[2604.04892] Data Attribution in Adaptive Learning

Computer Science > Machine Learning arXiv:2604.04892 (cs) [Submitted on 6 Apr 2026] Title:Data Attribution in Adaptive Learning Authors:Amit Kiran Rege View a PDF of the paper titled Data Attribution in Adaptive Learning, by Amit Kiran Rege View PDF HTML (experimental) Abstract:Machine learning models increasingly generate their own training data -- online bandits, reinforcement learning, and post-training pipelines for language models are leading examples. In these adaptive settings, a single training observation both updates the learner and shifts the distribution of future data the learner will collect. Standard attribution methods, designed for static datasets, ignore this feedback. We formalize occurrence-level attribution for finite-horizon adaptive learning via a conditional interventional target, prove that replay-side information cannot recover it in general, and identify a structural class in which the target is identified from logged data. Comments: Subjects: Machine Learning (cs.LG) Cite as: arXiv:2604.04892 [cs.LG]   (or arXiv:2604.04892v1 [cs.LG] for this version)   https://doi.org/10.48550/arXiv.2604.04892 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Amit Kiran Rege [view email] [v1] Mon, 6 Apr 2026 17:41:32 UTC (42 KB) Full-text links: Access Paper: View a PDF of the paper titled Data Attribution in Adaptive Learning, by Amit Kiran RegeView PDFHTML (experimental)TeX Source view license Current browse cont...