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20192025
most citedProvably Robust Blackbox Optimization for Reinforcement Learning

11 citations · 35 across the 10 of their papers we have counts for

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math.OC2019★ 6 cited

From Complexity to Simplicity: Adaptive ES-Active Subspaces for Blackbox Optimization

Krzysztof Choromanski, Aldo Pacchiano, Jack Parker-Holder +1

We present a new algorithm ASEBO for optimizing high-dimensional blackbox functions. ASEBO adapts to the geometry of the function and learns optimal sets of sensing directions, whi…