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Learning to Optimize in Swarms
Yue Cao · Tianlong Chen · Zhangyang Wang · Yang Shen

Wed Dec 11 05:00 PM -- 07:00 PM (PST) @ East Exhibition Hall B + C #34

Learning to optimize has emerged as a powerful framework for various optimization and machine learning tasks. Current such "meta-optimizers" often learn in the space of continuous optimization algorithms that are point-based and uncertainty-unaware. To overcome the limitations, we propose a meta-optimizer that learns in the algorithmic space of both point-based and population-based optimization algorithms. The meta-optimizer targets at a meta-loss function consisting of both cumulative regret and entropy. Specifically, we learn and interpret the update formula through a population of LSTMs embedded with sample- and feature-level attentions. Meanwhile, we estimate the posterior directly over the global optimum and use an uncertainty measure to help guide the learning process. Empirical results over non-convex test functions and the protein-docking application demonstrate that this new meta-optimizer outperforms existing competitors. The codes are publicly available at: https://github.com/Shen-Lab/LOIS

Author Information

Yue Cao (Texas A&M University)
Tianlong Chen (Texas A&M University)
Zhangyang Wang (TAMU)
Yang Shen (Texas A&M University)

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