Elicitation and Decision Geometry in Single-Index Bandits

arXiv:2609.35622 · stat.ML, cs.LG · Submitted 2026-09-28 · Read on arXiv

stat.ML, cs.LG

Submitted: 2026-09-28

Updated: 2026-09-28

Terminology

• Regret — 42× in this paper · explained in 3 episodes
• Contextual Bandits — 11× in this paper · explained in 3 episodes
• Reward Function — 7× in this paper · explained in 2 episodes
• Angular Error — 5× in this paper · explained in 1 episode
• Natural Boundary Learning — 5× in this paper
• Benchmark — 4× in this paper · explained in 2 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Stochastic Dynamics — 2× in this paper · explained in 2 episodes
• Adaptivity — 1× in this paper · explained in 1 episode
• Anticoncentration — 1× in this paper · explained in 1 episode
• Contextual Bandit Problem — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Score Function — 1× in this paper · explained in 4 episodes

Sources

• Kernel Single-Index Bandits: Estimation, Inference, and Learning
• Batched Single-Index Global Multi-Armed Bandits with Covariates
• Nonparametric Bandits with Single-Index Rewards: Optimality and Adaptivity

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