Sentence examples for generalization bounds from inspiring English sources

Derives generalization bounds using both stability and VC theory.

Topics include online learning, kernel methods, generalization bounds (uniform convergence), and spectral methods.

Topics include generalization bounds, implicit regularization, the theory of deep learning, spectral methods, and online learning and bandits problems.

Q. Liao, Miranda, B., Hidary, J., and Poggio, T., "Classical generalization bounds are surprisingly tight for Deep Networks".

We provide generalization bounds for dictionary learning using smooth sparse coding and show how the sample complexity depends on the L1 norm of kernel function used.

The observation, motivated by a previous theoretical analysis of overparametrization and overfitting, not only demonstrates the validity of classical generalization bounds for deep learning but suggests that they are tight.

Similar generalizations to bounds for the conditional value-at-risk measure (CVaR) can also be obtained (see Hürlimann [21], Theorem 4.2, for the special case (m = 2)).

Consequently, each side reduces the breadth of the problem through either generalizations or bounds.

The Bounded Set-up Knapsack Problem (BSKP) is a generalization of the Bounded Knapsack Problem (BKP), where each item type has a set-up weight and a set-up value that are included in the knapsack and the objective function value, respectively, if any copies of that item type are in the knapsack.

We introduce the notion of Schwartz (co)homology of a discrete group with a length function which is the natural generalization of the bounded cohomology of a discrete group.

Let (T_{beta}= -Delta+V)^{-beta}V^{beta}) and (bin operatorname{BMO}_{sigma}(rho)), where (operatorname{BMO}_{sigma}(rho)) denotes a function space associated with L. Such spaces were first introduced by Bongioanni, Harboure, and Salinas [14] as a generalization of the bounded mean oscillation space (operatorname{BMO}(mathbb{R}^{n})).