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Why It Matters
Online learning is an attractive framework in many domains because it permits well-defined learning even when data are dependent or chosen adversarially.
Provenance
Discovered via ArXiv and published by ArXiv.
Key Claims
Original description
Online learning is an attractive framework in many domains because it permits well-defined learning even when data are dependent or chosen adversarially. This generality, however, comes at a steep price, introducing significant statistical and computational barriers. Recently, smoothed online learning has emerged as a promising framework that interpolates between the fully adversarial and fully stochastic settings by assuming that the conditional law of each covariate has density at most $1/σ$ with respect to some fixed base measure $μ$, and it is known to match the statistical and computation...
Discovered via ArXiv
Research papers and preprints from arXiv.
Publisher: arxiv.org
ID: http://arxiv.org/abs/2610.10499v1 · Indexed about 2 hours ago