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ORCIDNihar B. Shah
Nihar Bhadresh Shah
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- affiliation: Carnegie Mellon University, Pittsburgh, PA, USA
- affiliation (former): University of California, Berkeley, CA, USA
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2020 – today
- 2024
[i73]Steven Jecmen, Nihar B. Shah, Fei Fang, Leman Akoglu:
On the Detection of Reviewer-Author Collusion Rings From Paper Bidding. CoRR abs/2402.07860 (2024)- [i72]Charvi Rastogi, Xiangchen Song, Zhijing Jin, Ivan Stelmakh, Hal Daumé III, Kun Zhang, Nihar B. Shah:
A Randomized Controlled Trial on Anonymizing Reviewers to Each Other in Peer Review Discussions. CoRR abs/2403.01015 (2024) - [i71]Ilia Kuznetsov, Osama Mohammed Afzal, Koen Dercksen, Nils Dycke, Alexander Goldberg, Tom Hope, Dirk Hovy, Jonathan K. Kummerfeld, Anne Lauscher, Kevin Leyton-Brown, Sheng Lu, Mausam, Margot Mieskes, Aurélie Névéol, Danish Pruthi, Lizhen Qu, Roy Schwartz, Noah A. Smith, Thamar Solorio, Jingyan Wang, Xiaodan Zhu, Anna Rogers, Nihar B. Shah, Iryna Gurevych:
What Can Natural Language Processing Do for Peer Review? CoRR abs/2405.06563 (2024) - [i70]John M. Carpenter, Andrea Corvillón, Nihar B. Shah:
Enhancing Peer Review in Astronomy: A Machine Learning and Optimization Approach to Reviewer Assignments for ALMA. CoRR abs/2410.10009 (2024) - [i69]Alexander Goldberg, Ihsan Ullah, Thanh Gia Hieu Khuong, Benedictus Kent Rachmat, Zhen Xu, Isabelle Guyon, Nihar B. Shah:
Usefulness of LLMs as an Author Checklist Assistant for Scientific Papers: NeurIPS'24 Experiment. CoRR abs/2411.03417 (2024) - [i68]Navita Goyal, Ivan Stelmakh, Nihar B. Shah, Hal Daumé III:
Causal Effect of Group Diversity on Redundancy and Coverage in Peer-Reviewing. CoRR abs/2411.11437 (2024) - [i67]Jhih-Yi Hsieh, Aditi Raghunathan, Nihar B. Shah:
Vulnerability of Text-Matching in ML/AI Conference Reviewer Assignments to Collusions. CoRR abs/2412.06606 (2024) - [i66]Iryna Gurevych, Anna Rogers, Nihar B. Shah, Jingyan Wang:
Reviewer No. 2: Old and New Problems in Peer Review (Dagstuhl Seminar 24052). Dagstuhl Reports 14(1): 130-161 (2024) - 2023
- [j30]Vijay Kamble
, Nihar B. Shah, David Marn, Abhay Parekh, Kannan Ramchandran:
The Square Root Agreement Rule for Incentivizing Truthful Feedback on Online Platforms. Manag. Sci. 69(1): 377-403 (2023) - [j29]Alexander Goldberg, Giulia Fanti, Nihar B. Shah:
Batching of Tasks by Users of Pseudonymous Forums: Anonymity Compromise and Protection. Proc. ACM Meas. Anal. Comput. Syst. 7(1): 22:1-22:41 (2023) - [j28]Joon Sik Kim, Valerie Chen, Danish Pruthi, Nihar B. Shah, Ameet Talwalkar:
Assisting Human Decisions in Document Matching. Trans. Mach. Learn. Res. 2023 (2023) - [c54]Martin Saveski, Steven Jecmen, Nihar B. Shah, Johan Ugander:
Counterfactual Evaluation of Peer-Review Assignment Policies. NeurIPS 2023 - [c53]Alexander Goldberg, Giulia Fanti, Nihar B. Shah:
