Starting January 24th, the OTMLE (Optimal Transport and Targeted Maximum Likelihood Estimation) reading group will further investigate the geometry of probability spaces and the implications for TMLE’s structure and behavior. Topics include deeper explorations of how optimal transport’s spatial and dynamic properties provide insights into likelihood-based optimization and its role in semiparametric models. Rather than diving into specific optimization techniques like natural gradient descent or Newton’s method, this semester focuses on laying the theoretical groundwork for understanding such methods in probability spaces. Participants refine their understanding of how probability space-based optimization differs fundamentally from traditional parameter space approaches. This exploration highlights the theoretical richness of TMLE’s operations in probability space and prepares participants to extend these ideas to advanced methods and practical implementations in their future work. For more information visit the OTMLE website.
Date: Fridays, January 24th - May 2nd
