A Complete Characterization of Learnability for Adversarial Noisy Bandits

arXiv:2605.09200 · cs.LG · Submitted 2026-05-09 · Read on arXiv

cs.LG

Submitted: 2026-05-09

Updated: 2026-10-16

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

Terminology

• Regret — 2× in this paper · explained in 4 episodes
• Adaptive Adversaries — 1× in this paper · explained in 1 episode

Related papers

• Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
• AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
• Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
• Convergence issues in Relational Concept Analysis based on AOC-posets
• Beliefs Beyond Posteriors: The Predictive Cost of ELBO Binding
• Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks