Big Learning

Big learning represents a paradigm shift in machine learning, focusing on training large-scale foundation models that simultaneously learn multiple joint, conditional, and marginal data distributions from diverse sources. Current research emphasizes developing algorithms that effectively leverage this "cooperative learning" approach, including adaptations of Expectation-Maximization and novel techniques like "slow kill" for efficient variable selection in massive datasets. This approach promises to improve the accuracy, robustness, and efficiency of machine learning models across various applications, unifying existing paradigms under a single, powerful framework.