Batching of Tasks by Users of Pseudonymous Forums: Anonymity Compromise and Protection. SIGMETRICS (Abstracts) 2023: 89-90 - [c52]Steven Jecmen, Minji Yoon, Vincent Conitzer, Nihar B. Shah, Fei Fang:
A Dataset on Malicious Paper Bidding in Peer Review. WWW 2023: 3816-3826 - [i65]Nihar B. Shah:
The Role of Author Identities in Peer Review. CoRR abs/2301.00221 (2023) - [i64]Joon Sik Kim, Valerie Chen, Danish Pruthi, Nihar B. Shah, Ameet Talwalkar:
Assisting Human Decisions in Document Matching. CoRR abs/2302.08450 (2023) - [i63]Ivan Stelmakh, John Wieting, Graham Neubig, Nihar B. Shah:
A Gold Standard Dataset for the Reviewer Assignment Problem. CoRR abs/2303.16750 (2023) - [i62]Martin Saveski, Steven Jecmen, Nihar B. Shah, Johan Ugander:
Counterfactual Evaluation of Peer-Review Assignment Policies. CoRR abs/2305.17339 (2023) - [i61]Ryan Liu, Nihar B. Shah:
ReviewerGPT? An Exploratory Study on Using Large Language Models for Paper Reviewing. CoRR abs/2306.00622 (2023) - [i60]Ryan Liu, Steven Jecmen, Vincent Conitzer, Fei Fang, Nihar B. Shah:
Testing for Reviewer Anchoring in Peer Review: A Randomized Controlled Trial. CoRR abs/2307.05443 (2023) - [i59]Alexander Goldberg, Ivan Stelmakh, Kyunghyun Cho, Alice H. Oh, Alekh Agarwal, Danielle Belgrave, Nihar B. Shah:
Peer Reviews of Peer Reviews: A Randomized Controlled Trial and Other Experiments. CoRR abs/2311.09497 (2023) - 2022
- [j27]Nihar B. Shah:
Challenges, experiments, and computational solutions in peer review. Commun. ACM 65(6): 76-87 (2022) - [j26]Charvi Rastogi, Sivaraman Balakrishnan, Nihar B. Shah, Aarti Singh:
Two-Sample Testing on Ranked Preference Data and the Role of Modeling Assumptions. J. Mach. Learn. Res. 23: 225:1-225:48 (2022) - [j25]Kangwook Lee, Nihar B. Shah, Longbo Huang, Kannan Ramchandran:
Addendum and Erratum to "The MDS Queue: Analysing the Latency Performance of Erasure Codes". IEEE Trans. Inf. Theory 68(9): 5850-5851 (2022) - [j24]Yusha Liu, Yichong Xu, Nihar B. Shah, Aarti Singh:
Integrating Rankings into Quantized Scores in Peer Review. Trans. Mach. Learn. Res. 2022 (2022) - [c51]Steven Jecmen, Hanrui Zhang, Ryan Liu, Fei Fang, Vincent Conitzer, Nihar B. Shah:
Near-Optimal Reviewer Splitting in Two-Phase Paper Reviewing and Conference Experiment Design. AAMAS 2022: 1642-1644 - [c50]Komal Dhull, Steven Jecmen, Pravesh Kothari, Nihar B. Shah:
Strategyproofing Peer Assessment via Partitioning: The Price in Terms of Evaluators' Expertise. HCOMP 2022: 53-63 - [c49]Steven Jecmen, Hanrui Zhang, Ryan Liu, Fei Fang, Vincent Conitzer, Nihar B. Shah:
Near-Optimal Reviewer Splitting in Two-Phase Paper Reviewing and Conference Experiment Design. HCOMP 2022: 102-113 - [c48]Jingyan Wang, Carmel Baharav, Nihar B. Shah, Anita Williams Woolley, R. Ravi:
Allocation Schemes in Analytic Evaluation: Applicant-Centric Holistic or Attribute-Centric Segmented? HCOMP 2022: 207-218 - [c47]Wenxin Ding, Gautam Kamath, Weina Wang, Nihar B. Shah:
