Summary of Spurious Correlations in Concept Drift: Can Explanatory Interaction Help?, by Cristiana Lalletti and Stefano Teso
Spurious Correlations in Concept Drift: Can Explanatory Interaction Help?
by Cristiana Lalletti, Stefano Teso
First submitted to arxiv on: 23 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper addresses a pressing issue in machine learning: concept drift (CD), where the data distribution changes over time, affecting prediction performance. The authors show that traditional detection methods can be fooled by spurious correlations (SCs). To tackle this challenge, they introduce ebc-exstream, a novel detector that incorporates model explanations and human feedback to correct for SCs. The algorithm uses an entropy-based heuristic to minimize the need for annotation, reducing costs. Preliminary experiments on artificially confounded data demonstrate the potential of ebc-exstream in mitigating the impact of SCs on detection. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A machine learning paper has found a way to fix a big problem: when models get old and stop working well because the data changes over time. This is called concept drift, and it’s like trying to predict what will happen tomorrow based on yesterday’s weather. To make sure this doesn’t happen, we need to detect when the data is changing. But sometimes, there are fake clues that can trick our detectors into thinking something is happening when it’s not. The authors of this paper have created a new way to detect these fake clues and fix them, making their models better at handling changes in the data. |
Keywords
* Artificial intelligence * Machine learning
