118 citations · 152 across the 16 of their papers we have counts for
6 papers · 1 filter
End-to-End Optimization and Learning of Fair Court Schedules
My H Dinh, James Kotary, Lauryn P. Gouldin +2
Criminal courts across the United States handle millions of cases every year, and the scheduling of those cases must accommodate a diverse set of constraints, including the prefere…
Learning Joint Models of Prediction and Optimization
James Kotary, Vincenzo Di Vito, Jacob Cristopher +2
The Predict-Then-Optimize framework uses machine learning models to predict unknown parameters of an optimization problem from exogenous features before solving. This setting is co…
Metric Learning to Accelerate Convergence of Operator Splitting Methods for Differentiable Parametric Programming
Ethan King, James Kotary, Ferdinando Fioretto +1
Recent work has shown a variety of ways in which machine learning can be used to accelerate the solution of constrained optimization problems. Increasing demand for real-time decis…
Learning Constrained Optimization with Deep Augmented Lagrangian Methods
James Kotary, Ferdinando Fioretto
Learning to Optimize (LtO) is a problem setting in which a machine learning (ML) model is trained to emulate a constrained optimization solver. Learning to produce optimal and feas…
End-to-End Learning for Fair Multiobjective Optimization Under Uncertainty
My H Dinh, James Kotary, Ferdinando Fioretto
Many decision processes in artificial intelligence and operations research are modeled by parametric optimization problems whose defining parameters are unknown and must be inferre…
Learning Fair Ranking Policies via Differentiable Optimization of Ordered Weighted Averages
My H. Dinh, James Kotary, Ferdinando Fioretto
Learning to Rank (LTR) is one of the most widely used machine learning applications. It is a key component in platforms with profound societal impacts, including job search, health…