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Centre Inria d', Universit茅 C么te d'Azur, INRIA, Universit茅 de la Polyn茅sie franc 赂aise, Princeton University
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Decentralized learning can match centralized performance by sharing only Gibbs measures, not datasets, opening new avenues for privacy-preserving collaboration.
RevIN, a popular normalization technique for time series forecasting, can be significantly improved by removing redundant components, challenging current best practices.
