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Distributed, Private and Bayesian Machine Learning

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Data and compute are resources distributed at different locations around the globe. To reap the full benefits of these essential ingredients for machine learning we need to develop algorithms that operate distributed, are privacy preserving by design and treat model uncertainty in a principled manner. In this talk I will discuss the progress that we have made at AMLAB and QUVA Lab towards these goals. I will in particular discuss an interesting synergy that seems to exists between these three goals.

This talk is part of the Microsoft Research Cambridge, public talks series.

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