arXiv:2607.07304v1 Announce Type: new Abstract: In this paper we first study the problem of generalized linear bandit (GLB) under heavy-tailed noise. The characteristics of heavy-tailed distributions are widely observed in real-world applications such as personalized recommendation, financial markets, and medical treatments. Based on the online mirror descent (OMD) method, we propose an algorithm EHM that extends the adaptive Huber loss method (Wang et al., 2025) with one-pass update ($\mathcal{O}(1)$ computational complexity with respect to current round $t$ and the time horizon $T$), which s
The continuous evolution of AI algorithms and their application in real-world scenarios, particularly with noisy data, drives ongoing research in robust learning methods.
This research contributes to making AI systems more reliable and effective in applications where data quality is inconsistent, such as finance or personalized recommendations, by addressing the challenge of heavy-tailed noise.
The development of more computationally efficient and robust algorithms for online learning in complex, noisy environments could lead to more stable and adaptable AI-powered systems.
- · AI researchers
- · Financial services
- · Personalized recommendation platforms
- · Healthcare AI
Improved performance of AI models in real-world applications with noisy data.
Increased adoption of AI in critical sectors due to enhanced reliability and robustness.
New competitive advantages for companies leveraging these advanced, robust AI techniques.
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Read at arXiv cs.LG