arXiv:2605.10299v2 Announce Type: replace Abstract: This paper studies kernelized bandits (also known as Gaussian process bandits) in an adversarial environment, where the reward functions in a known reproducing kernel Hilbert space (RKHS) may be adversarially chosen at each round. We show that the exponential-weight algorithm achieves $\tilde{O}(\sqrt{T \gamma_T})$ adversarial regret, where $T$ and $\gamma_T$ denote the number of total rounds and the maximum information gain, respectively. For squared exponential (SE) and $\nu$-Mat\'ern kernels, we also show algorithm-independent lower bounds
The paper provides a significant advancement in theoretical understanding of adversarial bandit algorithms, which is crucial for robust and reliable AI systems, especially as AI applications become more critical.
A strategic reader should care because improved algorithms for adversarial kernelized bandits contribute to more resilient and efficient AI learning in unpredictable, real-world environments, influencing fields from personalized recommendations to autonomous decision-making.
This research provides a nearly-optimal algorithm that improves the theoretical guarantees for learning under adversarial conditions, reducing regret and increasing robustness in AI systems.
- · AI/ML researchers
- · Developers of robust AI systems
- · Sectors reliant on adaptive algorithms
- · Systems vulnerable to adversarial attacks
- · Less efficient bandit algorithms
This research will enable the development of more stable and reliable machine learning models in dynamic and potentially malicious environments.
It could lead to new applications in areas like adaptive cybersecurity, drug discovery, or personalized medicine where real-time learning under uncertainty is critical.
Long-term, this could contribute to the foundational robustness of general AI agents, making them more trustworthy and deployable in high-stakes situations.
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Read at arXiv cs.LG