Set Function

Set functions, which map sets of data points to numerical values, are increasingly important for machine learning tasks involving unordered data. Current research focuses on developing neural network architectures, such as Deep Sets and variations thereof, to efficiently approximate these functions, often incorporating techniques like permutation invariance and differentiable quadrature for improved performance. This work is driven by the need for more expressive models capable of handling diverse applications, including submodular function optimization, graph edit distance calculation, and model prediction from training data. The resulting advancements have significant implications for various fields, enabling more efficient algorithms for tasks ranging from data summarization to combinatorial optimization.