Calibration with Privacy in Peer Review. ISIT 2022: 1635-1640 - [c46]Charvi Rastogi, Ivan Stelmakh, Nihar B. Shah, Sivaraman Balakrishnan:
No Rose for MLE: Inadmissibility of MLE for Evaluation Aggregation Under Levels of Expertise. ISIT 2022: 3168-3173 - [i58]Komal Dhull, Steven Jecmen, Pravesh Kothari, Nihar B. Shah:
The Price of Strategyproofing Peer Assessment. CoRR abs/2201.10631 (2022) - [i57]Wenxin Ding, Gautam Kamath, Weina Wang, Nihar B. Shah:
Calibration with Privacy in Peer Review. CoRR abs/2201.11308 (2022) - [i56]Ivan Stelmakh, Charvi Rastogi, Ryan Liu, Shuchi Chawla, Federico Echenique, Nihar B. Shah:
Cite-seeing and Reviewing: A Study on Citation Bias in Peer Review. CoRR abs/2203.17239 (2022) - [i55]Charvi Rastogi, Ivan Stelmakh, Xinwei Shen, Marina Meila, Federico Echenique, Shuchi Chawla, Nihar B. Shah:
To ArXiv or not to ArXiv: A Study Quantifying Pros and Cons of Posting Preprints Online. CoRR abs/2203.17259 (2022) - [i54]Yusha Liu, Yichong Xu, Nihar B. Shah, Aarti Singh:
Integrating Rankings into Quantized Scores in Peer Review. CoRR abs/2204.03505 (2022) - [i53]Steven Jecmen, Minji Yoon, Vincent Conitzer, Nihar B. Shah, Fei Fang:
A Dataset on Malicious Paper Bidding in Peer Review. CoRR abs/2207.02303 (2022) - [i52]Steven Jecmen, Nihar B. Shah, Fei Fang, Vincent Conitzer:
Tradeoffs in Preventing Manipulation in Paper Bidding for Reviewer Assignment. CoRR abs/2207.11315 (2022) - [i51]Jingyan Wang, Carmel Baharav, Nihar B. Shah, Anita Williams Woolley, R. Ravi:
Allocation Schemes in Analytic Evaluation: Applicant-Centric Holistic or Attribute-Centric Segmented? CoRR abs/2209.08665 (2022) - [i50]Alexander Goldberg, Giulia Fanti, Nihar B. Shah:
Batching of Tasks by Users of Pseudonymous Forums: Anonymity Compromise and Protection. CoRR abs/2211.12686 (2022) - [i49]Charvi Rastogi, Ivan Stelmakh, Alina Beygelzimer, Yann N. Dauphin, Percy Liang, Jennifer Wortman Vaughan, Zhenyu Xue, Hal Daumé III, Emma Pierson, Nihar B. Shah:
How do Authors' Perceptions of their Papers Compare with Co-authors' Perceptions and Peer-review Decisions? CoRR abs/2211.12966 (2022) - 2021
- [j23]Ritesh Noothigattu, Nihar B. Shah, Ariel D. Procaccia:
Loss Functions, Axioms, and Peer Review. J. Artif. Intell. Res. 70: 1481-1515 (2021) - [j22]Ivan Stelmakh, Nihar B. Shah, Aarti Singh:
PeerReview4All: Fair and Accurate Reviewer Assignment in Peer Review. J. Mach. Learn. Res. 22: 163:1-163:66 (2021) - [j21]Ivan Stelmakh, Nihar B. Shah, Aarti Singh, Hal Daumé III:
Prior and Prejudice: The Novice Reviewers' Bias against Resubmissions in Conference Peer Review. Proc. ACM Hum. Comput. Interact. 5(CSCW1): 75:1-75:17 (2021) - [j20]Nihar B. Shah, Sivaraman Balakrishnan, Martin J. Wainwright:
A Permutation-Based Model for Crowd Labeling: Optimal Estimation and Robustness. IEEE Trans. Inf. Theory 67(6): 4162-4184 (2021) - [c45]Emaad A. Manzoor, Nihar B. Shah:
Uncovering Latent Biases in Text: Method and Application to Peer Review. AAAI 2021: 4767-4775 - [c44]Ivan Stelmakh, Nihar B. Shah, Aarti Singh, Hal Daumé III:
