207 citations · 453 across the 21 of their papers we have counts for
25 papers
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…
A Fairness Analysis on Private Aggregation of Teacher Ensembles
Cuong Tran, My H. Dinh, Kyle Beiter +1
The Private Aggregation of Teacher Ensembles (PATE) is an important private machine learning framework. It combines multiple learning models used as teachers for a student model th…
Learning Hard Optimization Problems: A Data Generation Perspective
James Kotary, Ferdinando Fioretto, Pascal Van Hentenryck
Optimization problems are ubiquitous in our societies and are present in almost every segment of the economy. Most of these optimization problems are NP-hard and computationally de…
A Privacy-Preserving and Trustable Multi-agent Learning Framework
Anudit Nagar, Cuong Tran, Ferdinando Fioretto
Distributed multi-agent learning enables agents to cooperatively train a model without requiring to share their datasets. While this setting ensures some level of privacy, it has b…
Decision Making with Differential Privacy under a Fairness Lens
Ferdinando Fioretto, Cuong Tran, Pascal Van Hentenryck
Agencies, such as the U.S. Census Bureau, release data sets and statistics about groups of individuals that are used as input to a number of critical decision processes. To conform…
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…