156 citations · 445 across the 17 of their papers we have counts for
17 papers
Bounded Expectation of Label Assignment: Dataset Annotation by Supervised Splitting with Bias-Reduction Techniques
Alyssa Herbst, Bert Huang
Annotating large unlabeled datasets can be a major bottleneck for machine learning applications. We introduce a scheme for inferring labels of unlabeled data at a fraction of the c…
Labeled Graph Generative Adversarial Networks
Shuangfei Fan, Bert Huang
As a new approach to train generative models, \emph{generative adversarial networks} (GANs) have achieved considerable success in image generation. This framework has also recently…
Stochastic Generalized Adversarial Label Learning
Chidubem Arachie, Bert Huang
The usage of machine learning models has grown substantially and is spreading into several application domains. A common need in using machine learning models is collecting the dat…
Structured Output Learning with Conditional Generative Flows
You Lu, Bert Huang
Traditional structured prediction models try to learn the conditional likelihood, i.e., p(y|x), to capture the relationship between the structured output y and the input features x…
Block Belief Propagation for Parameter Learning in Markov Random Fields
You Lu, Zhiyuan Liu, Bert Huang
Traditional learning methods for training Markov random fields require doing inference over all variables to compute the likelihood gradient. The iteration complexity for those met…
Adversarial Label Learning
Chidubem Arachie, Bert Huang
We consider the task of training classifiers without labels. We propose a weakly supervised method---adversarial label learning---that trains classifiers to perform well against an…