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This paper studies KL-regularized contextual bandits and shows that greedy sampling can achieve logarithmic regret without explicit eluder-dimension dependence for both reward and preference feedback.
The article presents COBRA-Skills, a method using contextual bandits to efficiently optimize agent skills, reducing costs by avoiding repeated LLM-based trajectory analysis and skill rewriting.
COBRA-Skills introduces a method for optimizing agent skills using contextual bandits and evolutionary operators, achieving 55-58% lower optimization costs across multiple AI agent benchmarks.
This paper formulates the adaptive routing of prompts to large language model experts as a contextual bandit problem with limited feedback, proposing algorithms that achieve sublinear regret and demonstrate efficient learning of high-quality routing strategies.
This paper proposes High-Probability Constrained UCB for contextual bandits with continuous actions, emphasizing realized-cost constraints over expected-cost to improve safety, and provides theoretical regret bounds and experimental validation.
The paper introduces a failure-aware adversarial retrieval-augmented framework using contextual bandits to improve robustness in natural language understanding, with significant improvements on benchmarks like SNLI, ANLI, and MultiNLI.
Introduces Tree-Coupled A/B Testing (TCAB), an exact feedback-sharing design for comparing multiple adaptive policies with fewer reward queries while preserving each policy's trajectory law.
This paper presents a diagnostic protocol for selecting rewards and policies in delayed-feedback contextual bandits, arguing that standard offline evaluation can mislead and validating the approach on benchmarks and a deployed push system.
This paper proposes new reoptimization algorithms for contextual bandits with knapsack constraints, achieving an average regret bound of O((ln T)^3 / T) and improving existing results.
The paper proposes BC-ICL, a bootstrap-conditioned action selection method that leverages pretrained tabular foundation models with in-context learning for contextual bandits, improving exploration and regret performance under strict online protocols.
This paper introduces cross-domain off-policy evaluation and learning (OPE/L) for contextual bandits, allowing the use of logged data from multiple source domains to improve policy evaluation and learning in target domains with challenging conditions like few-shot data, deterministic logging policies, and new actions.
This paper studies contextual causal bandits with non-manipulable variables, proposing causal variants of Thompson Sampling and Information-Directed Sampling (IDS) that exploit shared causal mechanisms to accelerate decision-making. Theoretical regret bounds and experiments on synthetic tasks show that the proposed methods outperform causal and non-causal baselines.
This paper proposes correlation-aware contextual bandit algorithms that leverage surrogate reward signals from machine learning models for LLM routing, achieving improved accuracy-cost trade-offs and sample efficiency compared to standard baselines.
This paper introduces a contextual-bandit team game with two-sided informational asymmetry for runtime human oversight of AI agents, characterizing gaps between team-optimal and myopic human oversight strategies.
Proposes algorithms for contextual slate bandits with generalized linear rewards under limited adaptivity, achieving regret bounds independent of the non-linearity parameter. The batched and rarely-switching algorithms are computationally efficient and empirically outperform baselines, including in a language model example selection task.
This paper proposes D2R-RAG, a model-agnostic and resource-aware framework that diagnoses and repairs factual errors in RAG systems under latency and VRAM constraints, achieving better accuracy-efficiency trade-offs on FEVER and HotpotQA.
Proposes GraphDR-LinUCB, a method for contextual bandits with graph-structured arms that projects features onto the graph's low-frequency spectral subspace. Achieves the first regret bound for spectral-projection-based contextual bandits and demonstrates 15x regret reduction on real datasets over full-dimensional LinUCB.
This paper presents a contextual multi-armed bandit framework that learns individual spillover probabilities in social networks to optimize stimulated word-of-mouth marketing, achieving higher rewards by targeting connected users.
This paper formalizes embedding model routing as an adversarial contextual linear bandit with low-rank experts, proposing the Hypentropy Policy Gradient (HPG) algorithm that achieves O~(s√(MT)) policy regret, avoiding the curse of dimensionality.
This paper introduces an online contextual Pandora's Box model for adaptively querying and selecting LLM APIs, proposing a learning approach that combines GMM estimation with UCB-style confidence bounds and proving dimension-dependent regret bounds.