MO1.A.1
DATA-DRIVEN CONSTRUCTION OF DECISION RULES FOR INTERPRETABLE OPTIMIZATION
Weigu Wen, Jnana Sai Jagana, Qi Zhang, University of Minnesota, United States
Session:
MO1.A: Machine Learning for Optimization Oral
Track:
Track
Location:
Murphy
Presentation Time:
Mon, 11 Jan, 14:00 - 14:20 MST (UTC -7)
Presentation
Discussion
Resources
No resources available.
Session MO1.A
MO1.A.1: DATA-DRIVEN CONSTRUCTION OF DECISION RULES FOR INTERPRETABLE OPTIMIZATION
Weigu Wen, Jnana Sai Jagana, Qi Zhang, University of Minnesota, United States
MO1.A.2: Learning To Optimize: A Neural Network based Solver for Nonlinear Parametric Optimization
Bimol Nath Roy, Rahul Golder, M M Faruque Hasan, Texas A&M University, United States
MO1.A.3: Graph Neural Network-Guided Heuristics for Optimal Large-Scale Supply Chain Reconfigurations
Lucy Anthony, Lavinia Marina Paola Ghilardi, Imperial College London, United Kingdom; Olga Walz, Steffen Klosterhalfen, BASF, Germany; Antonio Del Rio Chanona, Calvin Tsay, Imperial College London, United Kingdom
MO1.A.4: Learning Optimization-Compatible Energy Landscapes for Mixed Integer Programming via Graph Energy-Based Models
Niki Triantafyllou, Maria M. Papathanasiou, Imperial College London, United Kingdom
Resources
No resources available.