141 citations · 237 across the 46 of their papers we have counts for
9 papers · 1 filter
Contextual Stochastic Optimization for School Desegregation Policymaking
Hongzhao Guan, Nabeel Gillani, Tyler Simko +2
Most US school districts draw geographic "attendance zones" to assign children to schools based on their home address, a process that can replicate existing neighborhood racial/eth…
Defining 'Good': Evaluation Framework for Synthetic Smart Meter Data
Sheng Chai, Gus Chadney, Charlot Avery +3
Access to granular demand data is essential for the net zero transition; it allows for accurate profiling and active demand management as our reliance on variable renewable generat…
Compact Optimality Verification for Optimization Proxies
Wenbo Chen, Haoruo Zhao, Mathieu Tanneau +1
Recent years have witnessed increasing interest in optimization proxies, i.e., machine learning models that approximate the input-output mapping of parametric optimization problems…
Scalable Exact Verification of Optimization Proxies for Large-Scale Optimal Power Flow
Rahul Nellikkath, Mathieu Tanneau, Pascal Van Hentenryck +1
Optimal Power Flow (OPF) is a valuable tool for power system operators, but it is a difficult problem to solve for large systems. Machine Learning (ML) algorithms, especially Neura…
Efficiently Training Deep-Learning Parametric Policies using Lagrangian Duality
Andrew Rosemberg, Alexandre Street, Davi M. Valladão +1
Constrained Markov Decision Processes (CMDPs) are critical in many high-stakes applications, where decisions must optimize cumulative rewards while strictly adhering to complex non…
Dual Lagrangian Learning for Conic Optimization
Mathieu Tanneau, Pascal Van Hentenryck
This paper presents Dual Lagrangian Learning (DLL), a principled learning methodology for dual conic optimization proxies. DLL leverages conic duality and the representation power…
Dual Interior Point Optimization Learning
Michael Klamkin, Mathieu Tanneau, Pascal Van Hentenryck
In many practical applications of constrained optimization, scale and solving time limits make traditional optimization solvers prohibitively slow. Thus, the research question of h…
Bound Tightening using Rolling-Horizon Decomposition for Neural Network Verification
Haoruo Zhao, Hassan Hijazi, Haydn Jones +3
Neural network verification aims at providing formal guarantees on the output of trained neural networks, to ensure their robustness against adversarial examples and enable their d…
Boosting Column Generation with Graph Neural Networks for Joint Rider Trip Planning and Crew Shift Scheduling
Jiawei Lu, Tinghan Ye, Wenbo Chen +1
Optimizing service schedules is pivotal to the reliable, efficient, and inclusive on-demand mobility. This pressing challenge is further exacerbated by the increasing needs of an a…