- Nisarg Shah 0004 — University of Wisconsin-Madison, WI, USA
- Nisarg Shah 0005 — Indiana Bloomington University, IN, USA
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2020 – today
- 2024
[j21]Allan Borodin, Omer Lev
, Nisarg Shah, Tyrone Strangway:
Primarily about primaries. Artif. Intell. 329: 104095 (2024)- [j20]Hadi Hosseini, Zhiyi Huang, Ayumi Igarashi, Nisarg Shah:
Class fairness in online matching. Artif. Intell. 335: 104177 (2024) - [j19]Haris Aziz, Rupert Freeman, Nisarg Shah, Rohit Vaish:
Best of Both Worlds: Ex Ante and Ex Post Fairness in Resource Allocation. Oper. Res. 72(4): 1674-1688 (2024) - [j18]Soroush Ebadian, Anson Kahng, Dominik Peters, Nisarg Shah:
Optimized Distortion and Proportional Fairness in Voting. ACM Trans. Economics and Comput. 12(1): 3:1-3:39 (2024) - [c77]Soroush Ebadian, Aris Filos-Ratsikas, Mohamad Latifian, Nisarg Shah:
Computational Aspects of Distortion. AAMAS 2024: 499-507 - [c76]Soroush Ebadian, Rupert Freeman, Nisarg Shah:
Harm Ratio: A Novel and Versatile Fairness Criterion. EAAMO 2024: 3:1-3:14 - [c75]Evi Micha, Shreyas Sekar, Nisarg Shah:
What Is Best for Students, Numerical Scores or Letter Grades? IJCAI 2024: 2949-2957 - [i26]Evi Micha, Shreyas Sekar, Nisarg Shah:
What is Best for Students, Numerical Scores or Letter Grades? CoRR abs/2406.15405 (2024) - [i25]Soroush Ebadian, Rupert Freeman, Nisarg Shah:
Harm Ratio: A Novel and Versatile Fairness Criterion. CoRR abs/2410.02977 (2024) - [i24]Haris Aziz, Evi Micha, Nisarg Shah:
Group Fairness in Peer Review. CoRR abs/2410.03474 (2024) - 2023
- [c74]Hadi Hosseini, Zhiyi Huang, Ayumi Igarashi, Nisarg Shah:
Class Fairness in Online Matching. AAAI 2023: 5673-5680 - [c73]Anson Kahng, Mohamad Latifian, Nisarg Shah:
Voting with Preference Intensities. AAAI 2023: 5697-5704 - [c72]Lily Li, Evi Micha, Aleksandar Nikolov, Nisarg Shah:
Partitioning Friends Fairly. AAAI 2023: 5747-5754 - [c71]Soroush Ebadian, Mohamad Latifian, Nisarg Shah:
The Distortion of Approval Voting with Runoff. AAMAS 2023: 1752-1760 - [c70]Haris Aziz, Evi Micha, Nisarg Shah:
Group Fairness in Peer Review. AAMAS 2023: 2889-2891 - [c69]Siddhartha Banerjee, Vasilis Gkatzelis, Safwan Hossain, Billy Jin, Evi Micha, Nisarg Shah:
Proportionally Fair Online Allocation of Public Goods with Predictions. IJCAI 2023: 20-28 - [c68]Nisarg Shah:
Pushing the Limits of Fairness in Algorithmic Decision-Making. IJCAI 2023: 7051-7056 - [c67]Haris Aziz, Evi Micha, Nisarg Shah:
Group Fairness in Peer Review. NeurIPS 2023 - [c66]Soroush Ebadian, Aris Filos-Ratsikas, Mohamad Latifian, Nisarg Shah:
Explainable and Efficient Randomized Voting Rules. NeurIPS 2023 - [c65]Vasilis Gkatzelis, Mohamad Latifian, Nisarg Shah:
Best of Both Distortion Worlds. EC 2023: 738-758 - [i23]Vasilis Gkatzelis, Mohamad Latifian, Nisarg Shah:
Best of Both Distortion Worlds. CoRR abs/2305.19453 (2023) - 2022
