Summary of Federated Automatic Latent Variable Selection in Multi-output Gaussian Processes, by Jingyi Gao et al.
Federated Automatic Latent Variable Selection in Multi-output Gaussian Processes
by Jingyi Gao, Seokhyun Chung
First submitted to arxiv on: 24 Jul 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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 proposes a novel federated learning approach that automatically selects the number of latent processes in multi-output Gaussian processes (MGPs). This approach addresses challenges in determining the adequate number of latent processes and relying on centralized learning, which poses privacy risks and computational burdens. The hierarchical model uses spike-and-slab priors to shrink unnecessary coefficients to zero, while variational inference-based optimization allows units to jointly select common latent processes without sharing data. The algorithm is demonstrated through simulation and case studies on Li-ion battery degradation and air temperature data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper develops a new way for machines to learn together without sharing their information. It’s like a team project where everyone contributes their ideas, but nobody shares their work. This helps protect privacy and saves computer power. The method uses special math formulas to figure out which patterns are common across different teams. It works well with real-world data, like battery performance or air temperature. |
Keywords
» Artificial intelligence » Federated learning » Inference » Optimization » Temperature
