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01/24/2023
Learning To Dive In Branch And Bound
Primal heuristics are important for solving mixed integer linear program...
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09/26/2022
Learning to Drop Out: An Adversarial Approach to Training Sequence VAEs
In principle, applying variational autoencoders (VAEs) to sequential dat...
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06/27/2022
Learning To Cut By Looking Ahead: Cutting Plane Selection via Imitation Learning
Cutting planes are essential for solving mixed-integer linear problems (...
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02/17/2022
Augment with Care: Contrastive Learning for the Boolean Satisfiability Problem
Supervised learning can improve the design of state-of-the-art solvers f...
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02/10/2022
Instance-wise algorithm configuration with graph neural networks
We present our submission for the configuration task of the Machine Lear...
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10/04/2021
A Review of the Gumbel-max Trick and its Extensions for Discrete Stochasticity in Machine Learning
The Gumbel-max trick is a method to draw a sample from a categorical dis...
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10/09/2020
Rao-Blackwellizing the Straight-Through Gumbel-Softmax Gradient Estimator
Gradient estimation in models with discrete latent variables is a challe...
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06/15/2020
