Generation of Adversarial Queries via Bayesian Optimization
This work introduces Gremlin, a system for generating adversarial benchmarks of queries with high headroom, and applies Bayesian optimization to search the joint space of queries and witness plans, seeking query-plan pairs that maximize headroom.
Jeffrey Tao, Yi-Meng Zeng, Natalie Maus et al.
· 0 citations