6 citations · 6 across the 21 of their papers we have counts for
4 papers · 1 filter
Stability Enhanced Gaussian Process Variational Autoencoders
Carl R. Richardson, Jichen Zhang, Ethan King +1
A novel stability-enhanced Gaussian process variational autoencoder (SEGP-VAE) is proposed for indirectly training a low-dimensional linear time invariant (LTI) system, using high-…
ML-Guided Primal Heuristics for Mixed Binary Quadratic Programs
Weimin Huang, Natalie M. Isenberg, Ján Drgoňa +2
Mixed Binary Quadratic Programs (MBQPs) are an important and complex set of problems in combinatorial optimization. As solving large-scale combinatorial optimization problems is ch…
Learning to Optimize for Mixed-Integer Non-linear Programming with Feasibility Guarantees
Bo Tang, Elias B. Khalil, Ján Drgoňa
Mixed-integer nonlinear programs (MINLPs) arise in domains such as energy systems, process engineering, and transportation, and are notoriously difficult to solve at scale due to t…
Metric Learning to Accelerate Convergence of Operator Splitting Methods for Differentiable Parametric Programming
Ethan King, James Kotary, Ferdinando Fioretto +1
Recent work has shown a variety of ways in which machine learning can be used to accelerate the solution of constrained optimization problems. Increasing demand for real-time decis…