56 citations · 134 across the 12 of their papers we have counts for
4 papers · 1 filter
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…
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…
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…
Learning Local Metrics and Influential Regions for Classification
Mingzhi Dong, Yujiang Wang, Xiaochen Yang +1
The performance of distance-based classifiers heavily depends on the underlying distance metric, so it is valuable to learn a suitable metric from the data. To address the problem…