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Supervised restricted boltzmann machines 33

Supervised restricted boltzmann machines

引用

33rd Conference on Uncertainty in Artificial Intelligence, UAI 2017

作者： Nguyen, Tu Dinh Phung, Dinh Huynh, Viet Le, Trung Center for Pattern Recognition and Data Analytics Deakin University Australia

We propose in this paper the supervised re- stricted Boltzmann machine (sRBM), a unified framework which combines the versatility of RBM to simultaneously learn the data representation and to perform supervised learning (i.e., a nonlinear classifier or a nonlinear regressor). Unlike the current state-of-the-art classification formulation proposed for RBM in (Larochelle et al., 2012), our model is a hybrid probabilistic graphical model consisting of a distinguished genera- tive component for data representation and a dis- criminative component for prediction. While the work of (Larochelle et al., 2012) typically incurs no extra difficulty in inference compared with a standard RBM, our discriminative component, modeled as a directed graphical model, renders MCMC-based inference (e.g., Gibbs sampler) very slow and unpractical for use. To this end, we further develop scalable variational inference for the proposed sRBM for both classification and regression cases. Extensive experiments on realworld datasets show that our sRBM achieves better predictive performance than baseline methods. At the same time, our proposed framework yields learned representations which are more discriminative, hence interpretable, than those of its counterparts. Besides, our method is probabilistic and capable of generating meaningful data conditioning on specific classes - a topic which is of current great interest in deep learning aiming at data generation.

关键词： Graphic methods

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Distributed data augmented support vector machine on Spark 23

Distributed data augmented support vector machine on Spark

引用

23rd International Conference on pattern recognition, ICPR 2016

作者： Nguyen, Tu Dinh Nguyen, Vu Le, Trung Phung, Dinh Center for Pattern Recognition and Data Analytics Deakin University Australia

ISBN: (纸本)9781509048472

Support vector machines (SVMs) are widely-used for classification in machine learning and data mining tasks. However, they traditionally have been applied to small to medium datasets. Recent need to scale up with data size has attracted research attention to develop new methods and implementation for SVM to perform tasks at scale. Distributed SVMs are relatively new and studied recently, but the distributed implementation for SVM with data augmentation has not been developed. This paper introduces a distributed data augmentation implementation for SVM on Apache Spark, a recent advanced and popular platform for distributed computing that has been employed widely in research as well as in industry. We term our implementation sparkling vector machine (SkVM) which supports both classification and regression tasks by scanning through the data exactly once. In addition, we further develop a framework to handle the data with new classes arriving under an online classification setting where new data points can have labels that have not previously seen - a problem we term label-drift classification. We demonstrate the scalability of our proposed method on large-scale datasets with more than one hundred million data points. The experimental results show that the predictive performances of our method are comparable or better than those of baselines whilst the execution time is much faster at an order of magnitude. © 2016 IEEE.

关键词： Support vector machines

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A Bayesian nonparametric approach for multi-label classification 8

A Bayesian nonparametric approach for multi-label classifica...

引用

8th Asian Conference on Machine Learning, ACML 2016

作者： Nguyen, Vu Gupta, Sunil Rana, Santu Li, Cheng Venkatesh, Svetha Center for Pattern Recognition and Data Analytics Deakin University Australia

Many real-world applications require multi-label classification where multiple target labels are assigned to each instance. In multi-label classification, there exist the intrinsic correlations between the labels and features. These correlations are beneficial for multi-label classification task since they reflect the coexistence of the input and output spaces that can be exploited for prediction. Traditional classification methods have attempted to reveal these correlations in different ways. However, existing methods demand expensive computation complexity for finding such correlation structures. Furthermore, these approaches can not identify the suitable number of label-feature correlation patterns. In this paper, we propose a Bayesian nonparametric (BNP) framework for multi-label classification that can automatically learn and exploit the unknown number of multi-label correlation. We utilize the recent techniques in stochastic inference to derive the cheap (but efficient) posterior inference algorithm for the model. In addition, our model can naturally exploit the useful information from missing label samples. Furthermore, we extend the model to update parameters in an online fashion that highlights the flexibility of our model against the existing approaches. We compare our method with the state-of-the-art multi-label classification algorithms on real-world datasets using both complete and missing label settings. Our model achieves better classification accuracy while our running time is consistently much faster than the baselines in an order of magnitude. © 2016 V. Nguyen, S. Gupta, S. Rana, C. Li & S. Venkatesh.

关键词： Stochastic models

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Regret for expected improvement over the best-observed value and stopping condition 9

Regret for expected improvement over the best-observed value...

