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Diagnostic Runtime Monitoring with Martingales

2024-07-31 17:05:10
Ali Hindy, Rachel Luo, Somrita Banerjee, Jonathan Kuck, Edward Schmerling, Marco Pavone

Abstract

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.

Abstract (translated)

机器学习系统在关键机器人环境中部署时,必须对分布漂移具有鲁棒性。然而,系统设计师必须了解分布漂移的原因,以便实现适当的干预或缓解策略,并防止系统失效。在本文中,我们提出了一种通过同时部署多个随机 Martingale 来以流式方式诊断分布漂移的新颖框架。我们证明了分布漂移的潜在原因可以导致部署系统整个生命周期内的适当干预。我们的实验框架可以很容易地适应不同类型的分布漂移、模型和数据集。我们发现,我们的方法在诊断分布漂移的速度、准确性和灵活性方面优于现有工作,并验证了我们在模拟和实时硬件环境中的模型的效率。

URL

https://arxiv.org/abs/2407.21748

PDF

https://arxiv.org/pdf/2407.21748.pdf


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