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cs.LG2018
Dynamic Ensemble Active Learning: A Non-Stationary Bandit with Expert Advice
Kunkun Pang, Mingzhi Dong, Yang Wu +1
Active learning aims to reduce annotation cost by predicting which samples are useful for a human teacher to label. However it has become clear there is no best active learning alg…
cs.LG2018
Meta-Learning Transferable Active Learning Policies by Deep Reinforcement Learning
Kunkun Pang, Mingzhi Dong, Yang Wu +1
Active learning (AL) aims to enable training high performance classifiers with low annotation cost by predicting which subset of unlabelled instances would be most beneficial to la…
cs.LG2018
Metric Learning via Maximizing the Lipschitz Margin Ratio
Mingzhi Dong, Xiaochen Yang, Yang Wu +1
In this paper, we propose the Lipschitz margin ratio and a new metric learning framework for classification through maximizing the ratio. This framework enables the integration of…