While machine learning models have demonstrated strong performance in many domains, these models have shown profound vulnerabilities when they are exposed to adversarial threats. While adversarial attacks fall into various categories, the most prominent category in research studies is evasion. In evasion attacks, the adversary generates perturbed versions of samples, which might not be observable by human eyes. These samples generally fool the machine learning models with high confidence. This phenomenon poses a significant security violation against machine learning models. In this paper, we investigate the certified and empirical robustness of various Kolmogorov-Arnold network architectures against strong evasion attacks. At first, we provide the mathematical foundations for randomized smoothing and interval bound propagation, and report the $\ell_2$-certified robustness of the models under randomized smoothing. After that, we systematically evaluate the robustness of various defended and undefended KAN models under FGSM, PGD, and C&W attacks in order to find out the optimal defense strategies and architectures.
Continual learning has made significant progress toward enabling adaptive machine learning under evolving environments, yet real-world deployment increasingly exposes systems to persistent harsh conditions, including distributional shifts, feature evolution, delayed or scarce supervision, imbalance, noise, and recurring or novel classes. While prior research has largely addressed these challenges in isolation, growing environmental complexity motivates a broader rethinking of continual adaptation as a self-regulating process rather than solely a parameter update problem. Building upon the emerging framework of Self-Adaptive Learning (SAL), this perspective explores how learning systems may progress beyond reactive adaptation toward autonomous recognition, policy selection, and context-sensitive regulation of learning behavior under persistent uncertainty. Rather than proposing a specific algorithmic solution, we position SAL as a conceptual systems framework for organizing future research on resilient, long-lived machine learning systems. We discuss key implications for deployment robustness, evaluation, safety, and adaptive governance, while outlining major open challenges in developing practical self-regulating learners. By strengthening SAL as a forward-looking framework, this work aims to advance the broader conversation on machine learning systems capable of sustained autonomy in dynamic real-world environments.
Ehsan Hallaji, R. Razavi-Far· Machine Learning and Knowled...· 0 citations
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