社交网络中交互的分布式学习
Distributed Learning from Interactions in Social Networks
AI总结：
本文提出基于社交网络交互的分布式学习框架，利用贝叶斯方法和最大似然估计，通过图模型工具实现参数和超参数的分布式估计，用于用户画像建模。
摘要：
我们考虑一个网络场景，其中代理可以根据表示某些交互的评分图来评估彼此。目标是设计一个分布式协议，由代理运行，使他们能够在有限的可能值中学习其未知状态。我们提出一个贝叶斯框架，其中评分和状态与具有未知参数和超参数的概率事件相关联。我们展示每个代理可以通过本地贝叶斯分类器和结合普通最大似然估计和经验贝叶斯方法的（集中式）最大似然（ML）估计器来学习其状态。通过使用图模型工具，我们可以获得评分和状态的条件依赖性的洞察，从而提供一个放松的概率模型，最终导致一个适合分布式计算的参数-超参数估计器。为了突出所提放松的适当性，我们将在社交互动设置中演示分布式估计器。
英文摘要：
We consider a network scenario in which agents can evaluate each other according to a score graph that models some interactions. The goal is to design a distributed protocol, run by the agents, that allows them to learn their unknown state among a finite set of possible values. We propose a Bayesian framework in which scores and states are associated to probabilistic events with unknown parameters and hyperparameters, respectively. We show that each agent can learn its state by means of a local Bayesian classifier and a (centralized) Maximum-Likelihood (ML) estimator of parameter-hyperparameter that combines plain ML and Empirical Bayes approaches. By using tools from graphical models, which allow us to gain insight on conditional dependencies of scores and states, we provide a relaxed probabilistic model that ultimately leads to a parameter-hyperparameter estimator amenable to distributed computation. To highlight the appropriateness of the proposed relaxation, we demonstrate the distributed estimators on a social interaction set-up for user profiling.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
