Given a learning task where the data is distributed among several parties, communication is one of the fundamental resources which the parties would like to minimize.We present a distributed boosting algorithm which is resilient to a limited amount of noise. Our algorithm is similar to classical boosting algorithms, although it is equipped with a new component, inspired by Impagliazzo's hard-core lemma (Impagliazzo, 1995), adding a robustness quality to the algorithm. We also complement this result by showing that resilience to any asymptotically larger noise is not achievable by a communication-efficient algorithm.
Yuval filmus (Technion)
After completing a PhD thesis under Prof. Toni Pitassi at the University of Toronto and postdocs at the Simons Institute in Berkeley and the Institute for Advanced Study, I joined the Technion at 2015, where I am currently an Associate Professor.
Idan Mehalel (Technion)
Shay Moran (-)
Related Events (a corresponding poster, oral, or spotlight)
2022 Poster: A Resilient Distributed Boosting Algorithm »
Wed. Jul 20th through Thu the 21st Room Hall E #1209
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2021 : Towards a Unified Information-Theoretic Framework for Generalization »
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