In this session, we will have an invited talk from Mijung Park at Max Planck Institute for Intelligent Systems and University of Tuebingen, as well as two contributed talks from Hao Wu (Conjugate Energy-Based Models) and William Tebbutt (Combining Pseudo-Point and State Space Approximations for Sum-Separable Gaussian Processes). See http://approximateinference.org/schedule/ for details.
Mijung Park: ABCDP: Approximate Bayesian Computation & Differential Privacy
Abstract: We develop a novel approximate Bayesian computation framework, called ABCDP, that produces differentially private posterior samples. Our framework requires minimal modification to existing ABC algorithms. We theoretically analyze the interplay between the noise added for the privacy guarantee and the accuracy of the ABC posterior samples. We apply ABCDP to simulated data as well as privacy-sensitive real data and show the efficacy of the proposed framework.