Structural kernels are a flexible learning paradigm that has been widely used in Natural Language Processing. However, the problem of model selection in kernel-based methods is usually overlooked. Previous approaches mostly rely on setting default values for kernel hyperparameters or using grid search, which is slow and coarse-grained. In contrast, Bayesian methods allow efficient model selection by maximizing the evidence on the training data through gradient-based methods. In this paper we show how to perform this in the context of structural kernels by using Gaussian Processes. Experimental results on tree kernels show that this procedure results in better prediction performance compared to hyperparameter optimization via grid search. The framework proposed in this paper can be adapted to other structures besides trees, e.g., strings and graphs, thereby extending the utility of kernel-based methods.
We propose a model for jointly predicting multiple emotions in natural language sentences. Our model is based on a low-rank coregionalisation approach, which combines a vector-valued Gaussian Process with a rich parameterisation scheme. We show that our approach is able to learn correlations and anti-correlations between emotions on a news headlines dataset. The proposed model outperforms both singletask baselines and other multi-task approaches.  Blimps are a promising platform for aerial robotics and have been studied extensively for this purpose. Unlike other aerial vehicles, blimps are relatively safe and also possess the ability to loiter for long periods. These advantages, however, have been difficult to exploit because blimp dynamics are complex and inherently non-linear. The classical approach to system modeling represents the system as an ordinary differential equation (ODE) based on Newtonian principles. A more recent modeling approach is based on representing state transitions as a Gaussian process (GP). In this paper, we present a general technique for system identification that combines these two modeling approaches into a single formulation. This is done by training a Gaussian process on the residual between the non-linear model and ground truth training data. The result is a GP-enhanced model that provides an estimate of uncertainty in addition to giving better state predictions than either ODE or GP alone. We show how the GP-enhanced model can be used in conjunction with reinforcement learning to generate a blimp controller that is superior to those learned with ODE or GP models alone. Temporal variations of text are usually ignored in NLP applications. However, text use changes with time, which can affect many applications. In this paper we model periodic distributions of words over time. Focusing on hashtag frequency in Twitter, we first automatically identify the periodic patterns. We use this for regression in order to forecast the volume of a hashtag based on past data. We use Gaussian Processes, a state-ofthe-art bayesian non-parametric model, with a novel periodic kernel. We demonstrate this in a text classification setting, assigning the tweet hashtag based on the rest of its text. This method shows significant improvements over competitive baselines. Annotating linguistic data is often a complex, time consuming and expensive endeavour. Even with strict annotation guidelines, human subjects often deviate in their analyses, each bringing different biases, interpretations of the task and levels of consistency. We present novel techniques for learning from the outputs of multiple annotators while accounting for annotator specific behaviour. These techniques use multi-task Gaussian Processes to learn jointly a series of annotator and metadata specific models, while explicitly representing correlations between models which can be learned directly from data. Our experiments on two machine translation quality estimation datasets show uniform significant accuracy gains from multi-task learning, and consistently outperform strong baselines. In this article, we present a novel approach to solving the localization problem in cellular networks. The goal is to estimate a mobile user's position, based on measurements of the signal strengths received from network base stations. Our solution works by building Gaussian process models for the distribution of signal strengths, as obtained in a series of calibration measurements. In the localization stage, the user's position can be estimated by maximizing the likelihood of received signal strengths with respect to the position. We investigate the accuracy of the proposed approach on data obtained within a large indoor cellular network. We report results obtained by the UoW method in SemEval-2014’s Task 10 – Multilingual Semantic Textual Similarity. We propose to model Semantic Textual Similarity in the context of Multi-task Learning in order to deal with inherent challenges of the task such as unbalanced performance across domains and the lack of training data for some domains (i.e. unknown domains). We show that the Multi-task Learning approach outperforms previous work on the 2012 dataset, achieves a robust performance on the 2013 dataset and competitive results on the 2014 dataset. We highlight the importance of the challenge of unknown domains, as it affects overall performance substantially. We describe our systems for the WMT14 Shared Task on Quality Estimation (subtasks 1.1, 1.2 and 1.3). Our submissions use the framework of Multi-task Gaussian Processes, where we combine multiple datasets in a multi-task setting. Due to the large size of our datasets we also experiment with Sparse Gaussian Processes, which aim to speed up training and prediction by providing sensible sparse approximations.