Hierarchical Collaborative Hyper-parameter Tuning
This addresses the critical problem of efficient hyper-parameter tuning for machine learning practitioners, though it appears incremental as an enhancement to existing randomized methods.
The paper tackles hyper-parameter tuning in machine learning by developing a distributed multi-agent system with a hierarchical architecture, which outperformed underlying randomized strategies in classification error and function evaluations, especially in higher dimensions.
Hyper-parameter Tuning is among the most critical stages in building machine learning solutions. This paper demonstrates how multi-agent systems can be utilized to develop a distributed technique for determining near-optimal values for any arbitrary set of hyper-parameters in a machine learning model. The proposed method employs a distributedly formed hierarchical agent-based architecture for the cooperative searching procedure of tuning hyper-parameter values. The presented generic model is used to develop a guided randomized agent-based tuning technique, and its behavior is investigated in both machine learning and global function optimization applications. According the empirical results, the proposed model outperformed both of its underlying randomized tuning strategies in terms of classification error and function evaluations, notably in higher number of dimensions.