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Uniform Generalization Bounds on Data-Dependent Hypothesis Sets via PAC-Bayesian Theory on Random Sets
We propose data-dependent uniform generalization bounds by approaching the problem
from a PAC-Bayesian perspective. We first apply the PAC-Bayesian framework on “random …
from a PAC-Bayesian perspective. We first apply the PAC-Bayesian framework on “random …
Generalization bounds for heavy-tailed SDEs through the fractional Fokker-Planck equation
Understanding the generalization properties of heavy-tailed stochastic optimization
algorithms has attracted increasing attention over the past years. While illuminating …
algorithms has attracted increasing attention over the past years. While illuminating …
Understanding the Generalization Error of Markov algorithms through Poissonization
Using continuous-time stochastic differential equation (SDE) proxies to stochastic
optimization algorithms has proven fruitful for understanding their generalization abilities. A …
optimization algorithms has proven fruitful for understanding their generalization abilities. A …