マルチンゲールによる診断的ランタイムモニタリング
Diagnostic Runtime Monitoring with Martingales
複数の確率的マルチンゲールを同時に用いて、ストリーミングデータ上の分布シフトの原因をリアルタイムに診断する枠組みを提案し、シミュレーションと実機で有効性を示した。
著者: Ali Hindy, Rachel Luo, Somrita Banerjee, Jonathan Kuck, Edward Schmerling, Marco Pavone
分類: cs.RO, cs.LG
原文アブストラクト
Machine learning systems deployed in safety-critical robotics settings must be robust to distribution shifts. However, system designers must understand the cause of a distribution shift in order to implement the appropriate intervention or mitigation strategy and prevent system failure. In this paper, we present a novel framework for diagnosing distribution shifts in a streaming fashion by deploying multiple stochastic martingales simultaneously. We show that knowledge of the underlying cause of a distribution shift can lead to proper interventions over the lifecycle of a deployed system. Our experimental framework can easily be adapted to different types of distribution shifts, models, and datasets. We find that our method outperforms existing work on diagnosing distribution shifts in terms of speed, accuracy, and flexibility, and validate the efficiency of our model in both simulated and live hardware settings.
