Probabilistic Models for Learning from Crowdsourced Data



This thesis leverages the general framework of probabilistic graphical models to develop probabilistic approaches for learning from crowdsourced data. This type of data is rapidly changing the way we approach many machine learning problems in different areas such as natural language processing, computer vision and music. By exploiting the wisdom of crowds, machine learning researchers and practitioners are able to develop approaches to perform complex tasks in a much more scalable manner. For instance, crowdsourcing platforms like Amazon mechanical turk provide users with an inexpensive and accessible resource for labeling large datasets efficiently. However, the different biases and levels of expertise that are commonly found among different annotators in these platforms deem the development of targeted approaches necessary.

With the issue of annotator heterogeneity in mind, we start by introducing a class of latent expertise models which are able to discern reliable annotators from random ones without access to the ground truth, while jointly learning a logistic regression classifier or a conditional random field. Then, a generalization of Gaussian process classifiers to multiple-annotator settings is developed, which makes it possible to learn non-linear decision boundaries between classes and to develop an active learning methodology that is able to increase the efficiency of crowdsourcing while reducing its cost. Lastly, since the majority of the tasks for which crowdsourced data is commonly used involves complex high-dimensional data such as images or text, two supervised topic models are also proposed, one for classification and another for regression problems. Using real crowdsourced data from Mechanical Turk, we empirically demonstrate the superiority of the aforementioned models over state-of-the-art approaches in many different tasks such as classifying posts, news stories, images and music, or even predicting the sentiment of a text, the number of stars of a review or the rating of movie.

But the concept of crowdsourcing is not limited to dedicated platforms such as Mechanical Turk. For example, if we consider the social aspects of the modern Web, we begin to perceive the true ubiquitous nature of crowdsourcing. This opened up an exciting new world of possibilities in artificial intelligence. For instance, from the perspective of intelligent transportation systems, the information shared online by crowds provides the context that allows us to better understand how people move in urban environments. In the second part of this thesis, we explore the use of data generated by crowds as additional inputs in order to improve machine learning models. Namely, the problem of understanding public transport demand in the presence of special events such as concerts, sports games or festivals, is considered. First, a probabilistic model is developed for explaining non-habitual overcrowding using crowd-generated information mined from the Web. Then, a Bayesian additive model with Gaussian process components is proposed. Using real data from Singapore’s transport system and crowd-generated data regarding special events, this model is empirically shown to be able to outperform state-of-the-art approaches for predicting public transport demand. Furthermore, due to its additive formulation, the proposed model is able to breakdown an observed time-series of transport demand into a routine component corresponding to commuting and the contributions of individual special events.

Overall, the models proposed in this thesis for learning from crowdsourced data are of wide applicability and can be of great value to a broad range of research communities.

Related Project

InfoCrowds - Social Web Information Retrieval for crowds mobility management

PhD Thesis

Probabilistic Models for Learning from Crowdsourced Data, September 2016

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