Monitoring a population of dependent processes under limited resources is
critical for abnormal events detection. A novel online collaborative learning
method is proposed to adaptively allocate the resources for exploitation of
high-risk processes and exploration of dependent dynamics. Efficiency of the
proposed method is proved through theoretical analysis and experiments.

基于有限资源监控依赖进程的人群是异常事件检测的关键。提出了一种新的在线协作学习方法，通过理论分析和实验证明了该方法的效率。

资源约束下的相关过程在线建模与监控

Online Modeling and Monitoring of Dependent Processes under Resource  Constraints

Measuring contributions is a classical problem in cooperative game theory
where the Shapley value is the most well-known solution concept. In this paper,
we establish the convergence property of the Shapley value in parametric
Bayesian learning games where players perform a Bayesian inference using their
combined data, and the posterior-prior KL divergence is used as the
characteristic function. We show that for any two players, under some
regularity conditions, their difference in Shapley value converges in
probability to the difference in Shapley value of a limiting game whose
characteristic function is proportional to the log-determinant of the joint
Fisher information. As an application, we present an online collaborative
learning framework that is asymptotically Shapley-fair. Our result enables this
to be achieved without any costly computations of posterior-prior KL
divergences. Only a consistent estimator of the Fisher information is needed.
The effectiveness of our framework is demonstrated with experiments using
real-world data.

研究合作博弈理论中测量贡献的古典问题，借助贝叶斯学习博弈模型以及 Shapley 值计算方法，探索了 Shapley 值在该模型中的收敛性及其应用，提出了一种渐近 Shapley 公平的在线协作学习框架，并通过实验验证了效果。