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Methodology & Statistics Data Science Lab


08/03/2018 – A Probabilistic Active Learning Approach for Learning from Data with Limited Supervision

You can find the slides of the presentation Georg gave here (.pdf, 3MB).


Georg explaining the results in his graphs

Original announcement:

Thursday 08/03/2018 at 15:00 in room B1.09

The speaker for this meeting will be Georg Krempl, who will talk about an approach for learning from data with limited supervision. Here is a shortened abstract:

Machine learning has become widely used throughout commerce, science, and technology. However, the ever increasing volumes of data are contrasted by various constraints, such as limited supervision, processing or storage capacities. This requires techniques to optimise the allocation of these capacities.

Active machine learning aims to provide techniques for selecting the most insightful information (like label annotations of data instances) to be queried from oracles (like human supervisors).

In this talk, I will present our recently developed probabilistic active learning approach PAL. This decision-theoretic approach combines the fast asymptotic runtime of popular heuristics like uncertainty sampling with a direct optimisation of the expected gain in classification performance.

I will conclude this presentation by demonstrating the use of PAL in different active learning scenarios, ranging from label selection in large data pools and evolving data streams to broader settings such as active class selection.


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