Federated zeroth-order optimization (FedZO) algorithm enjoys the advantages of both zeroth-
order optimization and federated learning, and has shown exceptional performance on …

With the proliferation of machine learning applications in the real world, the demand for
explaining machine learning predictions continues to grow especially in high-stakes fields …

Sparse additive models have been successfully applied to high-dimensional data analysis
due to the flexibility and interpretability of their representation. However, the existing …

Random feature methods have been successful in various machine learning tasks, are easy
to compute, and come with theoretical accuracy bounds. They serve as an alternative …

Triplet learning, ie learning from triplet data, has attracted much attention in computer vision
tasks with an extremely large number of categories, eg, face recognition and person re …

A lack of transparency in machine learning models can limit their application. We show that
analysis of variance (ANOVA) methods extract interpretable predictive models from them …

Most existing feature selection methods in literature are linear models, so that the nonlinear
relations between features and response variables are not considered. Meanwhile, in these …

Additive models have attracted much attention for high-dimensional regression estimation
and variable selection. However, the existing models are usually limited to the single-task …

Additive model plays an important role in machine learning due to its flexibility and
interpretability in the prediction function. However, solving large-scale additive models is a …

Recently, deep neural networks have made significant breakthroughs in the image super-
resolution (SR) field. Most deep learning-based image SR methods learn an end-to-end …