Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.740819
Title: Task-oriented learning of structured probability distributions
Author: Bouchacourt, Diane
ISNI:       0000 0004 7229 1273
Awarding Body: University of Oxford
Current Institution: University of Oxford
Date of Award: 2017
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Abstract:
Machine learning models automatically learn from historical data to predict unseen events. Such events are often represented as complex multi-dimensional structures. In many cases there is high uncertainty in the prediction process. Research has developed probabilistic models to capture distributions of complex objects, but their learning objective is often agnostic of the evaluation loss. In this thesis, we address the aforementioned defficiency by designing probabilistic methods for structured object prediction that take into account the task at hand. First, we consider that the task at hand is explicitly known, but there is ambiguity in the prediction due to an unobserved (latent) variable. We develop a framework for latent structured output prediction that unifies existing empirical risk minimisation methods. We empirically demonstrate that for large and ambiguous latent spaces, performing prediction by minimising the uncertainty in the latent variable provides more accurate results. Empirical risk minimisation methods predict only a pointwise estimate of the output, however there can be uncertainty on the output value itself. To tackle this deficiency, we introduce a novel type of model to perform probabilistic structured output prediction. Our training objective minimises a dissimilarity coefficient between the data distribution and the model's distribution. This coefficient is defined according to a loss of choice, thereby our objective can be tailored to the task loss. We empirically demonstrate the ability of our model to capture distributions over complex objects. Finally, we tackle a setting where the task loss is implicitly expressed. Specifically, we consider the case of grouped observations. We propose a new model for learning a representation of the data that decomposes according to the semantics behind this grouping, while allowing efficient test-time inference. We experimentally demonstrate that our model learns a disentangled and controllable representation, leverages grouping information when available, and generalises to unseen observations.
Supervisor: Nowozin, Sebastian ; Kumar, M. Pawan Sponsor: Microsoft Research Cambridge
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.740819  DOI: Not available
Keywords: Machine Learning
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