A new open-access paper proves a priori error estimates for kernel methods used as ansatz functions within the Deep Ritz ...

Are two sets of data genuinely different, or is it because of randomness? This question, known as the two-sample testing problem, becomes notoriously difficult in modern datasets, because they are ...

Kernel methods form a foundational framework in statistical learning theory, enabling algorithms to operate in implicitly defined high-dimensional feature spaces without ever computing feature vectors ...

Kernel ridge regression (KRR) is a regression technique for predicting a single numeric value and can deliver high accuracy for complex, non-linear data. KRR combines a kernel function (most commonly ...