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Found 4 papers, 3 papers with code
Instance-wise algorithm configuration with graph neural networks
We pose this task as a supervised learning problem: First, we compile a large dataset of the solver performance for various configurations and all provided MILP instances.
Learning Set Functions that are Sparse in Non-Orthogonal Fourier Bases
Many applications of machine learning on discrete domains, such as learning preference functions in recommender systems or auctions, can be reduced to estimating a set function that is sparse in the Fourier domain.
Efficiently Learning Fourier Sparse Set Functions
In this paper we consider the problem of efficiently learning set functions that are defined over a ground set of size $n$ and that are sparse (say $k$-sparse) in the Fourier domain.
A Scalable Walsh-Hadamard Regularizer to Overcome the Low-degree Spectral Bias of Neural Networks
We show how this spectral bias towards low-degree frequencies can in fact hurt the neural network's generalization on real-world datasets.
