7 papers
Distribution Matching for Machine Teaching
Xiaofeng Cao, Ivor W. Tsang
Machine teaching is an inverse problem of machine learning that aims at steering the student learner towards its target hypothesis, in which the teacher has already known the stude…
Bayesian Active Learning by Disagreements: A Geometric Perspective
Xiaofeng Cao, Ivor W. Tsang
We present geometric Bayesian active learning by disagreements (GBALD), a framework that performs BALD on its core-set construction interacting with model uncertainty estimation. T…
Learning Image-Specific Attributes by Hyperbolic Neighborhood Graph Propagation
Xiaofeng Xu, Ivor W. Tsang, Xiaofeng Cao +2
As a kind of semantic representation of visual object descriptions, attributes are widely used in various computer vision tasks. In most of existing attribute-based research, class…
Edge Federation: Towards an Integrated Service Provisioning Model
Xiaofeng Cao, Guoming Tang, Deke Guo +2
Edge computing is a promising computing paradigm for pushing the cloud service to the network edge. To this end, edge infrastructure providers (EIPs) need to bring computation and…
Target-Independent Active Learning via Distribution-Splitting
Xiaofeng Cao, Ivor W. Tsang, Xiaofeng Xu +1
To reduce the label complexity in Agnostic Active Learning (A^2 algorithm), volume-splitting splits the hypothesis edges to reduce the Vapnik-Chervonenkis (VC) dimension in version…
A Structured Perspective of Volumes on Active Learning
Xiaofeng Cao, Ivor W. Tsang, Guandong Xu
Active Learning (AL) is a learning task that requires learners interactively query the labels of the sampled unlabeled instances to minimize the training outputs with human supervi…