6 citations · 10 across the 5 of their papers we have counts for
6 papers
Optimality-Based Clustering: An Inverse Optimization Approach
Zahed Shahmoradi, Taewoo Lee
We propose a new clustering approach, called optimality-based clustering, that clusters data points based on their latent decision-making preferences. We assume that each data poin…
Quantile Inverse Optimization: Improving Stability in Inverse Linear Programming
Zahed Shahmoradi, Taewoo Lee
Inverse linear programming (LP) has received increasing attention due to its potential to generate efficient optimization formulations that can closely replicate the behavior of a…
Approximate Submodularity and Its Implications in Discrete Optimization
Temitayo Ajayi, Taewoo Lee, Andrew Schaefer
Submodularity is a key property in discrete optimization. Submodularity has been widely used for analyzing the greedy algorithm to give performance bounds and providing insight int…
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…
Trade-off preservation in inverse multi-objective convex optimization
Timothy C. Y. Chan, Taewoo Lee
We present a new inverse optimization methodology for multi-objective convex optimization that accommodates an input solution that may not be Pareto optimal and determines a weight…
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…