Researchers have established a new lower bound for bandit convex optimization problems involving Lipschitz functions on a Euclidean ball. The derived bound, $\widetilde{\Omega}(d^{5/4}\sqrt{T})$, indicates that this problem is more complex than linear bandits. The construction involves a class of functions with a trade-off between learning an unknown linear transformation and identifying a specific tube in the action space, leading to a sample complexity lower bound of $\widetilde{\Omega}(d^{5/2}/\varepsilon^2)$ for finding an $\varepsilon$-optimal action. AI
IMPACT This theoretical work may inform the design of more efficient algorithms for reinforcement learning and decision-making systems.
RANK_REASON Academic paper detailing a new theoretical result in machine learning. [lever_c_demoted from research: ic=1 ai=1.0]
- arXiv
- cs.LG
- Euclidean ball
- Fisher information matrices
- Linear Bandits
- The Price of Hidden Curvature: An $\widetilde{\Omega} (d^{5/4} \sqrt{T})$ Lower Bound for Bandit Convex Optimization
AI-generated summary · Google Gemini · from 1 sources. How we write summaries →
