Corpus ID: 220381492

Oracle Efficient Private Non-Convex Optimization

@inproceedings{Neel2020OracleEP,
  title={Oracle Efficient Private Non-Convex Optimization},
  author={Seth Neel and A. Roth and G. Vietri and Z. Wu},
  booktitle={ICML 2020},
  year={2020}
}
  • Seth Neel, A. Roth, +1 author Z. Wu
  • Published in ICML 2020
  • Computer Science, Mathematics
  • One of the most effective algorithms for differentially private learning and optimization is objective perturbation. This technique augments a given optimization problem (e.g. deriving from an ERM problem) with a random linear term, and then exactly solves it. However prior analyses of this approach crucially rely on the convexity and smoothness of the objective function. We give two algorithms that extend this approach substantially. The first algorithm requires nothing except boundedness of… CONTINUE READING
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