A Novice-Reviewer Experiment to Address Scarcity of Qualified Reviewers in Large Conferences. AAAI 2021: 4785-4793 - [c43]Ivan Stelmakh, Nihar B. Shah, Aarti Singh:
Catch Me if I Can: Detecting Strategic Behaviour in Peer Assessment. AAAI 2021: 4794-4802 - [c42]Jingyan Wang, Ivan Stelmakh, Yuting Wei, Nihar B. Shah:
Debiasing Evaluations That Are Biased by Evaluations. AAAI 2021: 10120-10128 - [c41]Nihar B. Shah:
JCDL 2021 Tutorial on Systemic Challenges and Computational Solutions on Bias and Unfairness in Peer Review. JCDL 2021: 356-357 - [c40]Nihar B. Shah:
KDD 2021 Tutorial on Systemic Challenges and Solutions on Bias and Unfairness in Peer Review. KDD 2021: 4066-4067 - [c39]Komal Dhull, Jingyan Wang, Nihar B. Shah, Yuanzhi Li, R. Ravi:
A heuristic for statistical seriation. UAI 2021: 621-631 - [c38]Nihar B. Shah:
WSDM 2021 Tutorial on Systematic Challenges and Computational Solutions on Bias and Unfairness in Peer Review. WSDM 2021: 1131-1133 - [i48]Steven Jecmen, Hanrui Zhang, Ryan Liu, Fei Fang, Vincent Conitzer, Nihar B. Shah:
Near-Optimal Reviewer Splitting in Two-Phase Paper Reviewing and Conference Experiment Design. CoRR abs/2108.06371 (2021) - 2020
- [j19]Nihar B. Shah, Dengyong Zhou:
Approval Voting and Incentives in Crowdsourcing. ACM Trans. Economics and Comput. 8(3): 13:1-13:40 (2020) - [c37]Jingyan Wang, Nihar B. Shah:
Ranking and Rating Rankings and Ratings. AAAI 2020: 13704-13707 - [c36]Jingyan Wang, Nihar B. Shah, R. Ravi:
Stretching the Effectiveness of MLE from Accuracy to Bias for Pairwise Comparisons. AISTATS 2020: 66-76 - [c35]Charvi Rastogi, Sivaraman Balakrishnan, Nihar B. Shah, Aarti Singh:
Two-Sample Testing on Pairwise Comparison Data and the Role of Modeling Assumptions. ISIT 2020: 1271-1276 - [c34]Steven Jecmen, Hanrui Zhang, Ryan Liu, Nihar B. Shah, Vincent Conitzer, Fei Fang:
Mitigating Manipulation in Peer Review via Randomized Reviewer Assignments. NeurIPS 2020 - [c33]Nihar B. Shah, Zachary C. Lipton:
SIGMOD 2020 Tutorial on Fairness and Bias in Peer Review and Other Sociotechnical Intelligent Systems. SIGMOD Conference 2020: 2637-2640 - [c32]Tanner Fiez, Nihar B. Shah, Lillian J. Ratliff:
A SUPER* Algorithm to Optimize Paper Bidding in Peer Review. UAI 2020: 580-589 - [i47]Charvi Rastogi, Sivaraman Balakrishnan, Nihar B. Shah, Aarti Singh:
Two-Sample Testing on Ranked Preference Data and the Role of Modeling Assumptions. CoRR abs/2006.11909 (2020) - [i46]Wenxin Ding, Nihar B. Shah, Weina Wang:
On the Privacy-Utility Tradeoff in Peer-Review Data Analysis. CoRR abs/2006.16385 (2020) - [i45]Steven Jecmen, Hanrui Zhang, Ryan Liu, Nihar B. Shah, Vincent Conitzer, Fei Fang:
Mitigating Manipulation in Peer Review via Randomized Reviewer Assignments. CoRR abs/2006.16437 (2020) - [i44]Tanner Fiez, Nihar B. Shah, Lillian J. Ratliff:
A SUPER* Algorithm to Optimize Paper Bidding in Peer Review. CoRR abs/2007.07079 (2020) - [i43]Ivan Stelmakh, Nihar B. Shah, Aarti Singh:
