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\section*{Broader Impact}
This work develops a new tool for derivative-free optimization and provides supporting theoretical convergence guarantees. Derivative-free methods can significantly impact a variety of practical applications where exact system models are unknown or are known but are too complex to differentiate. An example are decision making problems that incorporate human preference, which usually cannot be modeled using explicit mathematical formulas. Other examples include machine learning and reinforcement learning problems that rely on deep neural networks. Such problems are common in many everyday systems, including robotic, cyber-physical systems, and IoT systems. The zeroth-order oracle proposed in this paper significantly improves the performance of derivative-free methods and, therefore, their applicability in controlling such systems.
At the same time, derivative-free optimization methods can also be used to attack black-box cyber or physical systems. Therefore, as the study in this field advances, safety concerns related to zeroth-order attacks must also be addressed.
{"config": "arxiv", "file": "2006.10820/broad.tex"}