Methods to classify objects into two or more classes are at the core of various disciplines.
When a set of objects with their true classes is available, a supervised classifier can be …

Entity resolution is the process of merging and removing duplicate records from multiple
data sources, often in the absence of unique identifiers. Bayesian models for entity …

Databases covering all individuals of a population are increasingly used for research and
decision-making. The massive size of such databases is often mistaken as a guarantee for …

Scholarly data is growing continuously containing information about the articles from a
plethora of venues including conferences, journals, etc. Many initiatives have been taken to …

Deep learning provides powerful methods to impute structured information from large-scale,
unstructured text and image datasets. For example, economists might wish to detect the …

Entity resolution (ER) is the task of finding records that refer to the same real-world entities. A
common scenario, which we refer to as Clean-Clean ER, is to resolve records across two …

Research on the activities and influence of interest groups in state legislatures faces a data
problem: we are missing a comprehensive, systematic dataset of interest groups' policy …

Background Cancer registries link a large number of electronic health records reported by
medical institutions to already registered records of the matching individual and tumor …

In many healthcare and social science applications, information about units is dispersed
across multiple data files. Linking records across files is necessary to estimate the …

The availability of both structured and unstructured databases, such as electronic health
data, social media data, patent data, and surveys that are often updated in real time, among …