Active learning via transductive experimental design
By Kai Yu, Jinbo Bi, Volker Tresp. International Conference on Machine Learning, 2006
388 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/b3f4be661200220e8b9861342ed679106a57fd2e
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
In Statistics and Probability
- Xiaojun Chang
- Feiping Nie
- Frédéric Precioso
- Silvio Savarese
- Jundong Li
- Xia Hu
- Stefano Ermon
- Mélanie Ducoffe
Citations in the corpus
- Adversarial Active Learning for Deep Networks: a Margin Based Approach (2018, methodology): “First of all, scaling them to high dimensional parameters networks may turn out to be intractable: some classic active learning methods such as Optimal Experiment Design (Yu et al., 2006) require to inverse the Hessian matrix of the models at each iteration, which would be intractable for current st”
- Multi-Class Active Learning by Uncertainty Sampling with Diversity Maximization (2015, background): “As a different but complementary way to reduce the labelling cost in supervised learning, active learning has received much research attention.”
- Semi-supervised deep kernel learning (2016, background): “Our semi-supervised learning objective draws inspiration from transductive experimental design, which chooses informative experiments by seeking data points that are both hard to predict and informative for the unexplored test data [13].”
- Radar: Residual Analysis for Anomaly Detection in Attributed Networks (2017, methodology): “One natural way to build X̃ is by using some representative instances [Yu et al., 2006].”
- Active Learning for Convolutional Neural Networks: A Core-Set Approach (2017, background): “(2006); Guo & Schuurmans (2008), and linear regression with Gaussian noise (Yu et al., 2006).”
- Near-Optimal Sensor Placements in Gaussian Processes: Theory, Efficient Algorithms and Empirical Studies (2008, neutral): “Yu et al. (2006) extend classical experimental design to the transductive setting, which takes the distribution of test data into account.”