Author

Nihar Shah

ML and CS departments, Carnegie Mellon University - Cited by 6,007 - machine learning - statistics - social choice - peer review

Biography

Nihar Shah is currently working as a ML and CS departments, Carnegie Mellon University; he extended his valuable service in field of Pharmacology and Toxicology for several years and has been a recipient of few awards and grants. His research interests reflect in his wide range of publications in various national and international journals.
Title
Cited by
Year
A permutation-based model for crowd labeling: Optimal estimation and robustness
NB Shah, S Balakrishnan, MJ WainwrightIEEE Transactions on Information Theory 67 (6), 4162-4184, 2020202
72
2020
PeerReview4All: Fair and accurate reviewer assignment in peer review
I Stelmakh, NB Shah, A SinghAlgorithmic Learning Theory, 828-856, 2019201
69
2019
Mitigating manipulation in peer review via randomized reviewer assignments
S Jecmen, H Zhang, R Liu, N Shah, V Conitzer, F FangAdvances in Neural Information Processing Systems 33, 123-12545, 2020202
53
2020
On Testing for Biases in Peer Review
I Stelmakh, N Shah, A Singhhttp://www.cs.cmu.edu/afs/cs.cmu.edu/user/istelmak/www/papers/bias.pdf, 2019201
51
2019
Feeling the Bern: Adaptive estimators for Bernoulli probabilities of pairwise comparisons
NB Shah, S Balakrishnan, MJ WainwrightIEEE Transactions on Information Theory 65 (8), 4854-4874, 2019201
51
2019
Challenges, experiments, and computational solutions in peer review
NB ShahCommunications of the ACM 65 (6), 76-87, 202249202
49
2022
A SUPER* Algorithm to Optimize Paper Bidding in Peer Review
T Fiez, N Shah, L Ratliffhttps://realworld-sdm.github.io/paper/38.pdf, 2019201
42
2019
Loss functions, axioms, and peer review
R Noothigattu, N Shah, A ProcacciaJournal of Artificial Intelligence Research 70, 1481-1515, 2021202
39
2021
Uncovering latent biases in text: Method and application to peer review
E Manzoor, NB ShahProceedings of the AAAI Conference on Artificial Intelligence 35 (6), 4767-4775, 2021202
30
2021
Catch me if i can: Detecting strategic behaviour in peer assessment
I Stelmakh, NB Shah, A SinghProceedings of the AAAI Conference on Artificial Intelligence 35 (6), 4794-4802, 2021202
29
2021
Low permutation-rank matrices: Structural properties and noisy completion
N Shah, S Balakrishnan, M WainwrightJournal of machine learning research, 2019201
26
2019
A novice-reviewer experiment to address scarcity of qualified reviewers in large conferences
I Stelmakh, NB Shah, A Singh, H Daumé IIIProceedings of the AAAI Conference on Artificial Intelligence 35 (6), 4785-4793, 2021202
25
2021
Debiasing evaluations that are biased by evaluations
J Wang, I Stelmakh, Y Wei, NB ShahProceedings of the AAAI Conference on Artificial Intelligence 35 (11), 10120 …, 2021202
18
2021
PeerReview4All: Fair and accurate reviewer assignment in peer review
I Stelmakh, N Shah, A SinghThe Journal of Machine Learning Research 22 (1), 7393-7458, 2021202
16
2021
A large scale randomized controlled trial on herding in peer-review discussions
I Stelmakh, C Rastogi, NB Shah, A Singh, H Daumé IIIPlos one 18 (7), e0287443, 2023202
15
2023
Sigmod 2020 tutorial on fairness and bias in peer review and other sociotechnical intelligent systems
NB Shah, Z LiptonProceedings of the 2020 ACM SIGMOD International Conference on Management of …, 202013202
13
2020
Strategyproofing Peer Assessment via Partitioning: The Price in Terms of Evaluators’ Expertise
K Dhull, S Jecmen, P Kothari, NB ShaharXiv preprint arXiv:2201.10631, 202213202
13
2022
On the privacy-utility tradeoff in peer-review data analysis
W Ding, NB Shah, W WangarXiv preprint arXiv:2006.16385, 2020202
12
2020
Stretching the effectiveness of MLE from accuracy to bias for pairwise comparisons
J Wang, N Shah, R RaviInternational Conference on Artificial Intelligence and Statistics, 66-76, 2020202
8
2020
Principled methods to improve peer review
NB ShahRetrieved May 27, 2020, 2019201
8
2019

Referred From: https://scholar.google.co.in/citations?user=BF39lMQAAAAJ&hl=en

Cited by
Citations 6007
h-index 38
i10-index 57
Recommended Journals
Open Access Journals