Stochastics and Statistics Seminar

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Statistical Inference Under Information Constraints: User level approaches


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…

Learning learning-augmented algorithms. The example of stochastic scheduling


Abstract: In this talk, I will argue that it is sometimes possible to learn, with techniques originated from bandits, the "hints" on which learning-augmented algorithms rely to improve worst-case performances. We will describe this phenomenon, the combination of online learning with competitive analysis, on the example of stochastic online scheduling. We shall quantify the merits of this approach by computing and comparing non-asymptotic expected competitive ratios (the standard performance measure of algorithms) Bio: Vianney Perchet is a professor at the…

Adaptivity in Domain Adaptation and Friends


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…

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