Local Deep Kernel Learning for Efficient Non-linear SVM Prediction

More Generality in Efficient Multiple Kernel Learning

Online Supervised Dimension Reduction with Random Features: Diagnostics and Computational Trade-offs

Accurate optimization of a supervised spectral objective need not produce an accurate population subspace or a better predictive representation. We investigate these distinctions for Online Kernel Supervised Principal Component Analysis (OKSPCA), which combines a centered cross-moment in finite random-feature coordinat...

Kernel Methods for Learning Operators with Multiple Inputs and Outputs

This work introduces a general kernel-based encoder-decoder framework for operator learning that separates observation, representation, learning, and reconstruction, and develops this framework for multi-input, multi-output operator learning, where operators map between products of potentially distinct function spaces.

Robust SVM Classification with ℓ 0 -Norm Feature Selection

We introduce a robust classification model designed for feature selection. Support vector machine (SVM) models continue to play a crucial role in binary classification, particularly with tabular data. Their robust variants are essential for developing classifiers that remain stable despite shifts in data distribution...

Convex losses and their applications to SVM, SVR, and Shallow Neural Networks

This preliminary study shows that using pattern correlations inside the loss function could in theory enhance the generalization performances on some data-sets, and shows that generalization measures are the same with or without the new losses.

Learning of deep neural network regression estimates using gradient descent with pruning

Estimation of a regression function from independent and identically distributed data is considered. The $L_2$ error with integration with respect to the design variable is used as the error criterion. An initially randomly pruned fully connected deep neural network with logistic squasher as activation function is fitt...