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Representation Learning for High-Dimensional Data Collection under Local Differential Privacy
In this work, our contributions are two-fold: first, by adapting state-of-the-art techniques from representation learning, we introduce a novel approach to learning LDP mechanisms.
Improving latent variable descriptiveness with AutoGen
Powerful generative models, particularly in Natural Language Modelling, are commonly trained by maximizing a variational lower bound on the data log likelihood.
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