125,741 research outputs found
Learning to select data for transfer learning with Bayesian Optimization
Domain similarity measures can be used to gauge adaptability and select
suitable data for transfer learning, but existing approaches define ad hoc
measures that are deemed suitable for respective tasks. Inspired by work on
curriculum learning, we propose to \emph{learn} data selection measures using
Bayesian Optimization and evaluate them across models, domains and tasks. Our
learned measures outperform existing domain similarity measures significantly
on three tasks: sentiment analysis, part-of-speech tagging, and parsing. We
show the importance of complementing similarity with diversity, and that
learned measures are -- to some degree -- transferable across models, domains,
and even tasks.Comment: EMNLP 2017. Code available at:
https://github.com/sebastianruder/learn-to-select-dat
Bounded-Distortion Metric Learning
Metric learning aims to embed one metric space into another to benefit tasks
like classification and clustering. Although a greatly distorted metric space
has a high degree of freedom to fit training data, it is prone to overfitting
and numerical inaccuracy. This paper presents {\it bounded-distortion metric
learning} (BDML), a new metric learning framework which amounts to finding an
optimal Mahalanobis metric space with a bounded-distortion constraint. An
efficient solver based on the multiplicative weights update method is proposed.
Moreover, we generalize BDML to pseudo-metric learning and devise the
semidefinite relaxation and a randomized algorithm to approximately solve it.
We further provide theoretical analysis to show that distortion is a key
ingredient for stability and generalization ability of our BDML algorithm.
Extensive experiments on several benchmark datasets yield promising results
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