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Machine learning under budget constraints

Gabriella Contardo 1
1 MLIA - Machine Learning and Information Access
LIP6 - Laboratoire d'Informatique de Paris 6
Abstract : This thesis studies the problem of machine learning under budget constraints, in particular we propose to focus on the cost of the information used by the system to predict accurately. Most methods in machine learning usually defines the quality as the performance (e.g accuracy) on the task at hand, but ignores the cost of the model itself: for instance, the number of examples and/or labels needed during learning, the memory used, or the number of features required to predict at test-time. We propose more specifically in this manuscript several methods for cost-sensitive prediction w.r.t. the quantity of features used. We present three models that learn to predict under such constraint, i.e that learn a strategy to gather only the necessary information in order to predict well but with a small cost. The first model is a static approach applied on cold-start recommendation. We then define two adaptive methods that allow for a better trade-off between cost and accuracy, in a more generic setting. We rely on representation learning techniques, along with recurrent neural networks architecture and gradient descent algorithms for learning. In the last part of the thesis, we propose to study the problem of active-learning, where one aims at constraining the amount of labels used to train a model. We present our work for a novel approach of the problem using meta-learning, with an instantiation using bi-directional recurrent neural networks.
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Submitted on : Monday, January 8, 2018 - 10:51:08 AM
Last modification on : Friday, January 8, 2021 - 5:34:10 PM
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  • HAL Id : tel-01677223, version 1


Gabriella Contardo. Machine learning under budget constraints. Machine Learning [cs.LG]. Université Pierre et Marie Curie - Paris VI, 2017. English. ⟨NNT : 2017PA066203⟩. ⟨tel-01677223⟩



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