8 citations · 16 across the 6 of their papers we have counts for
7 papers
A Hybrid Inverse Optimization-Stochastic Programming Framework for Network Protection
Stephanie Allen, Daria Terekhov, Steven A. Gabriel
Disaster management is a complex problem demanding sophisticated modeling approaches. We propose utilizing a hybrid method involving inverse optimization to parameterize the cost f…
Comparing Inverse Optimization and Machine Learning Methods for Imputing a Convex Objective Function
Elaheh H. Iraj, Daria Terekhov
Inverse optimization (IO) aims to determine optimization model parameters from observed decisions. However, IO is not part of a data scientist's toolkit in practice, especially as…
Learning Linear Programs from Optimal Decisions
Yingcong Tan, Daria Terekhov, Andrew Delong
We propose a flexible gradient-based framework for learning linear programs from optimal decisions. Linear programs are often specified by hand, using prior knowledge of relevant c…
Deep Inverse Optimization
Yingcong Tan, Andrew Delong, Daria Terekhov
Given a set of observations generated by an optimization process, the goal of inverse optimization is to determine likely parameters of that process. We cast inverse optimization a…
An Ensemble Learning Framework for Model Fitting and Evaluation in Inverse Linear Optimization
Aaron Babier, Timothy C. Y. Chan, Taewoo Lee +2
We develop a generalized inverse optimization framework for fitting the cost vector of a single linear optimization problem given multiple observed decisions. This setting is motiv…
Inverse Optimization: Closed-form Solutions, Geometry and Goodness of fit
Timothy C. Y. Chan, Taewoo Lee, Daria Terekhov
In classical inverse linear optimization, one assumes a given solution is a candidate to be optimal. Real data is imperfect and noisy, so there is no guarantee this assumption is s…
A Constraint Programming Approach for Solving a Queueing Control Problem
Daria Terekhov, J. Christopher Beck
In a facility with front room and back room operations, it is useful to switch workers between the rooms in order to cope with changing customer demand. Assuming stochastic custome…