6 citations · 11 across the 3 of their papers we have counts for
11 papers
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…
On the Importance of Adaptive Data Collection for Extremely Imbalanced Pairwise Tasks
Stephen Mussmann, Robin Jia, Percy Liang
Many pairwise classification tasks, such as paraphrase detection and open-domain question answering, naturally have extreme label imbalance (e.g., of examples are negativ…
Concept Bottleneck Models
Pang Wei Koh, Thao Nguyen, Yew Siang Tang +4
We seek to learn models that we can interact with using high-level concepts: if the model did not think there was a bone spur in the x-ray, would it still predict severe arthritis?…
Selection via Proxy: Efficient Data Selection for Deep Learning
Cody Coleman, Christopher Yeh, Stephen Mussmann +5
Data selection methods, such as active learning and core-set selection, are useful tools for machine learning on large datasets. However, they can be prohibitively expensive to app…
A Tight Analysis of Greedy Yields Subexponential Time Approximation for Uniform Decision Tree
Ray Li, Percy Liang, Stephen Mussmann
Decision Tree is a classic formulation of active learning: given hypotheses with nonnegative weights summing to 1 and a set of tests that each partition the hypotheses, output…