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Learning from aggregated data with a maximum entropy model

Alexandre Gilotte
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Ahmed Ben Yamed
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David Rohde
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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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Dates and versions

hal-03770740 , version 1 (06-09-2022)
hal-03770740 , version 2 (04-10-2022)

Identifiers

  • HAL Id : hal-03770740 , version 2
  • ARXIV : 3796293

Cite

Alexandre Gilotte, Ahmed Ben Yamed, David Rohde. Learning from aggregated data with a maximum entropy model. 2022. ⟨hal-03770740v2⟩
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