引用

9th Asian Conference on Machine Learning, ACML 2017

作者： Nguyen, Vu Gupta, Sunil Rana, Santu Li, Cheng Venkatesh, Svetha Deakin University Center for Pattern Recognition and Data Analytics Geelong Australia

Bayesian optimization (BO) is a sample-efficient method for global optimization of expensive, noisy, black-box functions using probabilistic methods. The performance of a BO method depends on its selection strategy through the acquisition function. Expected improvement (EI) is one of the most widely used acquisition functions for BO that finds the expectation of the improvement function over the incumbent. The incumbent is usually selected as the best-observed value so far, termed as ymax (for the maximizing problem). Recent work has studied the convergence rate for EI under some mild assumptions or zero noise of observations. Especially, the work of Wang and de Freitas (2014) has derived the sublinear regret for EI under a stochastic noise. However, due to the difficulty in stochastic noise setting and to make the convergent proof feasible, they use an alternative choice for the incumbent as the maximum of the Gaussian process predictive mean, µmax. This modification makes the algorithm computationally inefficient because it requires an additional global optimization step to estimate µmax that is costly and may be inaccurate. To address this issue, we derive a sublinear convergence rate for EI using the commonly used ymax. Moreover, our analysis is the first to study a stopping criteria for EI to prevent unnecessary evaluations. Our analysis complements the results of Wang and de Freitas (2014) to theoretically cover two incumbent settings for EI. Finally, we demonstrate empirically that EI using ymax is both more computationally efficiency and more accurate than EI using µmax. © 2017 V. Nguyen, S. Gupta, S. Rana, C. Li & S. Venkatesh.

关键词： Global optimization

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Faster training of very deep networks via p-norm gates 23

Faster training of very deep networks via p-norm gates

引用

23rd International Conference on pattern recognition, ICPR 2016

作者： Pham, Trang Tran, Truyen Phung, Dinh Venkatesh, Svetha Center for Pattern Recognition and Data Analytics Deakin University Geelong Australia

ISBN: (纸本)9781509048472

A major contributing factor to the recent advances in deep neural networks is structural units that let sensory information and gradients to propagate easily. Gating is one such structure that acts as a flow control. Gates are employed in many recent state-of-the-art recurrent models such as LSTM and GRU, and feedforward models such as Residual Nets and Highway Networks. This enables learning in very deep networks with hundred layers and helps achieve record-breaking results in vision (e.g., ImageNet with Residual Nets) and NLP (e.g., machine translation with GRU). However, there is limited work in analysing the role of gating in the learning process. In this paper, we propose a flexible p-norm gating scheme, which allows user-controllable flow and as a consequence, improve the learning speed. This scheme subsumes other existing gating schemes, including those in GRU, Highway Networks and Residual Nets as special cases. Experiments on large sequence and vector datasets demonstrate that the proposed gating scheme helps improve the learning speed significantly without extra overhead. © 2016 IEEE.

关键词： Deep neural networks

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Stabilizing linear prediction models using autoencoder 12th

Stabilizing linear prediction models using autoencoder

引用

12th International Conference on Advanced data Mining and Applications, ADMA 2016

作者： Gopakumar, Shivapratap Tran, Truyen Phung, Dinh Venkatesh, Svetha Center for Pattern Recognition and Data Analytics Deakin University Burwood Australia

ISBN: (纸本)9783319495859

To date, the instability of prognostic predictors in a sparse high dimensional model, which hinders their clinical adoption, has received little attention. Stable prediction is often overlooked in favour of performance. Yet, stability prevails as key when adopting models in critical areas as healthcare. Our study proposes a stabilization scheme by detecting higher order feature correlations. Using a linear model as basis for prediction, we achieve feature stability by regularizing latent correlation in features. Latent higher order correlation among features is modelled using an autoencoder network. Stability is enhanced by combining a recent technique that uses a feature graph, and augmenting external unlabelled data for training the autoencoder network. Our experiments are conducted on a heart failure cohort from an Australian hospital. Stability was measured using Consistency index for feature subsets and signal-to-noise ratio for model parameters. Our methods demonstrated significant improvement in feature stability and model estimation stability when compared to baselines. © Springer International Publishing AG 2016.

关键词： Stability

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Intervention-driven predictive framework for modeling healthcare data

Intervention-driven predictive framework for modeling health...

