Nonstationary Generalized Linear Bandits with Discounted Online Mirror Descent

arXiv:2605.25590 · stat.ML, cs.LG · Submitted 2026-05-25 · Read on arXiv

stat.ML, cs.LG

Submitted: 2026-05-25

Updated: 2026-09-26

Terminology

• Regret — 52× in this paper · explained in 3 episodes
• Reinforcement Learning — 7× in this paper · explained in 4 episodes
• Admissibility — 3× in this paper · explained in 2 episodes
• Non-stationarity — 3× in this paper · explained in 2 episodes
• Loss Function — 2× in this paper · explained in 2 episodes
• Adaptivity — 1× in this paper · explained in 1 episode
• Contextual Bandit Problem — 1× in this paper · explained in 2 episodes
• Geometric Mismatch — 1× in this paper · explained in 2 episodes
• Pairwise Comparisons — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Trace — 1× in this paper · explained in 2 episodes

Sources

• Regret Bounds for Generalized Linear Bandits under Parameter Drift
• Algorithms for Non-Stationary Generalized Linear Bandits
• Variance-Dependent Regret Bounds for Non-stationary Linear Bandits
• Generalized Linear Bandits: Almost Optimal Regret with One-Pass Update
• High-Dimensional Dynamic Pricing under Non-Stationarity: Learning and Earning with Change-Point Detection

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