"谁经历了大型模型衰变,为什么?" 一个层次框架来诊断异构的性能漂移
Harvineet Singh1, Fan Xia1, Alexej Gossmann2
1University of California, San Francisco, USA.
概括
机器学习 (ML) 模型的性能衰退在各个子组中通常是不均的. 我们的SHIFT框架确定了性能下降的原因和原因,使得有针对性的干预措施能够有效地减轻衰退.
科学领域:
- 机器学习 机器学习
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 机器学习 (ML) 模型在新环境中部署时,通常会降低性能.
- 性能退化通常是不均的,影响某些子组比其他更严重.
- 现有的方法缺乏详细的洞察力,了解各个子组的性能衰退的原因和位置.
研究的目的:
- 引入一个新的框架来识别和理解不同子组的ML模型中的性能衰退.
- 通过确定特定的子组及其绩效下降的原因,能够设计有针对性的纠正措施.
- 解决当前方法的局限性,这些方法专注于平均绩效转变或简单地识别受影响的子组而没有解释.
主要方法:
- 开发用于性能漂移的子组扫描等级推理框架 (SHIFT).
- SHIFT采用双阶段方法:首先确定具有显著性能衰退的子组,然后调查潜在原因 (共变量/结果转移).
- 评估SHIFT识别可解释子组的能力,并建议有针对性的缓解策略.
主要成果:
- SHIFT成功地识别了经历过不成比例的大性能衰退的特定子组.
- 该框架通过分析特定变量的转变,为性能退化提供了可解释的解释.
- 现实世界的实验表明,SHIFT引导的操作有效地减轻了性能下降.
结论:
- SHIFT为诊断和解决部署的ML模型中的性能差异提供了一个强大的工具.
- 了解特定子组的性能衰退对于开发有效和有针对性的ML模型维护策略至关重要.
- 该框架通过允许精确的干预来促进创建更强大,更公平的AI系统.
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