The problem of choosing colors for data visualization is expressed by this quote from information visualization guru Edward Tufte: " … avoiding catastrophe becomes the first principle in bringing color to information: Above all, do no harm. " Color used well can enhance and clarify a presentation. Color used poorly will obscure, muddle and confuse. While there is a strong aesthetic component to...

It is challenging to handle a large volume of labels in multi-label learning. However, existing approaches explicitly or implicitly assume that all the labels in the learning process are given, which could be easily violated in changing environments. In this paper, we define and study streaming label learning (SLL), i.e.labels are arrived on the fly, to model newly arrived labels with the help ...

We address a largely open problem of multilabel classification over graphs. Unlike traditional vector input, graph has rich variable-size structures, that suggests complex relationships between labels and subgraphs. Uncovering these relations might hold the keys of classification performance and explainability. To this end, we design GAML (Graph Attentional Multi-Label learning), a graph neural...

We consider a strategic online social network that controls information flows between agents in a social learning setting. Agents on the network select among products of competing firms of unknown quality. The network sells advertising to firms. We consider display advertising, which is standard firm-to-consumer advertising, and social advertising, in which agents who purchased that firm’s prod...

Multi-label classification is an important task in many modern machine learning applications. Accurate methods model the correlations and relationships between labels, either by assuming a low-dimensional embedding of the labels or a graph structure of label dependencies. While such interactions can be achieved using feed-forward predictors, problems with tight coupling between labels are bette...

In multi-label classification the goal is to assign an instance to a set of different classes. This task is normally addressed either by defining a compound class variable with all the possible combinations of labels (label power-set methods) or by building independent classifiers for each class (binary relevance methods). The first approach suffers from high computationally complexity, while t...

The problem of choosing colors for data visualization is expressed by this quote from information visualization guru Edward Tufte: " …avoiding catastrophe becomes the first principle in bringing color to information: Above all, do no harm. " Color used well can enhance and clarify a presentation. Color used poorly will obscure, muddle and confuse. While there is a strong aesthetic component to ...

We present an adaptation of the recently proposed graph-shifts algorithm for labeling MRF problems from low-level vision. Graph-shifts is an energy minimization algorithm that does labeling by dynamically manipulating, or shifting, the parent-child relationships in a hierarchical decomposition of the image. Graph-shifts was originally proposed for labeling using relatively small label sets (e.g...

Labeled graphs provide a natural way of representing entities, relationships and structures within real datasets such as knowledge graphs and protein interactions. Applications such as question answering, semantic search, and motif discovery entail efficient approaches for subgraph matching involving both label and structural similarities. Given the NP-completeness of subgraph isomorphism and t...

Background: Quality assurance in the hematology laboratory is a must to ensure laboratory users of reliable test results with high degree of precision and accuracy. Even after so many advances in hematology laboratory practice, pre-analytical errors remain a challenge for practicing pathologists. This study was undertaken with an objective to ...