Covers model-free prediction methods in reinforcement learning, focusing on Monte Carlo and Temporal Differences for estimating value functions without transition dynamics knowledge.
Explores challenges in entity resolution in data streams and proposes optimizations to enhance resolution throughput and handle imbalanced distributions.
