Estimation of functions from sparse and noisy data is a central theme in machine learning. In
the last few years, many algorithms have been developed that exploit Tikhonov …

Most of the currently used techniques for linear system identification are based on classical
estimation paradigms coming from mathematical statistics. In particular, maximum likelihood …

The goal of learning theory is to approximate a function from sample values. To attain this
goal learning theory draws on a variety of diverse subjects, specifically statistics …

The regression problem in learning theory is investigated with least square Tikhonov
regularization schemes in reproducing kernel Hilbert spaces (RKHS). We follow our …

We study distributed learning with the least squares regularization scheme in a reproducing
kernel Hilbert space (RKHS). By a divide-and-conquer approach, the algorithm partitions a …

In this work, we introduce the average top-$ k $(\atk) loss as a new ensemble loss for
supervised learning. The\atk loss provides a natural generalization of the two widely used …

Recent works have revealed an essential paradigm in designing loss functions that
differentiate individual losses versus aggregate losses. The individual loss measures the …

In this paper, we study regression problems over a separable Hilbert space with the square
loss, covering non-parametric regression over a reproducing kernel Hilbert space. We …

Under mild assumptions on the kernel, we obtain the best known error rates in a regularized
learning scenario taking place in the corresponding reproducing kernel Hilbert space …

We discuss how a large class of regularization methods, collectively known as spectral
regularization and originally designed for solving ill-posed inverse problems, gives rise to …