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20222026
most citedPromises and Pitfalls of Threshold-based Auto-labeling

2 citations · 3 across the 5 of their papers we have counts for

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cs.LG2022★ 2 cited

Promises and Pitfalls of Threshold-based Auto-labeling

Harit Vishwakarma, Heguang Lin, Frederic Sala +1

Creating large-scale high-quality labeled datasets is a major bottleneck in supervised machine learning workflows. Threshold-based auto-labeling (TBAL), where validation data obtai…

stat.ML2022

Geometry of the Minimum Volume Confidence Sets

Heguang Lin, Mengze Li, Daniel Pimentel-Alarcón +1

Computation of confidence sets is central to data science and machine learning, serving as the workhorse of A/B testing and underpinning the operation and analysis of reinforcement…