Learning from aggregated data with a maximum entropy model

• URL: http://arxiv.org/abs/2210.02450v1
• Date: Wed, 5 Oct 2022 09:17:27 GMT
• Title: Learning from aggregated data with a maximum entropy model
• Authors: Alexandre Gilotte, Ahmed Ben Yahmed, David Rohde
• Abstract summary: We show how a new model, similar to a logistic regression, may be learned from aggregated data only by approximating the unobserved feature distribution with a maximum entropy hypothesis. We present empirical evidence on several public datasets that the model learned this way can achieve performances comparable to those of a logistic model trained with the full unaggregated data.
• Score: 73.63512438583375
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Aggregating a dataset, then injecting some noise, is a simple and common way to release differentially private data.However, aggregated data -- even without noise -- is not an appropriate input for machine learning classifiers.In this work, we show how a new model, similar to a logistic regression, may be learned from aggregated data only by approximating the unobserved feature distribution with a maximum entropy hypothesis. The resulting model is a Markov Random Field (MRF), and we detail how to apply, modify and scale a MRF training algorithm to our setting. Finally we present empirical evidence on several public datasets that the model learned this way can achieve performances comparable to those of a logistic model trained with the full unaggregated data.

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