Self-Healing Machine Learning: A Framework for Autonomous Adaptation in Real-World Environments

TL;DR

This paper introduces self-healing machine learning (SHML), an autonomous framework that diagnoses causes of model degradation due to data shifts and proposes targeted corrective actions, enhancing adaptability in real-world environments.

Contribution

It formalizes SHML as an optimization problem, develops a theoretical framework, and implements H-LLM using large language models for diagnosis and adaptation, advancing autonomous ML systems.

Findings

H-LLM effectively diagnoses causes of degradation.

H-LLM proposes targeted corrective actions.

Self-healing approach improves model robustness.

Abstract

Real-world machine learning systems often encounter model performance degradation due to distributional shifts in the underlying data generating process (DGP). Existing approaches to addressing shifts, such as concept drift adaptation, are limited by their reason-agnostic nature. By choosing from a pre-defined set of actions, such methods implicitly assume that the causes of model degradation are irrelevant to what actions should be taken, limiting their ability to select appropriate adaptations. In this paper, we propose an alternative paradigm to overcome these limitations, called self-healing machine learning (SHML). Contrary to previous approaches, SHML autonomously diagnoses the reason for degradation and proposes diagnosis-based corrective actions. We formalize SHML as an optimization problem over a space of adaptation actions to minimize the expected risk under the shifted DGP. We introduce a theoretical framework for self-healing systems and build an agentic self-healing solution H-LLM which uses large language models to perform self-diagnosis by reasoning about the structure underlying the DGP, and self-adaptation by proposing and evaluating corrective actions. Empirically, we analyze different components of H-LLM to understand why and when it works, demonstrating the potential of self-healing ML.