无需调参的去中心化在线学习算法，实现可证明的低累积误差。
Decentralized Parameter-Free Online Learning
- 基于博弈机制与消息传递设计新算法
- 无需调参即达次线性网络后悔值
- 适合分布式传感与协同机器学习场景
我们提出首个具备网络后悔界保证的无参数去中心化在线学习算法，无需超参数调优即可实现次线性后悔。该算法通过消息传递（gossip steps）将多智能体博弈与去中心化在线学习相连接。为支持去中心化分析，我们引入一种新颖的“投注函数”形式化方法，简化多智能体后悔分析。理论分析表明其具有次线性网络后悔上界，并在合成与真实数据集上得到实验验证。该算法适用于分布式感知、去中心化优化和协同机器学习等场景。
原文摘要 · Abstract (English)
We propose the first parameter-free decentralized online learning algorithms with network regret guarantees, which achieve sublinear regret without requiring hyperparameter tuning. This family of algorithms connects multi-agent coin-betting and decentralized online learning via gossip steps. To enable our decentralized analysis, we introduce a novel "betting function" formulation for coin-betting that simplifies the multi-agent regret analysis. Our analysis shows sublinear network regret bounds and is validated through experiments on synthetic and real datasets. This family of algorithms is applicable to distributed sensing, decentralized optimization, and collaborative ML applications.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。