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Decomposable Submodular Maximization in Federated Setting
To address these issues, we propose a {\em federated optimization} setting for decomposable submodular optimization.
Private Boosted Decision Trees via Smooth Re-Weighting
Differential Privacy is the appropriate mathematical framework for formal guarantees of privacy, and boosted decision trees are a popular machine learning technique.
Sparsification of Decomposable Submodular Functions
The underlying submodular functions for many of these tasks are decomposable, i. e., they are sum of several simple submodular functions.
Fast and Private Submodular and $k$-Submodular Functions Maximization with Matroid Constraints
In this paper, we study the problem of maximizing monotone submodular functions subject to matroid constraints in the framework of differential privacy.
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