Catch Me if I Can: Detecting Strategic Behaviour in Peer Assessment. CoRR abs/2010.04041 (2020) - [i42]Emaad A. Manzoor, Nihar B. Shah:
Uncovering Latent Biases in Text: Method and Application to Peer Review. CoRR abs/2010.15300 (2020) - [i41]Ivan Stelmakh, Nihar B. Shah, Aarti Singh, Hal Daumé III:
Prior and Prejudice: The Novice Reviewers' Bias against Resubmissions in Conference Peer Review. CoRR abs/2011.14646 (2020) - [i40]Ivan Stelmakh, Nihar B. Shah, Aarti Singh, Hal Daumé III:
A Novice-Reviewer Experiment to Address Scarcity of Qualified Reviewers in Large Conferences. CoRR abs/2011.15050 (2020) - [i39]Ivan Stelmakh, Charvi Rastogi, Nihar B. Shah, Aarti Singh, Hal Daumé III:
A Large Scale Randomized Controlled Trial on Herding in Peer-Review Discussions. CoRR abs/2011.15083 (2020) - [i38]Jingyan Wang, Ivan Stelmakh, Yuting Wei, Nihar B. Shah:
Debiasing Evaluations That are Biased by Evaluations. CoRR abs/2012.00714 (2020)
2010 – 2019
- 2019
- [j18]Nihar B. Shah, Sivaraman Balakrishnan, Martin J. Wainwright:
Low Permutation-rank Matrices: Structural Properties and Noisy Completion. J. Mach. Learn. Res. 20: 101:1-101:43 (2019) - [j17]Nihar B. Shah, Sivaraman Balakrishnan, Martin J. Wainwright:
Feeling the Bern: Adaptive Estimators for Bernoulli Probabilities of Pairwise Comparisons. IEEE Trans. Inf. Theory 65(8): 4854-4874 (2019) - [c31]Ivan Stelmakh, Nihar B. Shah, Aarti Singh:
PeerReview4All: Fair and Accurate Reviewer Assignment in Peer Review. ALT 2019: 827-855 - [c30]Jingyan Wang, Nihar B. Shah:
Your 2 is My 1, Your 3 is My 9: Handling Arbitrary Miscalibrations in Ratings. AAMAS 2019: 864-872 - [c29]Yichong Xu, Han Zhao, Xiaofei Shi, Nihar B. Shah:
On Strategyproof Conference Peer Review. IJCAI 2019: 616-622 - [c28]Ivan Stelmakh, Nihar B. Shah, Aarti Singh:
On Testing for Biases in Peer Review. NeurIPS 2019: 5287-5297 - [i37]Jingyan Wang, Nihar B. Shah, R. Ravi:
Stretching the Effectiveness of MLE from Accuracy to Bias for Pairwise Comparisons. CoRR abs/1906.04066 (2019) - 2018
- [j16]Yuan Luo, Nihar B. Shah, Jianwei Huang, Jean C. Walrand:
Parametric Prediction from Parametric Agents. Oper. Res. 66(2): 313-326 (2018) - [j15]Nihar B. Shah, Behzad Tabibian, Krikamol Muandet, Isabelle Guyon, Ulrike von Luxburg:
Design and Analysis of the NIPS 2016 Review Process. J. Mach. Learn. Res. 19: 49:1-49:34 (2018) - [j14]K. V. Rashmi, Nihar B. Shah, Kannan Ramchandran, P. Vijay Kumar:
Information-Theoretically Secure Erasure Codes for Distributed Storage. IEEE Trans. Inf. Theory 64(3): 1621-1646 (2018) - [c27]Nihar B. Shah, Sivaraman Balakrishnan, Martin J. Wainwright:
Low Permutation-Rank Matrices: Structural Properties and Noisy Completion. ISIT 2018: 366-370 - [i36]Jingyan Wang, Nihar B. Shah:
Your 2 is My 1, Your 3 is My 9: Handling Arbitrary Miscalibrations in Ratings. CoRR abs/1806.05085 (2018) - [i35]Ivan Stelmakh, Nihar B. Shah, Aarti Singh:
