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29 papers · 2 filters
Bayesian Optimization of Function Networks
Raul Astudillo, Peter I. Frazier
We consider Bayesian optimization of the output of a network of functions, where each function takes as input the output of its parent nodes, and where the network takes significan…
BGL: GPU-Efficient GNN Training by Optimizing Graph Data I/O and Preprocessing
Tianfeng Liu, Yangrui Chen, Dan Li +7
Graph neural networks (GNNs) have extended the success of deep neural networks (DNNs) to non-Euclidean graph data, achieving ground-breaking performance on various tasks such as no…
Tree in Tree: from Decision Trees to Decision Graphs
Bingzhao Zhu, Mahsa Shoaran
Decision trees have been widely used as classifiers in many machine learning applications thanks to their lightweight and interpretable decision process. This paper introduces Tree…
Approximate Decomposable Submodular Function Minimization for Cardinality-Based Components
Nate Veldt, Austin R. Benson, Jon Kleinberg
Minimizing a sum of simple submodular functions of limited support is a special case of general submodular function minimization that has seen numerous applications in machine lear…
Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods
Derek Lim, Felix Hohne, Xiuyu Li +4
Many widely used datasets for graph machine learning tasks have generally been homophilous, where nodes with similar labels connect to each other. Recently, new Graph Neural Networ…
Residual Overfit Method of Exploration
James McInerney, Nathan Kallus
Exploration is a crucial aspect of bandit and reinforcement learning algorithms. The uncertainty quantification necessary for exploration often comes from either closed-form expres…