56 citations · 112 across the 6 of their papers we have counts for
Showing 2018Show all
3 papers · 1 filter
cs.LG2018★ 1 cited
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★ 56 cited
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★ 3 cited
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…