118 citations · 152 across the 16 of their papers we have counts for
13 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…
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…
Analyzing and Enhancing the Backward-Pass Convergence of Unrolled Optimization
James Kotary, Jacob Christopher, My H Dinh +1
The integration of constrained optimization models as components in deep networks has led to promising advances on many specialized learning tasks. A central challenge in this sett…
Predict-Then-Optimize by Proxy: Learning Joint Models of Prediction and Optimization
James Kotary, Vincenzo Di Vito, Jacob Christopher +2
Many real-world decision processes are modeled by optimization problems whose defining parameters are unknown and must be inferred from observable data. The Predict-Then-Optimize f…
Decision-Focused Learning: Foundations, State of the Art, Benchmark and Future Opportunities
Jayanta Mandi, James Kotary, Senne Berden +4
Decision-focused learning (DFL) is an emerging paradigm that integrates machine learning (ML) and constrained optimization to enhance decision quality by training ML models in an e…
Backpropagation of Unrolled Solvers with Folded Optimization
James Kotary, My H. Dinh, Ferdinando Fioretto
The integration of constrained optimization models as components in deep networks has led to promising advances on many specialized learning tasks. A central challenge in this sett…
Differentiable Model Selection for Ensemble Learning
James Kotary, Vincenzo Di Vito, Ferdinando Fioretto
Model selection is a strategy aimed at creating accurate and robust models. A key challenge in designing these algorithms is identifying the optimal model for classifying any parti…
End-to-end Learning for Fair Ranking Systems
James Kotary, Ferdinando Fioretto, Pascal Van Hentenryck +1
The learning-to-rank problem aims at ranking items to maximize exposure of those most relevant to a user query. A desirable property of such ranking systems is to guarantee some no…
Fast Approximations for Job Shop Scheduling: A Lagrangian Dual Deep Learning Method
James Kotary, Ferdinando Fioretto, Pascal Van Hentenryck
The Jobs shop Scheduling Problem (JSP) is a canonical combinatorial optimization problem that is routinely solved for a variety of industrial purposes. It models the optimal schedu…
End-to-End Constrained Optimization Learning: A Survey
James Kotary, Ferdinando Fioretto, Pascal Van Hentenryck +1
This paper surveys the recent attempts at leveraging machine learning to solve constrained optimization problems. It focuses on surveying the work on integrating combinatorial solv…