The paper introduces a distribution‑free framework for assessing the quality of external data before acquisition. By applying novel conformal two‑sample tests, it identifies data agents whose data exceed a contamination threshold, enabling selective data acquisition for personalized machine learning. The approach guarantees finite‑sample false discovery rate control and demonstrates robustness across various collaborative learning scenarios.
arXiv:2605. 20726v2 Announce Type: replace-cross Abstract: Modern applications of conformal inference to multiple testing problems, such as outlier detection and candidate selection, often involve selecting test samples whose conformal p-values fall below a threshold.
Online Conformal Anomaly Detection with Prediction-Powered Data Acquisition introduces C-PP-COAD, a framework that uses synthetic calibration data to reduce reliance on real-world calibration while maintaining assumption-free false discovery rate control. The method wraps any anomaly detection algorithm, converting its scores into conformal p-values for online testing. Experiments on synthetic and real datasets—including thyroid dysfunction, O‑RAN conflict, 5G intrusion, and UE throughput degradation—show that C-PP-COAD preserves FDR guarantees while significantly cutting the need for real calibration data.
The paper introduces EIAD-C2ST, a deep classifier two-sample test designed for highly imbalanced data, particularly complex non-Euclidean structures like graphs. It reframes sample imbalance as a source of repeated testing evidence rather than a nuisance, tailoring classifiers to data structure and offering a scalable representation‑amortized strategy for expensive deep models. The authors provide theoretical guarantees on asymptotic size and power consistency, and demonstrate empirical improvements through simulations and a blockchain forensics application.
arXiv:2606. 16110v1 Announce Type: new Abstract: Machine unlearning has been extensively studied in response to growing privacy concerns and regulatory requirements.
arXiv:2603. 10937v2 Announce Type: replace Abstract: The use of synthetic data has become increasingly popular as a privacy-preserving alternative to sharing real datasets, especially in sensitive domains such as healthcare, finance, and demography.
arXiv:2608. 13418v1 Announce Type: cross Abstract: Given a dataset where a portion of the samples are contaminated, our goal is to recover the underlying clean population distribution.
arXiv:2610.33592v1 Announce Type: new Abstract: Augmented large language model (LLM) systems have reported high benchmark accuracies in rare disease diagnosis, yet their real-world clinical utility r...
arXiv:2606. 16952v1 Announce Type: cross Abstract: The rapid adoption of generative AI and Large Language Models (LLMs) has spurred interest in synthetic data as a privacy-preserving alternative to sensitive real-world datasets.
arXiv:2606. 03305v1 Announce Type: new Abstract: Benchmark contamination, where evaluation examples appear in a model's training data, threatens the validity of LLM assessment.
arXiv:2508. 10017v2 Announce Type: replace-cross Abstract: Federated Learning (FL) presents a groundbreaking approach for collaborative health research, allowing model training on decentralized data while safeguarding patient privacy.
Given a dataset where a portion of the samples are contaminated, our goal is to recover the underlying clean population distribution. To this end, we propose Wasserstein Filtering (WF), a novel sample selection framework that discards a fraction of suspicious samples and estimates the target distribution using the empirical measure of the remaining data.
arXiv:2606. 16952v2 Announce Type: replace-cross Abstract: The rapid adoption of generative AI and Large Language Models (LLMs) has spurred interest in synthetic data as a privacy-preserving alternative to sensitive real-world datasets.