In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.

Ex Input:
In colored graphs, node classes are often associated with either their neighbors class or with information not incorporated in the graph associated with each node. We here propose that node classes are also associated with topological features of the nodes. We use this association to improve Graph machine learning in general and specifically, Graph Convolutional Networks (GCN). 

 First, we show that even in the absence of any external information on nodes, a good accuracy can be obtained on the prediction of the node class using either topological features, or using the neighbors class as an input to a GCN. This accuracy is slightly less than the one that can be obtained using content based GCN.

 Secondly, we show that explicitly adding the topology as an input to the GCN does not improve the accuracy when combined with external information on nodes. However,  adding an additional adjacency matrix with edges between distant nodes with similar topology to the GCN does significantly improve its accuracy, leading to results better than all state of the art methods in multiple datasets.

Ex Output:
Topology-Based Graph Convolutional Network (GCN)


Ex Input:
Bayesian optimization (BO) is a popular methodology to tune the hyperparameters of expensive black-box functions. Despite its success, standard BO focuses on a single task at a time and is not designed to leverage information from related functions, such as tuning performance metrics of the same algorithm across multiple datasets. In this work, we introduce a novel approach to achieve transfer learning across different datasets as well as different metrics. The main idea is to regress the mapping from hyperparameter to metric quantiles with a semi-parametric Gaussian Copula distribution, which provides robustness against different scales or outliers that can occur in different tasks. We introduce two methods to leverage this estimation: a Thompson sampling strategy as well as a Gaussian Copula process using such quantile estimate as a prior. We show that these strategies can combine the estimation of multiple metrics such as runtime and accuracy, steering the optimization toward cheaper hyperparameters for the same level of accuracy. Experiments on an extensive set of hyperparameter tuning tasks demonstrate significant improvements over state-of-the-art methods.

Ex Output:
We show how using semi-parametric prior estimations can speed up HPO significantly across datasets and metrics.


Ex Input:
Significant work has been dedicated to developing methods for communicating reasons for decision-making within au-
 tomated scheduling systems to human users. However, much less focus has been placed on communicating reasons for why
 scheduling systems are unable to arrive at a feasible solution when over-constrained. We investigate this problem in the
 context of task scheduling. We introduce the agent resource-constrained project scheduling problem (ARCPSP), an ex-
 tension of the resource-constrained project scheduling problem which includes a conception of agents that execute tasks
 in parallel. We outline a generic framework, based on efficiently enumerating minimal unsatisfiable sets (MUS) and
 maximal satisfiable sets (MSS), to produce small descriptions of the source of infeasibility. These descriptions are supple-
 mented with potential relaxations that would fix the infeasibility found within the problem instance. We illustrate how
 this method may be applied to the ARCPSP and demonstrate how to generate different types of explanations for an over-
 constrained instance of the ARCPSP.

Ex Output:

We develop a framework for generating human-understandable explanations for why infeasibility is occurring in over-constrained instances of a class of resource-constrained scheduling problems.