Hypothesis Space

Hypothesis space, in machine learning and related fields, refers to the set of all possible models considered by a learning algorithm. Current research focuses on efficiently searching these spaces, particularly in high-dimensional settings, using techniques like causal graph partitioning and dropout-based exploration of the "Rashomon set" (the subset of near-optimal models). Understanding and controlling the complexity of the hypothesis space, for example through the use of prior information or by leveraging properties like Rademacher complexity, is crucial for improving generalization performance and mitigating issues like overfitting and predictive multiplicity in various applications, including causal inference, multi-view stereo, and natural language processing.