- [j17]Ioannis Caragiannis, Nisarg Shah, Alexandros A. Voudouris:
The metric distortion of multiwinner voting. Artif. Intell. 313: 103802 (2022) - [c64]Ioannis Caragiannis, Nisarg Shah, Alexandros A. Voudouris:
The Metric Distortion of Multiwinner Voting. AAAI 2022: 4900-4907 - [c63]Ioannis Caragiannis, Evi Micha, Nisarg Shah:
A Little Charity Guarantees Fair Connected Graph Partitioning. AAAI 2022: 4908-4916 - [c62]Allan Borodin, Omer Lev, Nisarg Shah, Tyrone Strangway:
Little House (Seat) on the Prairie: Compactness, Gerrymandering, and Population Distribution. AAMAS 2022: 154-162 - [c61]Soroush Ebadian, Dominik Peters, Nisarg Shah:
How to Fairly Allocate Easy and Difficult Chores. AAMAS 2022: 372-380 - [c60]Allan Borodin, Daniel Halpern, Mohamad Latifian, Nisarg Shah:
Distortion in Voting with Top-t Preferences. IJCAI 2022: 116-122 - [c59]Soroush Ebadian, Rupert Freeman, Nisarg Shah:
Efficient Resource Allocation with Secretive Agents. IJCAI 2022: 272-278 - [c58]Soroush Ebadian, Gregory Kehne, Evi Micha, Ariel D. Procaccia, Nisarg Shah:
Is Sortition Both Representative and Fair? NeurIPS 2022 - [c57]Soroush Ebadian, Anson Kahng, Dominik Peters, Nisarg Shah:
Optimized Distortion and Proportional Fairness in Voting. EC 2022: 563-600 - [i22]Ioannis Caragiannis, Nisarg Shah, Alexandros A. Voudouris:
The Metric Distortion of Multiwinner Voting. CoRR abs/2201.13332 (2022) - [i21]Hadi Hosseini, Zhiyi Huang, Ayumi Igarashi, Nisarg Shah:
Class Fairness in Online Matching. CoRR abs/2203.03751 (2022) - [i20]Soroush Ebadian, Anson Kahng, Nisarg Shah, Dominik Peters:
Optimized Distortion and Proportional Fairness in Voting. CoRR abs/2205.15760 (2022) - [i19]Siddhartha Banerjee, Vasilis Gkatzelis, Safwan Hossain, Billy Jin, Evi Micha, Nisarg Shah:
Proportionally Fair Online Allocation of Public Goods with Predictions. CoRR abs/2209.15305 (2022) - 2021
- [j16]Safwan Hossain, Nisarg Shah:
The effect of strategic noise in linear regression. Auton. Agents Multi Agent Syst. 35(2): 21 (2021) - [j15]Gerdus Benadè, Swaprava Nath, Ariel D. Procaccia, Nisarg Shah:
Preference Elicitation for Participatory Budgeting. Manag. Sci. 67(5): 2813-2827 (2021) - [j14]Elliot Anshelevich, Aris Filos-Ratsikas, Nisarg Shah, Alexandros A. Voudouris:
Distortion in social choice problems: an annotated reading list. SIGecom Exch. 19(1): 12-14 (2021) - [c56]Daniel Halpern, Gregory Kehne, Dominik Peters, Ariel D. Procaccia, Nisarg Shah, Piotr Skowron:
Aggregating Binary Judgments Ranked by Accuracy. AAAI 2021: 5456-5463 - [c55]Hadi Hosseini, Vijay Menon, Nisarg Shah, Sujoy Sikdar:
Necessarily Optimal One-Sided Matchings. AAAI 2021: 5481-5488 - [c54]Dominik Peters, Grzegorz Pierczynski, Nisarg Shah, Piotr Skowron:
Market-Based Explanations of Collective Decisions. AAAI 2021: 5656-5663 - [c53]Rupert Freeman, Evi Micha, Nisarg Shah:
Two-Sided Matching Meets Fair Division. IJCAI 2021: 203-209 - [c52]Daniel Halpern, Nisarg Shah:
