Summary of Early Time Classification with Accumulated Accuracy Gap Control, by Liran Ringel et al.
Early Time Classification with Accumulated Accuracy Gap Control
by Liran Ringel, Regev Cohen, Daniel Freedman, Michael Elad, Yaniv Romano
First submitted to arxiv on: 1 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces a statistical framework for early time classification algorithms, enabling accurate labeling without processing the full input stream. The calibrated stopping rule is applicable to any sequential classifier and achieves finite-sample, distribution-free control of the accuracy gap between full and early-time classification. Building on the Learn-then-Test calibration framework, the method controls the marginally averaged accuracy gap over i.i.d. instances. To address the issue of excessively high accuracy gaps for early halt times, the authors propose a framework controlling a stronger notion of error conditionally on accumulated halt times. Numerical experiments demonstrate the effectiveness and usefulness of this approach, which reduces up to 94% of timesteps used for classification while maintaining rigorous accuracy gap control. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making computers better at quickly guessing what something is or doing based on a small part of the information. Right now, some algorithms can do this but they might not be as good as others that look at everything first. The authors came up with a new way to make sure these quick guesses are accurate by using math and statistics. This helps computers use less time and energy while still getting things right. They tested their idea and it worked really well, reducing the time needed to get the answer by up to 94%. |
Keywords
* Artificial intelligence * Classification
