SELO combines Lyapunov-style virtual queues with pessimistic linear-bandit estimates to reach O(√T) regret and zero cumulative constraint violation under unknown linear budgets and partial constraint feedback.
An efficient pessimistic-optimistic algorithm for stochastic linear bandits with general constraints,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
math.OC 1years
2024 1verdicts
CONDITIONAL 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
Safe and Efficient Online Convex Optimization with Linear Budget Constraints and Partial Feedback
SELO combines Lyapunov-style virtual queues with pessimistic linear-bandit estimates to reach O(√T) regret and zero cumulative constraint violation under unknown linear budgets and partial constraint feedback.