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Sparse autoencoder based semi-supervised learning for phone classification with limited annotations

Published in Proceedings of the GLU 2017 International Workshop on Grounding Language Understanding, Stockholm, Sweden, 2017

We propose the application of a semi-supervised learning method to improve the performance of acoustic modelling for automatic speech recognition with limited linguistically annotated material. Our method combines sparse autoencoders with feed-forward networks, thus taking advantage of both unlabelled and labelled data simultaneously through mini-batch stochastic gradient descent. We tested the method with varying proportions of labelled vs unlabelled observations in frame based phoneme classification on the TIMIT database. Our experiments show that the method outperforms standard supervised models of similar complexity for an equal amount of labelled data and provides competitive error rates compared to state-of-the-art graph-based semi-supervised learning techniques

Recommended citation: Your Name, You. (2015). "Paper Title Number 3." Journal 1. 1(3).

Preferential Batch Bayesian Optimisation

Published in Arxiv, 2020

Most research in Bayesian optimization (BO) has focused on direct feedback scenarios, where one has access to exact, or perturbed, values of some expensive-to-evaluate objective. This direction has been mainly driven by the use of BO in machine learning hyper-parameter configuration problems. However, in domains such as modelling human preferences, A/B tests or recommender systems, there is a need of methods that are able to replace direct feedback with preferential feedback, obtained via rankings or pairwise comparisons. In this work, we present Preferential Batch Bayesian Optimization (PBBO), a new framework that allows to find the optimum of a latent function of interest, given any type of parallel preferential feedback for a group of two or more points. We do so by using a Gaussian process model with a likelihood specially designed to enable parallel and efficient data collection mechanisms, which are key in modern machine learning. We show how the acquisitions developed under this framework generalize and augment previous approaches in Bayesian optimization, expanding the use of these techniques to a wider range of domains. An extensive simulation study shows the benefits of this approach, both with simulated functions and four real data sets

Recommended citation: Your Name, You. (2010). "Paper Title Number 2." Journal 1. 1(2).

Scalable Gaussian Process for Extreme Classification

Published in IEEE MLSP 2020, 2020

We address the limitations of Gaussian processes for multiclass classification in the setting where both the number of classes and the number of observations is very large. We propose a scalable approximate inference framework by combining the inducing points method with variational approximations of the likelihood that have been recently proposed in the litera- ture. This leads to a tractable lower bound on the marginal likelihood that decomposes into a sum over both data points and class labels, and hence, is amenable to doubly stochastic optimization. To overcome memory issues when dealing with large datasets, we resort to amortized inference, which coupled with subsampling over classes reduces the computational and the memory footprint without a significant loss in performance. We demonstrate empirically that the proposed algorithm leads to superior performance in terms of test accuracy, and im- proved detection of tail labels.

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Robust Accurate Stochastic Optimization for Variational Inference

Published in Arxiv, Submitted, 2020

This work considers the challenging problem of fitting variational posterior approximations using stochastic optimization methods. The performance of these approximations depends on (1) how well the variational family matches the true posterior distribution, (2) the choice of divergence, and (3) the optimization of the variational objective. We show that even in the best-case scenario when the exact posterior belongs to the assumed variational family, common stochastic optimization algorithms lead to poor variational approximations if the problem dimension is moderately large. We also demonstrate that these methods are not robust across diverse model types. Motivated by these findings, we develop a more robust and accurate stochastic optimization framework by viewing the underlying optimization algorithm as producing a Markov chain. Our approach is theoretically motivated and includes a diagnostic for convergence and a novel stopping rule, both of which are robust to noisy evaluations of the objective function.

Recommended citation: Your Name, You. (2010). "Paper Title Number 2." Journal 1. 1(2).