## Robust Optimization for Non-Convex Objectives

We consider robust optimization problems, where the goal is to optimize in the worst case over a class of objective functions. We develop a reduction from robust improper optimization to Bayesian optimization: given an oracle that returns $\alpha$-approximate solutions for distributions over objectives, we compute a distribution over solutions that is $\alpha$-approximate in the worst case... We show that de-randomizing this solution is NP-hard in general, but can be done for a broad class of statistical learning tasks. We apply our results to robust neural network training and submodular optimization. We evaluate our approach experimentally on corrupted character classification, and robust influence maximization in networks. read more

PDF Abstract NeurIPS 2017 PDF NeurIPS 2017 Abstract

# Code Add Remove Mark official

No code implementations yet. Submit your code now

# Datasets

Add Datasets introduced or used in this paper

# Results from the Paper Edit

Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

# Methods Add Remove

No methods listed for this paper. Add relevant methods here