Neural Thompson Sampling

Neural Thompson Sampling (NTS) is a Bayesian approach to sequential decision-making that leverages neural networks to model uncertainty in reward functions, improving upon traditional methods like Upper Confidence Bound. Current research focuses on extending NTS to handle complex data structures (e.g., graphs) and combinatorial action spaces, often employing neural tangent kernels for theoretical analysis and efficient computation. These advancements are proving valuable in diverse applications, including optimizing deep brain stimulation for Parkinson's disease, enhancing ultra-reliable low-latency communication in industrial IoT, and improving the efficiency of automated machine learning.