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Distribution Learning Meets Graph Structure Sampling
This work establishes a novel link between the problem of PAC-learning high-dimensional graphical models and the task of (efficient) counting and sampling of graph structures, using an online learning framework.
Verifying Individual Fairness in Machine Learning Models
Our objective is to construct verifiers for proving individual fairness of a given model, and we do so by considering appropriate relaxations of the problem.
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