PeerReview4All: Fair and Accurate Reviewer Assignment in Peer Review. CoRR abs/1806.06237 (2018) - [i34]Yichong Xu, Han Zhao, Xiaofei Shi, Nihar B. Shah:
On Strategyproof Conference Peer Review. CoRR abs/1806.06266 (2018) - [i33]Ritesh Noothigattu, Nihar B. Shah, Ariel D. Procaccia:
Choosing How to Choose Papers. CoRR abs/1808.09057 (2018) - [i32]Susu Xu, Weiguang Mao, Yue Cao, Hae Young Noh, Nihar B. Shah:
An Incentive Mechanism for Crowd Sensing with Colluding Agents. CoRR abs/1809.05161 (2018) - 2017
- [b1]Nihar Bhadresh Shah:
Learning From People. University of California, Berkeley, USA, 2017 - [j13]Nihar B. Shah, Martin J. Wainwright:
Simple, Robust and Optimal Ranking from Pairwise Comparisons. J. Mach. Learn. Res. 18: 199:1-199:38 (2017) - [j12]Nihar B. Shah, Sivaraman Balakrishnan, Adityanand Guntuboyina, Martin J. Wainwright:
Stochastically Transitive Models for Pairwise Comparisons: Statistical and Computational Issues. IEEE Trans. Inf. Theory 63(2): 934-959 (2017) - [j11]Kangwook Lee, Nihar B. Shah, Longbo Huang, Kannan Ramchandran:
The MDS Queue: Analysing the Latency Performance of Erasure Codes. IEEE Trans. Inf. Theory 63(5): 2822-2842 (2017) - [j10]K. V. Rashmi, Nihar B. Shah, Kannan Ramchandran:
A Piggybacking Design Framework for Read-and Download-Efficient Distributed Storage Codes. IEEE Trans. Inf. Theory 63(9): 5802-5820 (2017) - [i31]Nihar B. Shah, Behzad Tabibian, Krikamol Muandet, Isabelle Guyon, Ulrike von Luxburg:
Design and Analysis of the NIPS 2016 Review Process. CoRR abs/1708.09794 (2017) - [i30]Nihar B. Shah, Sivaraman Balakrishnan, Martin J. Wainwright:
Low Permutation-rank Matrices: Structural Properties and Noisy Completion. CoRR abs/1709.00127 (2017) - 2016
- [j9]Nihar B. Shah, Sivaraman Balakrishnan, Joseph K. Bradley, Abhay Parekh, Kannan Ramchandran, Martin J. Wainwright:
Estimation from Pairwise Comparisons: Sharp Minimax Bounds with Topology Dependence. J. Mach. Learn. Res. 17: 58:1-58:47 (2016) - [j8]Nihar B. Shah, Dengyong Zhou:
Double or Nothing: Multiplicative Incentive Mechanisms for Crowdsourcing. J. Mach. Learn. Res. 17: 165:1-165:52 (2016) - [j7]Nihar B. Shah, Kangwook Lee, Kannan Ramchandran:
When Do Redundant Requests Reduce Latency? IEEE Trans. Commun. 64(2): 715-722 (2016) - [c26]Baiyu Chen, Sergio Escalera, Isabelle Guyon, Víctor Ponce-López, Nihar B. Shah, Marc Oliu Simon:
Overcoming Calibration Problems in Pattern Labeling with Pairwise Ratings: Application to Personality Traits. ECCV Workshops (3) 2016: 419-432 - [c25]Nihar B. Shah, Dengyong Zhou:
No Oops, You Won't Do It Again: Mechanisms for Self-correction in Crowdsourcing. ICML 2016: 1-10 - [c24]Nihar B. Shah, Sivaraman Balakrishnan, Aditya Guntuboyina, Martin J. Wainwright:
Stochastically Transitive Models for Pairwise Comparisons: Statistical and Computational Issues. ICML 2016: 11-20 - [c23]Nihar B. Shah, Sivaraman Balakrishnan, Martin J. Wainwright:
