# Private Empirical Risk Minimization Beyond the Worst Case: The Effect of the Constraint Set Geometry

@article{Talwar2014PrivateER, title={Private Empirical Risk Minimization Beyond the Worst Case: The Effect of the Constraint Set Geometry}, author={Kunal Talwar and Abhradeep Thakurta and Li Zhang}, journal={ArXiv}, year={2014}, volume={abs/1411.5417} }

Empirical Risk Minimization (ERM) is a standard technique in machine learning, where a model is selected by minimizing a loss function over constraint set. When the training dataset consists of private information, it is natural to use a differentially private ERM algorithm, and this problem has been the subject of a long line of work started with Chaudhuri and Monteleoni 2008. A private ERM algorithm outputs an approximate minimizer of the loss function and its error can be measured as the…

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## 43 Citations

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