Considerable progress was recently made on semi-supervised learning, which differs from the traditional supervised learning by additionally exploring the information of the unlabeled examples. However, a disadvantage of many existing methods is that it does not generalize to unseen inputs. This paper suggests a space of basis functions to perform semi-supervised inductive learning. As a nice pr...

In sentiment classification, conventional supervised approaches heavily rely on a large amount of linguistic resources, which are costly to obtain for under-resourced languages. To overcome this scarce resource problem, there exist several methods that exploit graph-based semisupervised learning (SSL). However, fundamental issues such as controlling label propagation, choosing the initial seeds...

We present an empirical investigation of a recent class of Generative Adversarial Networks (GANs) using Integral Probability Metrics (IPM) and their performance for semi-supervised learning. IPM-based GANs like Wasserstein GAN, Fisher GAN and Sobolev GAN have desirable properties in terms of theoretical understanding, training stability, and a meaningful loss. In this work we investigate how th...

We present a probabilistic approach to learning a Gaussian Process classifier in the presence of unlabeled data. Our approach involves a “null category noise model” (NCNM) inspired by ordered categorical noise models. The noise model reflects an assumption that the data density is lower between the class-conditional densities. We illustrate our approach on a toy problem and present comparative ...

We present a framework to address the imbalanced data problem using semi-supervised learning. Specifically, from a supervised problem, we create a semi-supervised problem and then use a semi-supervised learning method to identify the most relevant instances to establish a welldefined training set. We present extensive experimental results, which demonstrate that the proposed framework significa...

In this paper, we propose a novel framework, called Semi-supervised Embedding in Attributed Networks with Outliers (SEANO), to learn a low-dimensional vector representation that systematically captures the topological proximity, attribute affinity and label similarity of vertices in a partially labeled attributed network (PLAN). Our method is designed to work in both transductive and inductive ...

Semi-supervised learning and ensemble learning are two important machine learning paradigms. The former attempts to achieve strong generalization by exploiting unlabeled data; the latter attempts to achieve strong generalization by using multiple learners. Although both paradigms have achieved great success during the past decade, they were almost developed separately. In this paper, we advocat...

Extreme cold events in natural gas demand are characterized by unusual dynamics that makes modeling the characteristics of the gas demand during extreme cold events a challenging task. This unusual dynamics is in the form of hysteresis, possibly due to human behavioral response to extreme weather conditions. To natural gas distribution utilities, extreme cold events represent high risk events g...

In opinion mining, there has been only very little work investigating semi-supervised machine learning on document-level polarity classification. We show that semi-supervised learning performs significantly better than supervised learning when only few labeled data are available. Semi-supervised polarity classifiers rely on a predictive feature set. (Semi-)Manually built polarity lexicons are o...

This paper explores the use of self-ensembling for visual domain adaptation problems. Our technique is derived from the mean teacher variant [20] of temporal ensembling [8], a technique that achieved state of the art results in the area of semi-supervised learning. We introduce a number of modifications to their approach for challenging domain adaptation scenarios and evaluate its effectiveness...