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9218-from-complexity-to-simplicity-adaptive-es-active-subspaces-for-blackbox-optimization.pdf

🔗 Source: papers.neurips.cc

📊 Size: 759 KB

👤 Author: Krzysztof M. Choromanski, Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang, Vikas Sindhwani

⬇️ Downloads: 60

Summary

Researchers introduce ASEBO, a new algorithm for optimizing high-dimensional blackbox functions, which adapts to the function's geometry and learns optimal sensing directions on-the-fly. ASEBO addresses the exploration-exploitation trade-off by continuously learning the bias of a lower-dimensional model used to approximate gradients, leveraging techniques from active subspaces and contextual bandits. The algorithm is tested on reinforcement learning policy optimization tasks and non-RL blackbox functions, showing it consistently learns optimal inputs with fewer queries than other methods, outperforming state-of-the-art algorithms like CMA-ES.

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Description

Researchers introduce ASEBO, a new algorithm for optimizing high-dimensional blackbox functions, which adapts to the function's geometry and learns optimal...

Technical Information

File Format: PDF
File Size: 759 KB
Pages: 11
Language: EN
Author: Krzysztof M. Choromanski, Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang, Vikas Sindhwani
Total Downloads: 60
Last Updated: 6 days ago

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