Computationally Tractable and Near Optimal Design of Experiments

Computationally Tractable and Near Optimal Design of Experiments

Explore a lecture by Aarti Singh from Carnegie Mellon University on computationally tractable and near-optimal design of experiments, presented as part of the Computational Challenges in Machine Learning series at the Simons Institute. Delve into advanced concepts in experimental design, focusing on methods that balance computational efficiency with near-optimal performance. Gain insights into cutting-edge approaches for tackling complex experimental design problems in machine learning and data science.