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We present a general method for privacy-preserving Bayesian inference in Poisson factorization, a broad class of models that includes some of the most widely used models in the social sciences.
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On tensors, sparsity, and nonnegative factorizations
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Universally utility-maximizing privacy mechanisms
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Yang, X., Fienberg, S. E., and Rinaldo, A · 2012
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Bayesian nonnegative matrix factorization with stochastic variational inference
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Nonparametric Bayesian factor analysis for dynamic count matrices
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Dynamic Poisson factorization
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Bayesian Poisson tensor factorization for inferring multilateral relations from sparse dyadic event counts
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Cryptography and the economics of supervisory information: Balancing transparency and confidentiality
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Efficient discovery of overlapping communities in massive networks
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Poetics: Topic Models and the Cultural Sciences , volume 41
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The Poisson gamma belief network
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On the theory and practice of privacy-preserving Bayesian data analysis
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Park, M., Foulds, J., Chaudhuri, K., and Welling, M · 2016
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On the differential privacy of Bayesian inference
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Differential privacy for bayesian inference through posterior sampling
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Dstress: Efficient differentially private computations on distributed data
Papadimitriou, A., Narayan, A., and Haeberlen, A · 2017
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