112 citations · 123 across the 4 of their papers we have counts for
4 papers
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…
Generating and designing DNA with deep generative models
Nathan Killoran, Leo J. Lee, Andrew Delong +2
We propose generative neural network methods to generate DNA sequences and tune them to have desired properties. We present three approaches: creating synthetic DNA sequences using…
Submodularization for Quadratic Pseudo-Boolean Optimization
Lena Gorelick, Yuri Boykov, Olga Veksler +2
Many computer vision problems require optimization of binary non-submodular energies. We propose a general optimization framework based on local submodular approximations (LSA). Un…