Pooling and Drift in Delayed Bandits

arXiv:2609.01761 · stat.ML, cs.LG · Submitted 2026-09-01 · Read on arXiv

stat.ML, cs.LG

Submitted: 2026-09-01

Updated: 2026-09-06

Code: https://github.com/melikabaghi/state-exp3

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Regret — 31× in this paper · explained in 3 episodes
• Benchmark — 3× in this paper · explained in 2 episodes
• Adaptivity — 2× in this paper · explained in 1 episode
• Contextual Bandits — 1× in this paper · explained in 3 episodes
• Cooperation — 1× in this paper · explained in 2 episodes

Sources

• Regret Bounds for Adversarial Contextual Bandits with General Function Approximation and Delayed Feedback
• Improved Best-of-Both-Worlds Regret for Bandits with Delayed Feedback
• Bandit Convex Optimization with Gradient Prediction Adaptivity

Related papers

• Behavior of prediction performance metrics with rare events
• Optimal Estimation of Generic Dynamics by Path-Dependent Neural Jump ODEs
• A Posterior-Dynamics Framework for Imaging Inverse Problems with Pretrained Diffusion Priors
• One Permutation Is All You Need: Fast, Deterministic Feature Importance and Model Stress-Testing
• Online Conformal Prediction for Non-Exchangeable Panel Data
• Deep Time-Series Forecasting in 10 Years: A Survey