ML x OR: Mathematical Foundations and Operational Integration of Machine Learning for Uncertainty-Aware Decision-Making

Much of traditional decision-making science is grounded in the mathematical formulations and analyses of structured systems to recommend decisions that are optimized, robust, and uncertainty-aware. This scientific approach, rooted in the field of Operations Research (OR), has evolved through decades of advancements in stochastic modeling, computational simulation and optimization, and exhibits key strengths in methodological rigor and uncertainty encoding. On the other hand, recent advances in the AI/ML space have eschewed this model-based paradigm and increasingly embraced, to great success, model-free algorithmic design frameworks. This workshop, which is the first NeurIPS workshop explicitly themed and structured on ML-OR synergization, aspires to present recent developments, challenges and emerging research to accelerate ML-OR synthesis. By integrating ML into established OR methodologies, we have the opportunities to produce more data-centric and adaptive solutions for complex decision-making tasks that could propel, in a much faster-paced manner, the frontier of "optimality" across many relevant applications. Concomitantly, the goal is also to explore how model-based principled OR approaches can help alleviate issues revolving around "black box" systems, and provide paths to enhance interpretability, trust, and performance analysis.