Summary of Concept Drift Detection Using Ensemble Of Integrally Private Models, by Ayush K. Varshney et al.
Concept Drift Detection using Ensemble of Integrally Private Models
by Ayush K. Varshney, Vicenc Torra
First submitted to arxiv on: 7 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR)
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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 Deep neural networks (DNNs) are widely used in machine learning, but they require labeled training data. However, many real-world problems involve streaming data with scarce and expensive labels. This paper focuses on the privacy implications of detecting concept drifts in such data, where distributions change frequently. Existing models use methods like ADWIN and KSWIN to detect these changes. This paper introduces an ensemble method called “Integrally Private Drift Detection” (IPDD) that can detect concept drift without requiring labels. The IPDD method uses integrally private DNNs, which recur frequently from different datasets. Experimental results on binary and multi-class synthetic and real-world data show that IPDD can privately detect concept drift with comparable or better utility compared to ADWIN, while outperforming differentially private models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how to use special kinds of computer programs called neural networks to detect changes in large amounts of data. These changes are like surprises in the way the data is behaving. The problem is that finding these surprises requires a lot of labeled information, which can be hard or expensive to get. This paper wants to find a way to do it without needing all those labels. They created a new method called “Integrally Private Drift Detection” (IPDD) that can detect these changes without requiring labels. It’s like having a special tool that can spot changes in the data and let you know when something is different. |
Keywords
» Artificial intelligence » Machine learning
