Technical Program

Paper Detail

Paper IDL.9.5
Paper Title Model Projection: Theory and Applications to Fair Machine Learning
Authors Wael Alghamdi, Shahab Asoodeh, Hao Wang, Flavio P. Calmon, Harvard University, United States; Dennis Wei, Karthikeyan Natesan Ramamurthy, IBM Research, United States
Session L.9: Learning Methods and Networks
Presentation Lecture
Track Statistics and Learning Theory
Manuscript  Click here to download the manuscript
Virtual Presentation  Click here to watch in the Virtual Symposium
Abstract We study the problem of finding the element within a convex set of conditional distributions with the smallest f-divergence to a reference distribution. Motivated by applications in machine learning, we refer to this problem as model projection since any probabilistic classification model can be viewed as a conditional distribution. We provide conditions under which the existence and uniqueness of the optimal model can be guaranteed and establish strong duality results. Strong duality, in turn, allows the model projection problem to be reduced to a tractable finite-dimensional optimization. Our application of interest is fair machine learning: the model projection formulation can be directly used to design fair models according to different group fairness metrics. Moreover, this information-theoretic formulation generalizes existing approaches within the fair machine learning literature. We give explicit formulas for the optimal fair model and a systematic procedure for computing it.

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2021 IEEE International Symposium on Information Theory

11-16 July 2021 | Melbourne, Victoria, Australia

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