A Tale Of Two Long Tails
arXiv:2107.13098
Abstract
As machine learning models are increasingly employed to assist human decision-makers, it becomes critical to communicate the uncertainty associated with these model predictions. However, the majority of work on uncertainty has focused on traditional probabilistic or ranking approaches - where the model assigns low probabilities or scores to uncertain examples. While this captures what examples are challenging for the model, it does not capture the underlying source of the uncertainty. In this work, we seek to identify examples the model is uncertain about and characterize the source of said uncertainty. We explore the benefits of designing a targeted intervention - targeted data augmentation of the examples where the model is uncertain over the course of training. We investigate whether the rate of learning in the presence of additional information differs between atypical and noisy examples? Our results show that this is indeed the case, suggesting that well-designed interventions over the course of training can be an effective way to characterize and distinguish between different sources of uncertainty.
Preliminary results accepted to Workshop on Uncertainty and Robustness in Deep Learning (UDL), ICML, 2021
References in corpus (12)
- Understanding deep learning requires rethinking generalization
- Prototype selection for interpretable classification
- Gradient Descent with Early Stopping is Provably Robust to Label Noise for Overparameterized Neural Networks
- Are we done with ImageNet?
- Revisiting the Calibration of Modern Neural Networks
- Probabilistic End-to-end Noise Correction for Learning with Noisy Labels
- From ImageNet to Image Classification: Contextualizing Progress on Benchmarks
- Distribution Density, Tails, and Outliers in Machine Learning: Metrics and Applications
- A Study of Gradient Variance in Deep Learning
- Algorithmic Bias and Data Bias: Understanding the Relation between Distributionally Robust Optimization and Data Curation
- The Benchmark Lottery
- When does loss-based prioritization fail?