TechTalks from event: ICML 2011

statistical relational learning

  • Relational Active Learning for Joint Collective Classification Models Authors: Ankit Kuwadekar; Jennifer Neville
    In many network domains, labeled data may be costly to acquire---indicating a need for {em relational active learning} methods. Recent work has demonstrated that relational model performance can be improved by taking network structure into account when choosing instances to label. However, in collective inference settings, {em both} model estimation {em and} prediction can be improved by acquiring a node's label---since relational models estimate a joint distribution over labels in the network and collective classification methods propagate information from labeled training data during prediction. This conflates improvement in learning with improvement in inference, since labeling nodes can reduce inference error without improving the overall quality of the learned model. Here, we use {em across-network} classification to separate the effects on learning and prediction, and focus on reduction of learning error. When label propagation is used for learning, we find that labeling based on prediction {em certainty} is more effective than labeling based on {em uncertainty}. As such, we propose a novel active learning method that combines a network-based {em certainty} metric with semi-supervised learning and relational resampling. We evaluate our approach on synthetic and real-world networks and show faster learning compared to several baselines, including the network based method of Bilgic et al. 2010.
  • A Three-Way Model for Collective Learning on Multi-Relational Data Authors: Maximilian Nickel; Volker Tresp; Hans-Peter Kriegel
    Relational learning is becoming increasingly important in many areas of application. Here, we present a novel approach to relational learning based on the factorization of a three-way tensor. We show that unlike other tensor approaches, our method is able to perform collective learning via the latent components of the model and provide an efficient algorithm to compute the factorization. We substantiate our theoretical considerations regarding the collective learning capabilities of our model by the means of experiments on both a new dataset and a dataset commonly used in entity resolution. Furthermore, we show on common benchmark datasets that our approach achieves better or on-par results, if compared to current state-of-the-art relational learning solutions, while it is significantly faster to compute.