引用

18th Pacific-Asia Conference on Advances in Knowledge Discovery and data Mining, PAKDD 2014

作者： Rana, Santu Gupta, Sunil Kumar Phung, Dinh Venkatesh, Svetha Center for Pattern Recognition and Data Analytics Deakin University 3216 Australia

ISBN: (纸本)9783319066073

Assessing prognostic risk is crucial to clinical care, and critically dependent on both diagnosis and medical interventions. Current methods use this augmented information to build a single prediction rule. But this may not be expressive enough to capture differential effects of interventions on prognosis. To this end, we propose a supervised, Bayesian nonparametric framework that simultaneously discovers the latent intervention groups and builds a separate prediction rule for each intervention group. The prediction rule is learnt using diagnosis data through a Bayesian logistic regression. For inference, we develop an efficient collapsed Gibbs sampler. We demonstrate that our method outperforms baselines in predicting 30-day hospital readmission using two patient cohorts - Acute Myocardial Infarction and Pneumonia. The significance of this model is that it can be applied widely across a broad range of medical prognosis tasks. © 2014 Springer International Publishing.

关键词： Diagnosis

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Differentially private multi-task learning 11th

Differentially private multi-task learning

引用

11th Pacific Asia Workshop on Intelligence and Security Informatics, PAISI 2016

作者： Gupta, Sunil Kumar Rana, Santu Venkatesh, Svetha Center for Pattern Recognition and Data Analytics Deakin University Geelong3216 Australia

ISBN: (纸本)9783319318622

Privacy restrictions of sensitive data repositories imply that the data analysis is performed in isolation at each data source. A prime example is the isolated nature of building prognosis models from hospital data and the associated challenge of dealing with small number of samples in risk classes (e.g. suicide) while doing so. Pooling knowledge from other hospitals, through multi-task learning, can alleviate this problem. However, if knowledge is to be shared unrestricted, privacy is breached. Addressing this, we propose a novel multi-task learning method that preserves privacy of data under the strong guarantees of differential privacy. Further, we develop a novel attribute-wise noise addition scheme that significantly lifts the utility of the proposed method. We demonstrate the effectiveness of our method with a synthetic and two real datasets. © Springer International Publishing Switzerland 2016.

关键词： Hospitals

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A privacy preserving bayesian optimization with high efficiency 22nd

A privacy preserving bayesian optimization with high efficie...

引用

22nd Pacific-Asia Conference on Advances in Knowledge Discovery and data Mining, PAKDD 2018

作者： Nguyen, Thanh Dai Gupta, Sunil Rana, Santu Venkatesh, Svetha Center for Pattern Recognition and Data Analytics Deakin University Waurn Ponds3216 Australia

ISBN: (纸本)9783319930398

Bayesian optimization is a powerful machine learning technique for solving experimental design problems. With its use in industrial design optimization, time and cost of industrial processes can be reduced significantly. However, often the experimenters in industries may not have the expertise of optimization techniques and may require help from third-party optimization services. This can cause privacy concerns as the optimized design of an industrial process typically needs to be kept secret to retain its competitive advantages. To this end, we propose a novel Bayesian optimization algorithm that can allow the experimenters from an industry to utilize the expertise of a third-party optimization service in privacy preserving manner. Privacy of our proposed algorithm is guaranteed under a modern privacy preserving framework called Error Preserving Privacy, especially designed to maintain high utility even under the privacy restrictions. Using several benchmark optimization problems as well as optimization problems from real-world industrial processes, we demonstrate that the optimization efficiency of our algorithm is comparable to the non-private Bayesian optimization algorithm and significantly better than its differential privacy counterpart. © Springer International Publishing AG, part of Springer Nature 2018.

关键词： Product design

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Split-merge augmented Gibbs sampling for Hierarchical Dirichlet Processes

引用

17th Pacific-Asia Conference on Knowledge Discovery and data Mining, PAKDD 2013

作者： Rana, Santu Phung, Dinh Venkatesh, Svetha Center for Pattern Recognition and Data Analytics Deakin University Waurn Ponds VIC 3216 Australia

ISBN: (纸本)9783642374555

The Hierarchical Dirichlet Process (HDP) model is an important tool for topic analysis. Inference can be performed through a Gibbs sampler using the auxiliary variable method. We propose a splitmerge procedure to augment this method of inference, facilitating faster convergence. Whilst the incremental Gibbs sampler changes topic assignments of each word conditioned on the previous observations and model hyper-parameters, the split-merge sampler changes the topic assignments over a group of words in a single move. This allows efficient exploration of state space. We evaluate the proposed sampler on a synthetic test set and two benchmark document corpus and show that the proposed sampler enables the MCMC chain to converge faster to the desired stationary distribution. © Springer-Verlag 2013.

关键词： Artificial intelligence

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