Fast High-Dimensional Regret Minimization for Interactive Queries
Published:Dec 30, 2025 08:40
•1 min read
•ArXiv
Analysis
This paper addresses the scalability problem of interactive query algorithms in high-dimensional datasets, a critical issue in modern applications. The proposed FHDR framework offers significant improvements in execution time and the number of user interactions compared to existing methods, potentially revolutionizing interactive query processing in areas like housing and finance.
Key Takeaways
- •Addresses the scalability limitations of interactive query algorithms in high-dimensional datasets.
- •Proposes FHDR, a novel framework for fast high-dimensional regret minimization.
- •Demonstrates significant improvements in execution time and interaction rounds compared to existing methods.
- •Establishes a new state-of-the-art for scalable interactive regret minimization.
Reference
“FHDR outperforms the best-known algorithms by at least an order of magnitude in execution time and up to several orders of magnitude in terms of the number of interactions required, establishing a new state of the art for scalable interactive regret minimization.”