Fair and Efficient Resource Allocation with Partial Information. IJCAI 2021: 224-230 - [c51]Hadi Hosseini, Debmalya Mandal, Nisarg Shah, Kevin Shi:
Surprisingly Popular Voting Recovers Rankings, Surprisingly! IJCAI 2021: 245-251 - [c50]Elliot Anshelevich, Aris Filos-Ratsikas, Nisarg Shah, Alexandros A. Voudouris:
Distortion in Social Choice Problems: The First 15 Years and Beyond. IJCAI 2021: 4294-4301 - [c49]Safwan Hossain, Evi Micha, Nisarg Shah:
Fair Algorithms for Multi-Agent Multi-Armed Bandits. NeurIPS 2021: 24005-24017 - [i18]Elliot Anshelevich, Aris Filos-Ratsikas, Nisarg Shah, Alexandros A. Voudouris:
Distortion in Social Choice Problems: The First 15 Years and Beyond. CoRR abs/2103.00911 (2021) - [i17]Hadi Hosseini, Debmalya Mandal, Nisarg Shah, Kevin Shi:
Surprisingly Popular Voting Recovers Rankings, Surprisingly! CoRR abs/2105.09386 (2021) - [i16]Daniel Halpern, Nisarg Shah:
Fair and Efficient Resource Allocation with Partial Information. CoRR abs/2105.10064 (2021) - [i15]Rupert Freeman, Evi Micha, Nisarg Shah:
Two-Sided Matching Meets Fair Division. CoRR abs/2107.07404 (2021) - [i14]Soroush Ebadian, Dominik Peters, Nisarg Shah:
How to Fairly Allocate Easy and Difficult Chores. CoRR abs/2110.11285 (2021) - 2020
- [j13]Arpit Agarwal, Debmalya Mandal, David C. Parkes, Nisarg Shah:
Peer Prediction with Heterogeneous Users. ACM Trans. Economics and Comput. 8(1): 2:1-2:34 (2020) - [c48]Safwan Hossain, Evi Micha, Nisarg Shah:
The Surprising Power of Hiding Information in Facility Location. AAAI 2020: 2168-2175 - [c47]Evi Micha, Nisarg Shah:
Can We Predict the Election Outcome from Sampled Votes? AAAI 2020: 2176-2183 - [c46]Safwan Hossain, Nisarg Shah:
The Effect of Strategic Noise in Linear Regression. AAMAS 2020: 511-519 - [c45]Vasilis Gkatzelis, Daniel Halpern, Nisarg Shah:
Resolving the Optimal Metric Distortion Conjecture. FOCS 2020: 1427-1438 - [c44]Evi Micha, Nisarg Shah:
Proportionally Fair Clustering Revisited. ICALP 2020: 85:1-85:16 - [c43]Rupert Freeman, Nisarg Shah, Rohit Vaish:
Best of Both Worlds: Ex-Ante and Ex-Post Fairness in Resource Allocation. EC 2020: 21-22 - [c42]Debmalya Mandal, Nisarg Shah, David P. Woodruff:
Optimal Communication-Distortion Tradeoff in Voting. EC 2020: 795-813 - [c41]Siddharth Barman, Umang Bhaskar, Nisarg Shah:
Optimal Bounds on the Price of Fairness for Indivisible Goods. WINE 2020: 356-369 - [c40]Daniel Halpern, Ariel D. Procaccia, Alexandros Psomas, Nisarg Shah:
Fair Division with Binary Valuations: One Rule to Rule Them All. WINE 2020: 370-383 - [c39]Safwan Hossain, Andjela Mladenovic, Nisarg Shah:
Designing Fairly Fair Classifiers Via Economic Fairness Notions. WWW 2020: 1559-1569 - [i13]Haris Aziz, Nisarg Shah:
Participatory Budgeting: Models and Approaches. CoRR abs/2003.00606 (2020) - [i12]Vasilis Gkatzelis, Daniel Halpern, Nisarg Shah:
