Learning to Optimize High Dimensional Optimization Problems

Explore cutting-edge approaches to solving high-dimensional optimization problems in this 43-minute conference talk. Delve into recent advancements that combine deep neural networks with reinforcement learning and search methods to outperform traditional human-driven solutions. Discover how these innovative techniques are applied to various domains, including online job scheduling, neural architecture search, and black-box optimization. Learn about the Latent Space Monte-Carlo Tree Search (LaMCTS) algorithm, its simple usage, and performance in optimizing linear policies for Mujoco tasks. Gain insights into the challenges of learning action spaces, multi-objective optimization, and the limitations of current approaches. This talk provides a comprehensive overview of state-of-the-art methods for tackling complex optimization problems in design, operations research, and scientific exploration.