Discrete Function

Discrete functions, mappings between discrete input and output spaces, are a focus of current research due to their importance in diverse fields like robotics and machine learning. Researchers are exploring novel methods for learning and optimizing these functions, including differentiable neural networks that leverage gradient descent and techniques for efficiently exploring the space of near-optimal solutions (the Rashomon set). This work addresses challenges like the incompatibility of discrete domains with gradient-based optimization and aims to improve the efficiency, interpretability, and sample efficiency of learning algorithms for discrete functions, impacting areas such as robotic control and the understanding of in-context learning in large language models.