Adversarial Contamination Meets Hard Thresholding: An Iterative Algorithm with Signal Adaptivity and Minimax Optimality
arXiv:2606.27685v1 Announce Type: cross Abstract: Pervasive data contamination -- stemming from measurement errors, outliers, or adversarial corruption -- has motivated the development of robust statistical methods. In this context, we propose a two-stage Adversarial Contamination-resistant Iterative Hard Thresholding (AC-IHT) algorithm for high-dimensional regression with contamination. Our nonconvex algorithm achieves minimax near-optimal (up to logarithmic terms) estimation by iteratively updating the coefficient vector and the contamination vector with different thresholding scales. We fur
This research addresses the growing need for robust AI methods in the face of increasingly complex and contaminated data environments, mirroring real-world challenges in AI deployment.
Improving the resilience of high-dimensional regression models against adversarial contamination has direct implications for the reliability and trustworthiness of AI systems across various applications.
The development of algorithms like AC-IHT offers a more robust approach to handling corrupted data, potentially leading to more stable and dependable AI-driven insights and decisions, particularly in sensitive areas.
- · AI developers
- · Data scientists
- · Cybersecurity researchers
- · Industries relying on AI for critical decisions
- · Adversarial actors
- · Systems vulnerable to data poisoning
Increased reliability of AI models in real-world, noisy data environments.
Accelerated adoption of AI in sectors where data integrity and model robustness are paramount.
Potential for new cybersecurity paradigms focusing on data integrity at the input stage of AI systems.
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Read at arXiv cs.LG