4 papers
Constrained Online Convex Optimization without Slater's Condition
Kihyun Yu, Junehee Lee, Dabeen Lee
We study constrained online convex optimization with adversarial losses and stochastic or adversarial constraints. For stochastic constraints, existing algorithms that achieve near…
Algorithm for Contextual Queueing Bandits with Rate-Optimal Queue Length Regret
Seoungbin Bae, Dabeen Lee
Contextual queueing bandits provide a framework for learning to schedule heterogeneous jobs under unknown context-dependent service rates. Under stochastic contexts, existing algor…
Logistic Bandits with Regret without Context Diversity Assumptions
Seoungbin Bae, Dabeen Lee
We study the -armed logistic bandit problem, where at each round, the agent observes feature vectors associated with actions. Existing approaches that achieve a rate-opt…
Near-Optimal Primal-Dual Algorithm for Learning Linear Mixture CMDPs with Adversarial Rewards
Kihyun Yu, Seoungbin Bae, Dabeen Lee
We study safe reinforcement learning in finite-horizon linear mixture constrained Markov decision processes (CMDPs) with adversarial rewards under full-information feedback and an…