Resolving the Optimal Metric Distortion Conjecture. CoRR abs/2004.07447 (2020) - [i11]Rupert Freeman, Nisarg Shah, Rohit Vaish:
Best of Both Worlds: Ex-Ante and Ex-Post Fairness in Resource Allocation. CoRR abs/2005.14122 (2020) - [i10]Daniel Halpern, Ariel D. Procaccia, Alexandros Psomas, Nisarg Shah:
Fair Division with Binary Valuations: One Rule to Rule Them All. CoRR abs/2007.06073 (2020) - [i9]Siddharth Barman, Umang Bhaskar, Nisarg Shah:
Settling the Price of Fairness for Indivisible Goods. CoRR abs/2007.06242 (2020) - [i8]Safwan Hossain, Evi Micha, Nisarg Shah:
Fair Algorithms for Multi-Agent Multi-Armed Bandits. CoRR abs/2007.06699 (2020) - [i7]Safwan Hossain, Nisarg Shah:
The Effect of Strategic Noise in Linear Regression. CoRR abs/2007.07316 (2020) - [i6]Hadi Hosseini, Vijay Menon, Nisarg Shah, Sujoy Sikdar:
Learning Desirable Matchings From Partial Preferences. CoRR abs/2007.09079 (2020)
2010 – 2019
- 2019
- [j12]Ioannis Caragiannis, David Kurokawa, Hervé Moulin, Ariel D. Procaccia, Nisarg Shah, Junxing Wang:
The Unreasonable Fairness of Maximum Nash Welfare. ACM Trans. Economics and Comput. 7(3): 12:1-12:32 (2019) - [c38]Colleen Alkalay-Houlihan, Nisarg Shah:
The Pure Price of Anarchy of Pool Block Withholding Attacks in Bitcoin Mining. AAAI 2019: 1724-1731 - [c37]Allan Borodin, Omer Lev, Nisarg Shah, Tyrone Strangway:
Primarily about Primaries. AAAI 2019: 1804-1811 - [c36]Vincent Conitzer, Rupert Freeman, Nisarg Shah, Jennifer Wortman Vaughan:
Group Fairness for the Allocation of Indivisible Goods. AAAI 2019: 1853-1860 - [c35]Debmalya Mandal, Ariel D. Procaccia, Nisarg Shah, David P. Woodruff:
Efficient and Thrifty Voting by Any Means Necessary. NeurIPS 2019: 7178-7189 - [c34]Daniel Halpern, Nisarg Shah:
Fair Division with Subsidy. SAGT 2019: 374-389 - 2018
- [j11]Yiling Chen, Chara Podimata, Ariel D. Procaccia, Nisarg Shah:
Strategyproof linear regression in high dimensions: an overview. SIGecom Exch. 17(1): 54-60 (2018) - [j10]David Kurokawa, Ariel D. Procaccia, Nisarg Shah:
Leximin Allocations in the Real World. ACM Trans. Economics and Comput. 6(3-4): 11:1-11:24 (2018) - [c33]Allan Borodin, Omer Lev, Nisarg Shah, Tyrone Strangway:
Big City vs. the Great Outdoors: Voter Distribution and How It Affects Gerrymandering. IJCAI 2018: 98-104 - [c32]Yiling Chen, Chara Podimata, Ariel D. Procaccia, Nisarg Shah:
Strategyproof Linear Regression in High Dimensions. EC 2018: 9-26 - [c31]Brandon Fain, Kamesh Munagala, Nisarg Shah:
Fair Allocation of Indivisible Public Goods. EC 2018: 575-592 - [i5]Brandon Fain, Kamesh Munagala, Nisarg Shah:
Fair Allocation of Indivisible Public Goods. CoRR abs/1805.03164 (2018) - [i4]Yiling Chen, Chara Podimata, Ariel D. Procaccia, Nisarg Shah:
Strategyproof Linear Regression in High Dimensions. CoRR abs/1805.10693 (2018) - 2017
- [j9]Nisarg Shah:
Making the world fairer. XRDS 24(1): 24-28 (2017) - [j8]Ioannis Caragiannis, Swaprava Nath, Ariel D. Procaccia, Nisarg Shah:
