The problem of learning functions over spaces of probabilities-or distribution regression-is
gaining significant interest in the machine learning community. The main challenge in these …

In this paper, we consider the coefficient-based regularized distribution regression which
aims to regress from probability measures to real-valued responses over a reproducing …

Regularization schemes for regression have been widely studied in learning theory and
inverse problems. In this paper, we study regularized distribution regression (DR) which …

In this paper, we consider the coefficient-based regularized distribution regression which
aims to regress from probability measures to real-valued responses over a reproducing …

In this paper, we aim at establishing an approximation theory and a learning theory of
distribution regression via a fully connected neural network (FNN). In contrast to the classical …

We establish a deep learning theory for distribution regression with deep convolutional
neural networks (DCNNs). Deep learning based on structured deep neural networks has …

In this paper, we study the learning rate of distribution regression for strong mixing
sequences. The distribution regression containing two stages of sampling aims at …