Given a spending plan, primal-dual no-regret algorithms achieve tilde-O(sqrt T) dynamic or static regret under adversarially changing reward and cost distributions, and tilde-O(T^{3/4}) when the plan is highly imbalanced.
Adversarial bandits with knapsacks
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
No-Regret Learning Under Adversarial Resource Constraints: A Spending Plan Is All You Need!
Given a spending plan, primal-dual no-regret algorithms achieve tilde-O(sqrt T) dynamic or static regret under adversarially changing reward and cost distributions, and tilde-O(T^{3/4}) when the plan is highly imbalanced.