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Found 11 papers, 0 papers with code
Bootstrap SGD: Algorithmic Stability and Robustness
In this paper some methods to use the empirical bootstrap approach for stochastic gradient descent (SGD) to minimize the empirical risk over a separable Hilbert space are investigated from the view point of algorithmic stability and statistical robustness.
Optimality of Robust Online Learning
In this paper, we study an online learning algorithm with a robust loss function $\mathcal{L}_{\sigma}$ for regression over a reproducing kernel Hilbert space (RKHS).
Total Stability of SVMs and Localized SVMs
Regularized kernel-based methods such as support vector machines (SVMs) typically depend on the underlying probability measure $\mathrm{P}$ (respectively an empirical measure $\mathrm{D}_n$ in applications) as well as on the regularization parameter $\lambda$ and the kernel $k$.
On the robustness of kernel-based pairwise learning
It is shown that many results on the statistical robustness of kernel-based pairwise learning can be derived under basically no assumptions on the input and output spaces.
Total stability of kernel methods
However, the actually used kernel often depends on one or on a few hyperparameters or the kernel is even data dependent in a much more complicated manner.
A short note on extension theorems and their connection to universal consistency in machine learning
Statistical machine learning plays an important role in modern statistics and computer science.
On the Robustness of Regularized Pairwise Learning Methods Based on Kernels
Regularized empirical risk minimization including support vector machines plays an important role in machine learning theory.
Learning rates for the risk of kernel based quantile regression estimators in additive models
Additive models play an important role in semiparametric statistics.
Universal Kernels on Non-Standard Input Spaces
We apply this technique for the following special cases: universal kernels on the set of probability measures, universal kernels based on Fourier transforms, and universal kernels for signal processing.
Fast Learning from Non-i.i.d. Observations
We prove an oracle inequality for generic regularized empirical risk minimization algorithms learning from $\a$-mixing processes.
Sparsity of SVMs that use the epsilon-insensitive loss
In this paper lower and upper bounds for the number of support vectors are derived for support vector machines (SVMs) based on the epsilon-insensitive loss function.
