A new optimistic lower-confidence-bound method for gray-box optimization that improves regret bounds for linear stochastic bandits via a recent multi-output least-squares confidence set result.
hub
IEEE Control Systems Magazine 26(3): 96--114
2 Pith papers cite this work, alongside 2,934 external citations. Polarity classification is still indexing.
2
Pith papers citing it
2,934
external citations · external index
hub tools
verdicts
UNVERDICTED 2representative citing papers
Integrates iterative learning control with a torque library to enable high-precision adaptive locomotion on bipedal and quadrupedal robots, reducing tracking errors by up to 85% and achieving over 30x faster control rates.
citing papers explorer
-
Gray-Box Optimization using Optimism in the Face of Uncertainty
A new optimistic lower-confidence-bound method for gray-box optimization that improves regret bounds for linear stochastic bandits via a recent multi-output least-squares confidence set result.