Subset Selection Via Implicit Utilitarian Voting. J. Artif. Intell. Res. 58: 123-152 (2017) - [c30]Gerdus Benade, Swaprava Nath, Ariel D. Procaccia, Nisarg Shah:
Preference Elicitation For Participatory Budgeting. AAAI 2017: 376-382 - [c29]Nisarg Shah:
Optimal Social Decision Making. AAMAS 2017: 5 - [c28]Arpit Agarwal, Debmalya Mandal, David C. Parkes, Nisarg Shah:
Peer Prediction with Heterogeneous Users. EC 2017: 81-98 - [c27]Vincent Conitzer, Rupert Freeman, Nisarg Shah:
Fair Public Decision Making. EC 2017: 629-646 - 2016
- [j7]Ariel D. Procaccia, Nisarg Shah, Yair Zick:
Voting rules as error-correcting codes. Artif. Intell. 231: 1-16 (2016) - [j6]Ioannis Caragiannis, Ariel D. Procaccia, Nisarg Shah:
When Do Noisy Votes Reveal the Truth? ACM Trans. Economics and Comput. 4(3): 15:1-15:30 (2016) - [c26]Ariel D. Procaccia, Nisarg Shah:
Optimal Aggregation of Uncertain Preferences. AAAI 2016: 608-614 - [c25]Markus Brill, Vincent Conitzer, Rupert Freeman, Nisarg Shah:
False-Name-Proof Recommendations in Social Networks. AAMAS 2016: 332-340 - [c24]Ioannis Caragiannis, Ariel D. Procaccia, Nisarg Shah:
Truthful Univariate Estimators. ICML 2016: 127-135 - [c23]Ioannis Caragiannis, Swaprava Nath, Ariel D. Procaccia, Nisarg Shah:
Subset Selection via Implicit Utilitarian Voting. IJCAI 2016: 151-157 - [c22]Markus Brill, Vincent Conitzer, Rupert Freeman, Nisarg Shah:
False-Name-Proof Recommendations in Social Networks. ISAIM 2016 - [c21]Ioannis Caragiannis, David Kurokawa, Hervé Moulin, Ariel D. Procaccia, Nisarg Shah, Junxing Wang:
The Unreasonable Fairness of Maximum Nash Welfare. EC 2016: 305-322 - 2015
- [j5]Krishnendu Chatterjee, Manas Joglekar, Nisarg Shah:
Average case analysis of the classical algorithm for Markov decision processes with Büchi objectives. Theor. Comput. Sci. 573: 71-89 (2015) - [j4]David C. Parkes, Ariel D. Procaccia, Nisarg Shah:
Beyond Dominant Resource Fairness: Extensions, Limitations, and Indivisibilities. ACM Trans. Economics and Comput. 3(1): 3:1-3:22 (2015) - [c20]Ariel D. Procaccia, Nisarg Shah, Yair Zick:
Voting Rules As Error-Correcting Codes. AAAI 2015: 1000-1006 - [c19]Ariel D. Procaccia, Nisarg Shah, Eric Sodomka:
Ranked Voting on Social Networks. IJCAI 2015: 2040-2046 - [c18]Ariel D. Procaccia, Nisarg Shah:
Is Approval Voting Optimal Given Approval Votes? NIPS 2015: 1801-1809 - [c17]David Kurokawa, Ariel D. Procaccia, Nisarg Shah:
Leximin Allocations in the Real World. EC 2015: 345-362 - 2014
- [j3]Ian A. Kash, Ariel D. Procaccia, Nisarg Shah:
No Agent Left Behind: Dynamic Fair Division of Multiple Resources. J. Artif. Intell. Res. 51: 579-603 (2014) - [c16]Ioannis Caragiannis, Ariel D. Procaccia, Nisarg Shah:
Modal Ranking: A Uniquely Robust Voting Rule. AAAI 2014: 616-622 - [c15]Willemien Kets, David M. Pennock, Rajiv Sethi, Nisarg Shah:
Betting Strategies, Market Selection, and the Wisdom of Crowds. AAAI 2014: 735-741 - [c14]Ariel D. Procaccia, Nisarg Shah, Max Lee Tucker:
