Semi-overlapping Multi-bandit for Support Network Learning
Published:Dec 31, 2025 16:42
•1 min read
•ArXiv
Analysis
This paper introduces a novel framework, Sequential Support Network Learning (SSNL), to address the problem of identifying the best candidates in complex AI/ML scenarios where evaluations are shared and computationally expensive. It proposes a new pure-exploration model, the semi-overlapping multi-bandit (SOMMAB), and develops a generalized GapE algorithm with improved error bounds. The work's significance lies in providing a theoretical foundation and performance guarantees for sequential learning tools applicable to various learning problems like multi-task learning and federated learning.
Key Takeaways
- •Introduces Sequential Support Network Learning (SSNL) for identifying best candidates in shared evaluation scenarios.
- •Proposes the semi-overlapping multi-bandit (SOMMAB) model.
- •Develops a generalized GapE algorithm with improved error bounds.
- •Provides theoretical foundation and performance guarantees for sequential learning tools in various applications (MTL, ATL, FL, MAS).
Reference
“The paper introduces the semi-overlapping multi-(multi-armed) bandit (SOMMAB), in which a single evaluation provides distinct feedback to multiple bandits due to structural overlap among their arms.”