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Fed+: A Family of Fusion Algorithms for Federated Learning
We present a class of methods for federated learning, which we call Fed+...
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Query-time Entity Resolution
Entity resolution is the problem of reconciling database references corr...
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A Practioner's Guide to Evaluating Entity Resolution Results
Entity resolution (ER) is the task of identifying records belonging to t...
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Performance Bounds for Pairwise Entity Resolution
One significant challenge to scaling entity resolution algorithms to mas...
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FedDANE: A Federated Newton-Type Method
Federated learning aims to jointly learn statistical models over massive...
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Developing a Temporal Bibliographic Data Set for Entity Resolution
Entity resolution is the process of identifying groups of records within...
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AutoER: Automated Entity Resolution using Generative Modelling
Entity resolution (ER) refers to the problem of identifying records in o...
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Entity Resolution and Federated Learning get a Federated Resolution
Consider two data providers, each maintaining records of different feature sets about common entities. They aim to learn a linear model over the whole set of features. This problem of federated learning over vertically partitioned data includes a crucial upstream issue: entity resolution, i.e. finding the correspondence between the rows of the datasets. It is well known that entity resolution, just like learning, is mistake-prone in the real world. Despite the importance of the problem, there has been no formal assessment of how errors in entity resolution impact learning. In this paper, we provide a thorough answer to this question, answering how optimal classifiers, empirical losses, margins and generalisation abilities are affected. While our answer spans a wide set of losses --- going beyond proper, convex, or classification calibrated ---, it brings simple practical arguments to upgrade entity resolution as a preprocessing step to learning. As an example, we modify a simple token-based entity resolution algorithm so that it aims at avoiding matching rows belonging to different classes, and perform experiments in the setting where entity resolution relies on noisy data, which is very relevant to real world domains. Notably, our approach covers the case where one peer does not have classes, or a noisy record of classes. Experiments display that using the class information during entity resolution can buy significant uplift for learning at little expense from the complexity standpoint.
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