The Pitfalls of Using ML-Based Optimization - IPAM at UCLA

Explore a conference talk that delves into the limitations of machine learning-based optimization in solving graph problems. Examine two specific cases where traditional optimization methods outperform ML approaches: the Force Path Cut problem and the Hypergraph Discovery problem. Gain insights into the speaker's conjectures about why ML-based optimization struggles with these challenges. Learn about the implications for targeted diffusion, cyber security, and the potential instability of embeddings. Discover the importance of considering alternative optimization techniques when dealing with complex graph structures and energy-constrained agent-task allocation scenarios.