We consider multiagent stochastic optimization problems over reproducing kernel Hilbert
spaces. In this setting, a network of interconnected agents aims to learn decision functions …

Despite their attractiveness, popular perception is that techniques for nonparametric function
approximation do not scale to streaming data due to an intractable growth in the amount of …

We consider stochastic nonparametric regression problems in a reproducing kernel Hilbert
space (RKHS), an extension of expected risk minimization to nonlinear function estimation …

To set the values of the hyperparameters of a support vector machine (SVM), the method of
choice is cross-validation. Several upper bounds on the leave-one-out error of the pattern …

The core of saline-alkali land improvement is planting suitable plants. Planting rice in saline-
alkali land can not only effectively improve saline-alkali soil, but also increase grain yield …

We consider the problem of stochastic optimization with nonlinear constraints, where the
decision variable is not vector-valued but instead a function belonging to a reproducing …

We consider Markov Decision Problems defined over continuous state and action spaces,
where an autonomous agent seeks to learn a map from its states to actions so as to …

We consider policy evaluation in infinite-horizon discounted Markov decision problems with
continuous compact state and action spaces. We reformulate this task as a compositional …

Kernel-based online classification algorithms, such as the Perceptron, NORMA, and passive-
aggressive, are renowned for their computational efficiency but have been criticized for slow …

Non-parametric function approximators provide a principled way to fit nonlinear statistical
models while affording formal performance guarantees. However, their complexity …