# Random Hypervolume Scalarizations for Provable Multi-Objective Black Box Optimization

Daniel GolovinQiuyi Zhang

Single-objective black box optimization (also known as zeroth-order optimization) is the process of minimizing a scalar objective $f(x)$, given evaluations at adaptively chosen inputs $x$. In this paper, we consider multi-objective optimization, where $f(x)$ outputs a vector of possibly competing objectives and the goal is to converge to the Pareto frontier... (read more)

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