Abstract: In this talk, we will present highlights from some of the work we have been doing in distributed inference under information constraints, such as privacy and communication. We consider basic tasks such as learning and testing of discrete as well as high dimensional distributions, when the samples are distributed across users who can then only send an information-constrained message about their sample. Of key interest to us has been the role of the various types of communication protocols (e.g., non-interactive protocols…

Abstract: Domain adaptation, transfer, multitask, meta, few-shots, representation, or lifelong learning … these are all important recent directions in ML that all touch at the core of what we might mean by ‘AI’. As these directions all concern learning in heterogeneous and ever-changing environments, they all share a central question: what information a data distribution may have about another, critically, in the context of a given estimation problem, e.g., classification, regression, bandits, etc. Our understanding of these problems is still…