Learning Linear Programs from Optimal Decisions

no code implementations • NeurIPS 2020 • Yingcong Tan, Daria Terekhov, Andrew Delong

We propose a flexible gradient-based framework for learning linear programs from optimal decisions.

Deep Inverse Optimization

1 code implementation • 3 Dec 2018 • 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.

Generating and designing DNA with deep generative models

2 code implementations • 17 Dec 2017 • Nathan Killoran, Leo J. Lee, Andrew Delong, David Duvenaud, Brendan J. Frey

We propose generative neural network methods to generate DNA sequences and tune them to have desired properties.

Generative Adversarial Network

Submodularization for Binary Pairwise Energies

no code implementations • CVPR 2014 • Lena Gorelick, Yuri Boykov, Olga Veksler, Ismail Ben Ayed, Andrew Delong

We propose a general optimization framework based on local submodular approximations (LSA).

Submodularization for Quadratic Pseudo-Boolean Optimization

no code implementations • 8 Nov 2013 • Lena Gorelick, Yuri Boykov, Olga Veksler, Ismail Ben Ayed, Andrew Delong

We propose a general optimization framework based on local submodular approximations (LSA).

Minimizing Sparse High-Order Energies by Submodular Vertex-Cover

no code implementations • NeurIPS 2012 • Andrew Delong, Olga Veksler, Anton Osokin, Yuri Boykov

Inference on high-order graphical models has become increasingly important in recent years.

Clustering Vocal Bursts Intensity Prediction