Adaptive Neighborhood

Adaptive neighborhood methods aim to dynamically determine the most relevant data points for each individual data point within a dataset, improving upon fixed-size neighborhood approaches used in various machine learning tasks. Current research focuses on developing algorithms that learn these adaptive neighborhoods, often integrating them into graph neural networks or other models, and employing techniques like optimal transport or iterative refinement to optimize neighborhood selection. This approach enhances the performance of numerous applications, including graph-based learning, anomaly detection, causal inference, and reinforcement learning, by adapting to the specific characteristics of the data and improving efficiency and accuracy.