March 9, 2020
Machine Learning is invaluable for extracting insights from large volumes of data. A key assumption enabling many methods, however, is having access to training data comprising independent observations from the entire distribution of relevant data. In practice, data is commonly missing due to measurement limitations, legal restrictions, or data collection and sharing practices. Moreover, observations are commonly collected on a network, a spatial or a temporal domain and may be intricately dependent.