Theoretical analyses for graph learning methods often assume a complete
...
We study the problem of modeling a binary operation that satisfies some
...
We consider the min-max r-gathering problem described as follows: We are...
We study Graph Convolutional Networks (GCN) from the graph signal proces...
In this paper, we study the graph classification problem from the graph
...
Data-driven decision-making is performed by solving a parameterized
opti...
Neural networks using numerous text data have been successfully applied ...
We present a simple proof for the universality of invariant and equivari...
Selecting appropriate regularization coefficients is critical to perform...
We consider a problem of maximizing a monotone DR-submodular function un...
We consider min-max r-gather clustering problem and min-max r-gathering
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r-gathering problem is a variant of facility location problems. In this
...
We study a stochastic variant of monotone submodular maximization proble...
Data cleansing is a typical approach used to improve the accuracy of mac...
Graph neural networks have become one of the most important techniques t...
The Floyd--Warshall algorithm is a well-known algorithm for the all-pair...
This paper addresses the problem of finding a representation of a subtre...
Fairness by decision-makers is believed to be auditable by third parties...
In an ordinary feature selection procedure, a set of important features ...
Statistical hypothesis testing serves as statistical evidence for scient...
We introduce Stochastic Probing with Prices (SPP), a variant of the
Stoc...
The automaton constrained tree knapsack problem is a variant of the knap...
We consider a monotone submodular maximization problem whose constraint ...
While several feature scoring methods are proposed to explain the output...
The subspace selection problem seeks a subspace that maximizes an object...
The fundamental problem in short-text classification is feature
sparsene...
We present a novel convolutional neural network architecture for photome...
Tensor train (TT) decomposition provides a space-efficient representatio...
We propose a method for finding alternate features missing in the Lasso
...
Methods for learning word representations using large text corpora have
...
Learning representations for semantic relations is important for various...
Attributes of words and relations between two words are central to numer...