arXiv AI
Jun 29

When Is an LLM Worth It for Hyperparameter Optimization? A Budget-Matched Study on Tabular Data Finds the Warm-Start Is a Default Configuration, Not the Model

arXiv:2606. 21641v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have been proposed as hyperparameter-optimization (HPO) advisors that "warm-start" search from prior knowledge, proposing strong configurations in very few evaluations.

By Carson Rodrigues, Oysturn Vas, Isaiah Abner DCosta, Nithish Kumar Prabhakaran
arXiv Machine Learning
Sep 16

Stream Assembly Is an Uncontrolled Treatment in Streaming Intrusion-Detection Benchmarks

The paper demonstrates that the way evaluation streams are assembled in streaming intrusion‑detection benchmarks—by interleaving, pooling, or replaying network captures—acts as an uncontrolled experimental variable that can significantly alter performance metrics. In the CICIDS2017 benchmark, reordering the same set of records under a fixed split changes the held‑out samples’ overlap, prevalence, and even reverses the ranking of two deterministic scorers. Similar effects are observed in the LITNET‑2020 benchmark, where pooling disjoint captures yields a single operating point that masks large variations in per‑capture prevalences, and minor changes in batch composition can shift reported AUC‑PR values by a few thousandths.

By Michel A. Youssef