DAGnosis: Localized Identification of Data Inconsistencies using Structures

arXiv:2402.17599v1 Announce Type: new
Abstract: Identification and appropriate handling of inconsistencies in data at deployment time is crucial to reliably use machine learning models. While recent data-centric methods are able to identify such inconsistencies with respect to the training set, they suffer from two key limitations: (1) suboptimality in settings where features exhibit statistical independencies, due to their usage of compressive representations and (2) lack of localization to pin-point why a sample might be flagged as inconsistent, which is important …

abstract arxiv cs.ai cs.lg data data-centric deployment features identification identify key limitations machine machine learning machine learning models set stat.ml training type