Summary of Learning to Help: Training Models to Assist Legacy Devices, by Yu Wu et al.
Learning To Help: Training Models to Assist Legacy Devices
by Yu Wu, Anand Sarwate
First submitted to arxiv on: 24 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 The paper proposes a novel approach to address the issue of deploying machine learning models on legacy devices with limited computational capabilities. By formalizing the problem in the framework of Learning with Abstention (LWA), the authors develop a method to train an edge cloud as an expert that assists legacy devices. The key innovation lies in training the expert for a fixed, legacy client rather than assuming the edge is either an oracle or human expert. The paper derives the Bayes-optimal rejection rule, establishes a generalization bound, and finds a consistent surrogate loss function. Empirical results demonstrate that this approach outperforms confidence-based rejection rules. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Legacy devices can be outdated with limited computational abilities. This makes it difficult to deploy machine learning models on these devices. The authors suggest offloading some computation to an edge cloud to help legacy devices. They use a framework called Learning with Abstention (LWA) where the expert (edge) helps the client (device). In this work, they train the expert for a fixed client. This is different from previous LWA work that assumes the edge is either very smart or human-like. The authors find the best way to decide when to offload inference, prove that their method works well, and show how to use it in practice. |
Keywords
» Artificial intelligence » Generalization » Inference » Loss function » Machine learning
