ICML 2011
TechTalks from event: ICML 2011
statistical relational learning
-
Relational Active Learning for Joint Collective Classification ModelsIn 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 DataRelational 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.
- All Sessions
- Keynotes
- Bandits and Online Learning
- Structured Output
- Reinforcement Learning
- Graphical Models and Optimization
- Recommendation and Matrix Factorization
- Neural Networks and Statistical Methods
- Invited Cross-Conference Track
- Feature Selection, Dimensionality Reduction
- Learning Theory
- Large-Scale Learning
- Latent-Variable Models
- Active and Online Learning
- Latent-Variable Models
- Neural Networks and Deep Learning
- Tutorial: Learning Kernels
- Tutorial: Machine Learning for Large Scale Recommender Systems
- Ensemble Methods
- Tutorial: Introduction to Bandits: Algorithms and Theory
- Tutorial: Machine Learning and Robotics
- Tutorial: Machine Learning in Ecological Science and Environmental Policy
- Tutorial : Collective Intelligence and Machine Learning
- Test-of-Time
- Best Paper
- Robotics and Reinforcement Learning
- Transfer Learning
- Kernel Methods
- Optimization
- Learning Theory
- Invited Cross-Conference Session
- Neural Networks and Deep Learning
- Reinforcement Learning
- Bayesian Inference and Probabilistic Models
- Supervised Learning
- Social Networks
- Evaluation Metrics
- statistical relational learning
- Outlier Detection
- Time Series
- Graphical Models and Bayesian Inference
- Sparsity and Compressed Sensing
- Clustering
- Game Theory and Planning and Control
- Semi-Supervised Learning
- Kernel Methods and Optimization
- Neural Networks and NLP
- Probabilistic Models & MCMC
- Online Learning
- Ranking and Information Retrieval