Regression is a very relevant problem in machine learning, with many different available
approaches. The current work presents a comparison of a large collection composed by 77 …

Owing to its recent advance, machine learning has spawned a large collection of solar
forecasting works. In particular, machine learning is currently one of the most popular …

Groundwater resources are vital to ecosystems and livelihoods. Excessive groundwater
withdrawals can cause groundwater levels to decline,,,,,,,,–, resulting in seawater intrusion …

The recent developments in the modern data collection tools, techniques, and storage
capabilities are leading towards huge volume of data. The dimensions of data indicate the …

Despite the great empirical success of deep reinforcement learning, its theoretical
foundation is less well understood. In this work, we make the first attempt to theoretically …

We introduce a simple new approach to variable selection in linear regression, with a
particular focus on quantifying uncertainty in which variables should be selected. The …

On the rate of convergence of fully connected deep neural network regression estimates Page
1 The Annals of Statistics 2021, Vol. 49, No. 4, 2231–2249 https://doi.org/10.1214/20-AOS2034 …

We consider neural networks with a single hidden layer and non-decreasing positively
homogeneous activation functions like the rectified linear units. By letting the number of …

This first chapter formulates the objectives of compressive sensing. It introduces the
standard compressive problem studied throughout the book and reveals its ubiquity in many …

The success of machine learning algorithms generally depends on data representation, and
we hypothesize that this is because different representations can entangle and hide more or …