Adversarial Bandit Optimization with Globally Bounded Perturbations to Linear Losses
Adversarial Bandit Optimization with Globally Bounded Perturbations to Linear Losses explores This paper theoretically analyzes adversarial bandit optimization with bounded perturbations to linear losses, providing new regret bounds.. Commercial viability score: 2/10 in Reinforcement Learning.
BUILDER'S SANDBOX
Build This Paper
Use an AI coding agent to implement this research.
OpenAI CodexAI Agent
Lightweight coding agent in your terminal.
Claude CodeAI Agent
Agentic coding tool for terminal workflows.
AntiGravity IDEScaffolding
AI agent mindset installer and workflow scaffolder.
CursorIDE
AI-first code editor built on VS Code.
VS CodeIDE
Free, open-source editor by Microsoft.
Recommended Stack
Startup Essentials
Estimated $9K - $13K over 6-10 weeks.
See exactly what it costs to build this -- with 3 comparable funded startups.
7-day free trial. Cancel anytime.
Discover the researchers behind this paper and find similar experts.
7-day free trial. Cancel anytime.
Related Papers
- Prior WorkA Perturbation Approach to Unconstrained Linear BanditsViability: 2.0
- Prior WorkBatched Kernelized Bandits: Refinements and ExtensionsViability: 2.0
- Prior WorkOptimal High-Probability Regret for Online Convex Optimization with Two-Point Bandit FeedbackViability: 2.0
- Prior WorkParameter-Free Dynamic Regret for Unconstrained Linear BanditsViability: 2.0
- Higher ViabilityRobust and Computationally Efficient Linear Contextual Bandits under Adversarial Corruption and Heavy-Tailed Noise
Related Resources
- Confidence-Calibrated Reinforcement Learning(glossary)
- Multi-Agent Reinforcement Learning(glossary)
- Maximum Entropy Reinforcement Learning(glossary)
- How does PRISM improve reinforcement learning?(question)
- What is the significance of reinforcement learning in AI?(question)
- How does RetroAgent improve reinforcement learning?(question)
- Reinforcement Learning – Use Cases(use_case)