Summary of Minimal Communication-cost Statistical Learning, by Milad Sefidgaran et al.
Minimal Communication-Cost Statistical Learning
by Milad Sefidgaran, Abdellatif Zaidi, Piotr Krasnowski
First submitted to arxiv on: 12 Jun 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Information Theory (cs.IT); Machine Learning (cs.LG)
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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 joint training and source coding scheme to enable a client device to send a statistical hypothesis or model W to a remote server while meeting two design criteria: small population risk during inference and small complexity for efficient communication. The approach uses a constraint on Kullback-Leibler divergence between the conditional distribution of a compressed learning model and the prior to guarantee simultaneously low average empirical risk, generalization error, and communication cost. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A device needs to send a statistical model W to a server while meeting two goals: keeping the population risk small during inference and minimizing communication costs. The paper offers a solution that balances these goals by controlling the distance between the compressed model’s distribution and the prior distribution. This approach ensures low average training loss, generalization error, and communication cost. |
Keywords
» Artificial intelligence » Generalization » Inference » Statistical model
