The randomly pivoted Cholesky algorithm (RPCholesky) computes a factorized rank‐kk
approximation of an N× NN*N positive‐semidefinite (psd) matrix. RPCholesky requires only …

The theory of Koopman operators allows to deploy non-parametric machine learning
algorithms to predict and analyze complex dynamical systems. Estimators such as principal …

In this paper, we prove state-of-the-art Bayesian regret bounds for Thompson Sampling in
reinforcement learning in a multitude of settings. We present a refined analysis of the …

Kernel ridge regression (KRR) is a fundamental computational tool, appearing in problems
that range from computational chemistry to health analytics, with a particular interest due to …

While many nonlinear pattern recognition and data mining tasks rely on embedding the data
into a latent space, one often needs to extract the patterns in the input space. Estimating the …

The lack of sufficient flexibility is the key bottleneck of kernel-based learning that relies on
manually designed, pre-given, and non-trainable kernels. To enhance kernel flexibility, this …

Kernel methods are a popular class of nonlinear predictive models in machine learning.
Scalable algorithms for learning kernel models need to be iterative in nature, but …

The search for prognostic biomarkers capable of predicting patient outcomes, by analyzing
gene expression in tissue samples and other molecular profiles, remains largely focused on …

Column selection is an essential tool for structure-preserving low-rank approximation, with
wide-ranging applications across many fields, such as data science, machine learning, and …

Concept-based learning, a promising approach in machine learning, emphasizes the value
of high-level representations called concepts. However, despite growing interest in concept …