Parameter Space

Parameter space refers to the multi-dimensional space encompassing all possible settings of a model's parameters. Research focuses on efficiently exploring and optimizing these spaces, often employing techniques like Bayesian optimization, gradient-based methods (including adaptations for discrete spaces), and dimensionality reduction strategies such as low-rank approximations. This is crucial for improving model performance in diverse fields, from training neural networks and optimizing cooling systems to calibrating market simulations and inferring network structures from time series data, ultimately leading to more accurate and efficient models across numerous scientific and engineering disciplines.