Abstract Machine learning with missing data has been approached in many different ways,
including feature imputation where missing feature values are estimated based on observed …

In this paper, we present a new deep learning architecture for addressing the problem of
supervised learning with sparse and irregularly sampled multivariate time series. The …

Abstract Graph Convolutional Network (GCN) has experienced great success in graph
analysis tasks. It works by smoothing the node features across the graph. The current GCN …

We propose a general, theoretically justified mechanism for processing missing data by
neural networks. Our idea is to replace typical neuron's response in the first hidden layer by …

Reliable data delivery in the Internet of Things (IoT) is very important in order to provide IoT-
based services with the required quality. However, IoT data delivery may not be successful …

Society increasingly relies on machine learning models for automated decision making. Yet,
efficiency gains from automation have come paired with concern for algorithmic …

Knowledge discovery in ergonomics is complicated by the presence of missing data,
because most methodologies do not tolerate incomplete sample instances. Data-miners …

Abstract<? Pub Inc> Learning, inference, and prediction in the presence of missing data are
pervasive problems in machine learning and statistical data analysis. This thesis focuses on …

Multidimensional data (ie, tensors) with missing entries are common in practice. Extracting
features from incomplete tensors is an important yet challenging problem in many fields …

Feature selection is an important preprocessing task for many machine learning and pattern
recognition applications, including regression and classification. Missing data are …