Tracking the Risk of Machine Learning Systems with Partial Monitoring

Although efficient at performing specific tasks, Machine Learning Systems (MLSs) remain vulnerable to instabilities such as noise or adversarial attacks. In this work, we aim to track the risk exposure of an MLS to these events. We formulate this problem under the stochastic Partial Monitoring (PM) setting. We focus on two instances of partial monitoring, namely the Apple Tasting and Label Efficient games, that are particularly relevant to our problem. Our review of the practicality of existing algorithms motivates RandCBP, a randomized variation of the deterministic algorithm Confidence Bound (CBP) inspired by recent theoretical developments in the bandits setting. Our preliminary results indicate that RandCBP enjoys the same regret guarantees as its deterministic counterpart CBP and achieves competitive empirical performance on settings of interest which suggests it could be a suitable candidate for our problem.