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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 …
Emergence of heavy tails in homogenized stochastic gradient descent
It has repeatedly been observed that loss minimization by stochastic gradient descent (SGD)
leads to heavy-tailed distributions of neural network parameters. Here, we analyze a …
leads to heavy-tailed distributions of neural network parameters. Here, we analyze a …
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 …
Algorithmic Stability of Stochastic Gradient Descent with Momentum under Heavy-Tailed Noise
Understanding the generalization properties of optimization algorithms under heavy-tailed
noise has gained growing attention. However, the existing theoretical results mainly focus …
noise has gained growing attention. However, the existing theoretical results mainly focus …