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  • title: Random Hypervolume Scalarizations for Provable Multi-Objective Black Box Optimization
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            Random Hypervolume Scalarizations for Provable Multi-Objective Black Box Optimization
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            Random Hypervolume Scalarizations for Provable Multi-Objective Black Box Optimization

            Jul 12, 2020

            Speakers

            DG

            Daniel Golovin

            Speaker · 0 followers

            QZ

            Qiuyi Zhank

            Speaker · 0 followers

            About

            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. Quantitatively, we wish to maximize the standard hypervolume indicator metric, which measures the dominated hypervolume of the entire set of chos…

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            The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning. ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics. ICML is one of the fastest growing artificial intelligence conferences in the world. Participants at ICML span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.

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