Feeling the bern: Adaptive estimators for Bernoulli probabilities of pairwise comparisons. ISIT 2016: 1153-1157 - [i29]Yuan Luo, Nihar B. Shah, Jianwei Huang, Jean C. Walrand:
Parametric Prediction from Parametric Agents. CoRR abs/1602.07435 (2016) - [i28]Nihar B. Shah, Sivaraman Balakrishnan, Martin J. Wainwright:
Feeling the Bern: Adaptive Estimators for Bernoulli Probabilities of Pairwise Comparisons. CoRR abs/1603.06881 (2016) - [i27]Reinhard Heckel, Nihar B. Shah, Kannan Ramchandran, Martin J. Wainwright:
Active Ranking from Pairwise Comparisons and the Futility of Parametric Assumptions. CoRR abs/1606.08842 (2016) - [i26]Nihar B. Shah, Sivaraman Balakrishnan, Martin J. Wainwright:
A Permutation-based Model for Crowd Labeling: Optimal Estimation and Robustness. CoRR abs/1606.09632 (2016) - 2015
- [j6]Nihar B. Shah, K. V. Rashmi, Kannan Ramchandran:
Distributed Secret Dissemination Across a Network. IEEE J. Sel. Top. Signal Process. 9(7): 1206-1216 (2015) - [j5]Yuan Luo, Nihar B. Shah, Jianwei Huang, Jean C. Walrand:
Parametric Prediction from Parametric Agents? SIGMETRICS Perform. Evaluation Rev. 43(3): 57 (2015) - [c22]Nihar B. Shah, Dengyong Zhou:
On the Impossibility of Convex Inference in Human Computation. AAAI 2015: 1291-1297 - [c21]Nihar B. Shah, Sivaraman Balakrishnan, Joseph K. Bradley, Abhay Parekh, Kannan Ramchandran, Martin J. Wainwright:
Estimation from Pairwise Comparisons: Sharp Minimax Bounds with Topology Dependence. AISTATS 2015 - [c20]K. V. Rashmi, Preetum Nakkiran, Jingyan Wang, Nihar B. Shah, Kannan Ramchandran:
Having Your Cake and Eating It Too: Jointly Optimal Erasure Codes for I/O, Storage, and Network-bandwidth. FAST 2015: 81-94 - [c19]Nihar B. Shah, Dengyong Zhou, Yuval Peres:
Approval Voting and Incentives in Crowdsourcing. ICML 2015: 10-19 - [c18]Nihar Bhadresh Shah, Denny Zhou:
Double or Nothing: Multiplicative Incentive Mechanisms for Crowdsourcing. NIPS 2015: 1-9 - [i25]Nihar B. Shah, Dengyong Zhou, Yuval Peres:
Approval Voting and Incentives in Crowdsourcing. CoRR abs/1502.05696 (2015) - [i24]Dengyong Zhou, Qiang Liu, John C. Platt, Christopher Meek, Nihar B. Shah:
Regularized Minimax Conditional Entropy for Crowdsourcing. CoRR abs/1503.07240 (2015) - [i23]Nihar B. Shah, Sivaraman Balakrishnan, Joseph K. Bradley, Abhay Parekh, Kannan Ramchandran, Martin J. Wainwright:
Estimation from Pairwise Comparisons: Sharp Minimax Bounds with Topology Dependence. CoRR abs/1505.01462 (2015) - [i22]Vijay Kamble, Nihar B. Shah, David Marn, Abhay Parekh, Kannan Ramchandran:
Truth Serums for Massively Crowdsourced Evaluation Tasks. CoRR abs/1507.07045 (2015) - [i21]Nihar B. Shah, K. V. Rashmi, Kannan Ramchandran, P. Vijay Kumar:
Information-theoretically Secure Erasure Codes for Distributed Storage. CoRR abs/1508.03787 (2015) - [i20]Nihar B. Shah, Sivaraman Balakrishnan, Adityanand Guntuboyina, Martin J. Wainwright:
Stochastically Transitive Models for Pairwise Comparisons: Statistical and Computational Issues. CoRR abs/1510.05610 (2015) - [i19]