Add to ORKGAbstract We present a noise resilient probabilistic model for active learning of a Gaussian process classifier from crowds, i.e., a set of noisy labelers. It explicitly models both the overall label noise and the expertise level of each indi-vidual labeler with two levels of flip models. Expectation propagation is adopted for efficient approximate Bayesian inference of our probabilistic model for classification, based on which, a generalized EM algorithm is derived to esti-mate both the global label noise and the expertise of each individual labeler. The probabilistic nature of our model immediately allows the adoption of the prediction entropy for active selection of data samples to be labeled, and active selection of high quality labelers...
Although supervised learning requires a labeled dataset, ob- taining labels from experts is generall...
Machine learning (ML) methods often require large volumes of labeled data to achieve meaningful perf...
This thesis describes contributions to the field of interpretable models in probabilistic machine le...
We present a noise resilient probabilistic model for ac-tive learning of a Gaussian process classifi...
Discriminative methods for visual object category recognition are typically non-probabilistic, predi...
Active learning is an effective way to relieve the tedious work of manual annotation in many applica...
With the proliferation of social media, gathering data has became cheaper and easier than before. Ho...
This paper studies the active learning problem in crowdsourcing settings, where multiple imperfect a...
Abstract. Gaussian processes offer the advantage of calculating the classification uncertainty in te...
International audienceIn the context of Active Learning for classification, the classification error...
... recognition are typically non-probabilistic, predicting class labels but not directly providing ...
Although supervised learning requires a labeled dataset, obtaining labels from experts is generally ...
Biased labelers are a systemic problem in crowdsourcing, and a comprehensive toolbox for handling th...
This paper addresses the problem of active learning of a multi-output Gaussian process (MOGP) model ...
Labeled data can be expensive to acquire in several application domains, including medical imaging, ...
Although supervised learning requires a labeled dataset, ob- taining labels from experts is generall...
Machine learning (ML) methods often require large volumes of labeled data to achieve meaningful perf...
This thesis describes contributions to the field of interpretable models in probabilistic machine le...
We present a noise resilient probabilistic model for ac-tive learning of a Gaussian process classifi...
Discriminative methods for visual object category recognition are typically non-probabilistic, predi...
Active learning is an effective way to relieve the tedious work of manual annotation in many applica...
With the proliferation of social media, gathering data has became cheaper and easier than before. Ho...
This paper studies the active learning problem in crowdsourcing settings, where multiple imperfect a...
Abstract. Gaussian processes offer the advantage of calculating the classification uncertainty in te...
International audienceIn the context of Active Learning for classification, the classification error...
... recognition are typically non-probabilistic, predicting class labels but not directly providing ...
Although supervised learning requires a labeled dataset, obtaining labels from experts is generally ...
Biased labelers are a systemic problem in crowdsourcing, and a comprehensive toolbox for handling th...
This paper addresses the problem of active learning of a multi-output Gaussian process (MOGP) model ...
Labeled data can be expensive to acquire in several application domains, including medical imaging, ...
Although supervised learning requires a labeled dataset, ob- taining labels from experts is generall...
Machine learning (ML) methods often require large volumes of labeled data to achieve meaningful perf...
This thesis describes contributions to the field of interpretable models in probabilistic machine le...