第66回統計的機械学習セミナー / The 66th Statistical Machine Learning Seminar (Hybrid)

【Date & Time】
June 19th (Thursday), 2025　16:00 - 17:30
Admission Free
【Place】
Seminar Room 4, The Institute of Statistical Mathamatics
Hybrid :
Please register at the following link and get a Zoom link, if you join by Zoom
https://forms.gle/iHV4jEYmB96tbJRs5
【Speaker】
Prof. Alex Luedtke (Associate Professor of Statistics, University of Washington.)
【Title】
Simplifying debiased inference via automatic differentiation and probabilistic programming
【Abstract】
We introduce an algorithm that simplifies the construction of efficient estimators, making them accessible to a broader audience. 'Dimple' takes as input computer code representing a parameter of interest and outputs an efficient estimator. Unlike standard approaches, it does not require users to derive a functional derivative known as the efficient influence function. Dimple avoids this task by applying automatic differentiation to the statistical functional of interest. Doing so requires expressing this functional as a composition of primitives satisfying a novel differentiability condition. Dimple also uses this composition to determine the nuisances it must estimate. In software, primitives can be implemented independently of one another and reused across different estimation problems. We provide a proof-of-concept Python implementation and showcase through examples how it allows users to go from parameter specification to efficient estimation with just a few lines of code.
https://arxiv.org/abs/2405.08675