Information Theoretic Acquisition Function

Information-theoretic acquisition functions guide the efficient exploration of complex, expensive-to-evaluate functions by strategically selecting which data points to sample. Current research focuses on improving these functions for various scenarios, including multi-fidelity optimization, active learning, and multi-objective optimization, often employing Bayesian optimization and Gaussian processes to model uncertainty. These methods leverage entropy and information gain to maximize the information learned from each sample, leading to faster convergence in optimization problems. This approach has significant implications for diverse fields, enabling more efficient design optimization, improved active learning strategies, and enhanced decision-making in resource-constrained environments.