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cs.LG2024★ 1 cited
Compression of Structured Data with Autoencoders: Provable Benefit of Nonlinearities and Depth
Kevin Kögler, Alexander Shevchenko, Hamed Hassani +1
Autoencoders are a prominent model in many empirical branches of machine learning and lossy data compression. However, basic theoretical questions remain unanswered even in a shall…
cs.IT2024
Tight Bounds for Linear and Non-Linear Contraction of Divergences via Duality
Amedeo Roberto Esposito, Marco Mondelli
We develop a novel framework for bounding the contraction of information divergences, using duality and associated norms in Orlicz spaces. By working in the dual space, we obtain a…