论文标题
黑盒优化的蒙特卡洛树下降
Monte Carlo Tree Descent for Black-Box Optimization
论文作者
论文摘要
Black-Box优化的关键是有效地浏览具有潜在广泛变化的数值属性的输入区域，以实现低温下降和朝着Optima的快速进步。最近引入了Monte Carlo Tree搜索（MCT）方法，以通过计算平衡探索和开发的搜索空间来改善贝叶斯优化。扩展了这个有希望的框架，我们研究了如何进一步整合基于样本的下降以更快的优化。我们设计了扩展蒙特卡洛搜索树的新颖方法，并在结合了随机搜索和高斯过程的顶点上采用新的下降方法。我们提出了平衡进度和不确定性，分支选择，树木扩展和反向传播的相应规则。设计的搜索过程更加强调更快的下降速度，并将局部高斯过程用作辅助指标进行开发和探索。我们从经验上表明，所提出的算法可以在许多具有挑战性的基准问题上胜过最先进的方法。
The key to Black-Box Optimization is to efficiently search through input regions with potentially widely-varying numerical properties, to achieve low-regret descent and fast progress toward the optima. Monte Carlo Tree Search (MCTS) methods have recently been introduced to improve Bayesian optimization by computing better partitioning of the search space that balances exploration and exploitation. Extending this promising framework, we study how to further integrate sample-based descent for faster optimization. We design novel ways of expanding Monte Carlo search trees, with new descent methods at vertices that incorporate stochastic search and Gaussian Processes. We propose the corresponding rules for balancing progress and uncertainty, branch selection, tree expansion, and backpropagation. The designed search process puts more emphasis on sampling for faster descent and uses localized Gaussian Processes as auxiliary metrics for both exploitation and exploration. We show empirically that the proposed algorithms can outperform state-of-the-art methods on many challenging benchmark problems.