On the Structure of Synergies in Cooperative Games. AAAI 2014: 763-769 - [c13]Yoram Bachrach, Rahul Savani, Nisarg Shah:
Cooperative max games and agent failures. AAMAS 2014: 29-36 - [c12]Albert Xin Jiang, Leandro Soriano Marcolino, Ariel D. Procaccia, Tuomas Sandholm, Nisarg Shah, Milind Tambe:
Diverse Randomized Agents Vote to Win. NIPS 2014: 2573-2581 - [c11]Sébastien Lahaie, Nisarg Shah:
Neutrality and geometry of mean voting. EC 2014: 333-350 - [c10]Edith Elkind, Nisarg Shah:
Electing the Most Probable Without Eliminating the Irrational: Voting Over Intransitive Domains. UAI 2014: 182-191 - 2013
- [j2]Krishnendu Chatterjee, Monika Henzinger, Manas Joglekar, Nisarg Shah:
Symbolic algorithms for qualitative analysis of Markov decision processes with Büchi objectives. Formal Methods Syst. Des. 42(3): 301-327 (2013) - [c9]Ian A. Kash, Ariel D. Procaccia, Nisarg Shah:
No agent left behind: dynamic fair division of multiple resources. AAMAS 2013: 351-358 - [c8]Albert Xin Jiang, Ariel D. Procaccia, Yundi Qian, Nisarg Shah, Milind Tambe:
Defender (Mis)coordination in Security Games. IJCAI 2013: 220-226 - [c7]Yoram Bachrach, Nisarg Shah:
Reliability Weighted Voting Games. SAGT 2013: 38-49 - [c6]Ioannis Caragiannis, Ariel D. Procaccia, Nisarg Shah:
When do noisy votes reveal the truth? EC 2013: 143-160 - 2012
- [j1]Manas Joglekar, Nisarg Shah, Ajit A. Diwan:
Balanced group-labeled graphs. Discret. Math. 312(9): 1542-1549 (2012) - [c5]Krishnendu Chatterjee, Manas Joglekar, Nisarg Shah:
Average Case Analysis of the Classical Algorithm for Markov Decision Processes with Büchi Objectives. FSTTCS 2012: 461-473 - [c4]David C. Parkes, Ariel D. Procaccia, Nisarg Shah:
Beyond dominant resource fairness: extensions, limitations, and indivisibilities. EC 2012: 808-825 - [c3]Ariel D. Procaccia, Sashank Jakkam Reddi, Nisarg Shah:
A Maximum Likelihood Approach For Selecting Sets of Alternatives. UAI 2012: 695-704 - [c2]Yoram Bachrach, Ian A. Kash, Nisarg Shah:
Agent Failures in Totally Balanced Games and Convex Games. WINE 2012: 15-29 - [i3]Krishnendu Chatterjee, Manas Joglekar, Nisarg Shah:
Average Case Analysis of the Classical Algorithm for Markov Decision Processes with Büchi Objectives. CoRR abs/1202.4175 (2012) - [i2]Ariel D. Procaccia, Sashank Jakkam Reddi, Nisarg Shah:
A Maximum Likelihood Approach For Selecting Sets of Alternatives. CoRR abs/1210.4882 (2012) - 2011
- [c1]Krishnendu Chatterjee, Monika Henzinger, Manas Joglekar, Nisarg Shah:
Symbolic Algorithms for Qualitative Analysis of Markov Decision Processes with Büchi Objectives. CAV 2011: 260-276 - [i1]Krishnendu Chatterjee, Monika Henzinger, Manas Joglekar, Nisarg Shah:
Symbolic Algorithms for Qualitative Analysis of Markov Decision Processes with Büchi Objectives. CoRR abs/1104.3348 (2011)
Coauthor Index
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