Providing Post-Hoc Explanation for Node Representation Learning Models Through Inductive Conformal Predictions

Multiple Kernel Learning for Efficient Conformal Predictions

The Conformal Predictions framework is a recent development in machine learning to associate reliable measures of confidence with results in classification and regression. This framework is founded on the principles of algorithmic randomness (Kolmogorov complexity), transductive inference and hypothesis testing. While the formulation of the framework guarantees validity, the efficiency of the f...

Revising Process Models through Inductive Learning

Discovering the Business Process (BP) model underpinning existing practices through analysis of event logs, allows users to understand, analyse and modify the process. But, to be useful, the BP model must be kept in line with practice throughout its lifetime, as changes occur to the business objectives, technologies and quality programs. Current techniques require users to manually revise the B...

Ing Inductive Neural Network Learning and Explanation-based Learning

Integrating Inductive Neural Network Learning and Explanation-Based Learning

Many researchers have noted the importance of combining inductive and analytical learning, yet we still lack combined learning methods that are effective in practice. We present here a learning method that combines explanation-based learning from a previously learned approximate domain theory, together with inductive learning from observations. This method, called explanation-based neural netwo...

Inductive Representation Learning on Large Graphs

Low-dimensional embeddings of nodes in large graphs have proved extremely useful in a variety of prediction tasks, from content recommendation to identifying protein functions. However, most existing approaches require that all nodes in the graph are present during training of the embeddings; these previous approaches are inherently transductive and do not naturally generalize to unseen nodes. ...