587 citations · 2.1k across the 58 of their papers we have counts for
9 papers · 1 filter
Distributed Training using an Intelligent Network
Nihar Shah, Ben Blier
Distributed training across a wide area network (WAN) is challenging, as continuous parameter exchange by islands of compute is constrained by limited bandwidth, high latency, and…
Assisting Human Decisions in Document Matching
Joon Sik Kim, Valerie Chen, Danish Pruthi +2
Many practical applications, ranging from paper-reviewer assignment in peer review to job-applicant matching for hiring, require human decision makers to identify relevant matches…
How do Authors' Perceptions of their Papers Compare with Co-authors' Perceptions and Peer-review Decisions?
Charvi Rastogi, Ivan Stelmakh, Alina Beygelzimer +7
How do author perceptions match up to the outcomes of the peer-review process and perceptions of others? In a top-tier computer science conference (NeurIPS 2021) with more than 23,…
A Permutation-based Model for Crowd Labeling: Optimal Estimation and Robustness
Nihar B. Shah, Sivaraman Balakrishnan, Martin J. Wainwright
The task of aggregating and denoising crowd-labeled data has gained increased significance with the advent of crowdsourcing platforms and massive datasets. We propose a permutation…
Active Ranking from Pairwise Comparisons and when Parametric Assumptions Don't Help
Reinhard Heckel, Nihar B. Shah, Kannan Ramchandran +1
We consider sequential or active ranking of a set of n items based on noisy pairwise comparisons. Items are ranked according to the probability that a given item beats a randomly c…
Feeling the Bern: Adaptive Estimators for Bernoulli Probabilities of Pairwise Comparisons
Nihar B. Shah, Sivaraman Balakrishnan, Martin J. Wainwright
We study methods for aggregating pairwise comparison data in order to estimate outcome probabilities for future comparisons among a collection of n items. Working within a flexible…