Robust Distributed Learning and Robust Learning Machines (Research Statement)

Whether it occurs in artificial or biological substrates, {\it learning} is a {distributed} phenomenon in at least two aspects. First, meaningful data and experiences are rarely found in one location, hence {\it learners} have a strong incentive to work together, through {\it distributed learning}. Second, a learner is itself a distributed system, a {\it learning machine}, made of more basic processes; the change in the connections between these basic processes is what allows learning. This high-level abstraction encompasses a large set of learning situations, from nervous systems, to metabolic networks in an organism, to data centers, where several machines are working together to recommend personalized content for a billion-users social media. In both levels of distribution, a system's ability to cope with the failure of some of its components is crucial. My research explores the robustness of learning systems from %these two perspectives. The first aspect is {\it coarse-grained}, considering the unit of failure as a whole learner. The second is {\it fine-grained}, considering the unit of failure as the basic component of the learner (e.g. a neuron or a synapse).

Dec 28 2019

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 Record created 2019-12-28, last modified 2020-10-29

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