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Graph Sparsification via Meta-Learning
We present a novel graph sparsification approach for semisupervised learning on undirected attributed graphs. The main challenge is to retain few edges while minimize the loss of node classificationExpand
A Unified Framework for Knowledge Intensive Gradient Boosting: Leveraging Human Experts for Noisy Sparse Domains
A unified framework for both classification and regression settings that can both effectively and efficiently incorporate qualitative constraints to accelerate learning to a better model is developed. Expand
Morpheme Extraction Task at FIRE 2012--2013
The Morpheme Extraction task (MET) was organized for the first time in FIRE 2012 and subsequently in FIRE 2013 and the goals, data, tasks, participants, evaluation and results obtained are described. Expand
RePReL: Integrating Relational Planning and Reinforcement Learning for Effective Abstraction
This work proposes RePReL, a hierarchical framework that leverages a relational planner to provide useful state abstractions and enables the application of standard RL approaches for learning in structured domains. Expand
A Probabilistic Approach to Extract Qualitative Knowledge for Early Prediction of Gestational Diabetes
This work applies the Qualitative Knowledge Extraction method toward early prediction of gestational diabetes on clinical study data and empirical results demonstrate that the extracted rules are both interpretable and valid. Expand