23 citations · 56 across the 8 of their papers we have counts for
8 papers
Learning To Dive In Branch And Bound
Max B. Paulus, Andreas Krause
Primal heuristics are important for solving mixed integer linear programs, because they find feasible solutions that facilitate branch and bound search. A prominent group of primal…
Learning to Drop Out: An Adversarial Approach to Training Sequence VAEs
Đorđe Miladinović, Kumar Shridhar, Kushal Jain +4
In principle, applying variational autoencoders (VAEs) to sequential data offers a method for controlled sequence generation, manipulation, and structured representation learning.…
Learning To Cut By Looking Ahead: Cutting Plane Selection via Imitation Learning
Max B. Paulus, Giulia Zarpellon, Andreas Krause +2
Cutting planes are essential for solving mixed-integer linear problems (MILPs), because they facilitate bound improvements on the optimal solution value. For selecting cuts, modern…
Instance-wise algorithm configuration with graph neural networks
Romeo Valentin, Claudio Ferrari, Jérémy Scheurer +3
We present our submission for the configuration task of the Machine Learning for Combinatorial Optimization (ML4CO) NeurIPS 2021 competition. The configuration task is to predict a…
Augment with Care: Contrastive Learning for Combinatorial Problems
Haonan Duan, Pashootan Vaezipoor, Max B. Paulus +2
Supervised learning can improve the design of state-of-the-art solvers for combinatorial problems, but labelling large numbers of combinatorial instances is often impractical due t…
A Review of the Gumbel-max Trick and its Extensions for Discrete Stochasticity in Machine Learning
Iris A. M. Huijben, Wouter Kool, Max B. Paulus +1
The Gumbel-max trick is a method to draw a sample from a categorical distribution, given by its unnormalized (log-)probabilities. Over the past years, the machine learning communit…