6 citations · 11 across the 8 of their papers we have counts for
11 papers · 1 filter
The Approximation Ratio for the Risk of Myopic Bayesian Active Learning for Linear Regression
Stephen Mussmann
Active learning studies the fundamental question: what data should we choose to observe? The greedy algorithm in optimal experiment design is a common heuristic and also equivalent…
Instance-Level Costs for Nuanced Classifier Evaluation
Kabir Kang, Stephen Mussmann
Standard classification treats all errors equally, but in applications such as content moderation and medical screening, mistakes on clear-cut cases are more costly than errors on…
Batch Bayesian Active Learning with Partial Batch Label Sampling
Kangping Hu, Stephen Mussmann
Over the past couple of decades, many active learning acquisition functions have been proposed, leaving practitioners with an unclear choice of which to use. Bayesian-based active…
LabelBench: A Comprehensive Framework for Benchmarking Adaptive Label-Efficient Learning
Jifan Zhang, Yifang Chen, Gregory Canal +8
Labeled data are critical to modern machine learning applications, but obtaining labels can be expensive. To mitigate this cost, machine learning methods, such as transfer learning…
Active Learning with Expected Error Reduction
Stephen Mussmann, Julia Reisler, Daniel Tsai +3
Active learning has been studied extensively as a method for efficient data collection. Among the many approaches in literature, Expected Error Reduction (EER) (Roy and McCallum) h…
Comparing the Value of Labeled and Unlabeled Data in Method-of-Moments Latent Variable Estimation
Mayee F. Chen, Benjamin Cohen-Wang, Stephen Mussmann +2
Labeling data for modern machine learning is expensive and time-consuming. Latent variable models can be used to infer labels from weaker, easier-to-acquire sources operating on un…