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cs.LG2025
Inverse Optimization Latent Variable Models for Learning Costs Applied to Route Problems
Alan A. Lahoud, Erik Schaffernicht, Johannes A. Stork
Learning representations for solutions of constrained optimization problems (COPs) with unknown cost functions is challenging, as models like (Variational) Autoencoders struggle to…
cs.LG2024
Learning Solutions of Stochastic Optimization Problems with Bayesian Neural Networks
Alan A. Lahoud, Erik Schaffernicht, Johannes A. Stork
Mathematical solvers use parametrized Optimization Problems (OPs) as inputs to yield optimal decisions. In many real-world settings, some of these parameters are unknown or uncerta…
cs.LG2024
DataSP: A Differential All-to-All Shortest Path Algorithm for Learning Costs and Predicting Paths with Context
Alan A. Lahoud, Erik Schaffernicht, Johannes A. Stork
Learning latent costs of transitions on graphs from trajectories demonstrations under various contextual features is challenging but useful for path planning. Yet, existing methods…