GoGP: Fast Online Regression with Gaussian Processes

Le, Trung; Nguyen, Khanh; Nguyen, Vu; Nguyen, Tu Dinh; Phung, Dinh

doi:10.1109/icdm.2017.35

Cited by 16 publications

(9 citation statements)

References 9 publications

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“…Sparse GPs are extensible for online learning since they employ a small inducing set to summarize the whole training data [206]- [208]. As a result, the arrived new data interacts only with the inducing points to enhance fast online learning.…”

Section: Scalable Online Gpmentioning

confidence: 99%

When Gaussian Process Meets Big Data: A Review of Scalable GPs

Liu

Ong

Shen

et al. 2020

IEEE Trans. Neural Netw. Learning Syst.

566

320

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The vast quantity of information brought by big data as well as the evolving computer hardware encourages success stories in the machine learning community. In the meanwhile, it poses challenges for the Gaussian process (GP) regression, a well-known non-parametric and interpretable Bayesian model, which suffers from cubic complexity to data size. To improve the scalability while retaining desirable prediction quality, a variety of scalable GPs have been presented. But they have not yet been comprehensively reviewed and analyzed in order to be well understood by both academia and industry. The review of scalable GPs in the GP community is timely and important due to the explosion of data size. To this end, this paper is devoted to the review on state-of-the-art scalable GPs involving two main categories: global approximations which distillate the entire data and local approximations which divide the data for subspace learning. Particularly, for global approximations, we mainly focus on sparse approximations comprising prior approximations which modify the prior but perform exact inference, posterior approximations which retain exact prior but perform approximate inference, and structured sparse approximations which exploit specific structures in kernel matrix; for local approximations, we highlight the mixture/product of experts that conducts model averaging from multiple local experts to boost predictions. To present a complete review, recent advances for improving the scalability and capability of scalable GPs are reviewed. Finally, the extensions and open issues regarding the implementation of scalable GPs in various scenarios are reviewed and discussed to inspire novel ideas for future research avenues.Index Terms-Gaussian process regression, big data, scalability, sparse approximations, local approximations Haitao Liu is with the Rolls-

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Section: Scalable Online Gpmentioning

confidence: 99%

When Gaussian Process Meets Big Data: A Review of Scalable GPs

Liu

Ong

Shen

et al. 2020

IEEE Trans. Neural Netw. Learning Syst.

566

320

View full text Add to dashboard Cite

show abstract

“…Regarding regression analysis, Le et al [124] present the geometric-based online Gaussian process that could scale with massive datasets, guaranteeing that the proposed algorithm produces a good enough solution (close to the optimal one) and a fast-online regression. Marx and Vreeken [125] present an information theory-based approach using the Kolmogorov complexity and the principle of minimum description length to provide a practical solution to the problem of inferring the direction of causal dependence of observational data.…”

Section: Recent Methods For General Applicationsmentioning

confidence: 99%

Predictive Data Mining Techniques for Fault Diagnosis of Electric Equipment: A Review

et al. 2020

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Data mining is a technological and scientific field that, over the years, has been gaining more importance in many areas, attracting scientists, developers, and researchers around the world. The reason for this enthusiasm derives from the remarkable benefits of its usefulness, such as the exploitation of large databases and the use of the information extracted from them in an intelligent way through the analysis and discovery of knowledge. This document provides a review of the predictive data mining techniques used for the diagnosis and detection of faults in electric equipment, which constitutes the pillar of any industrialized country. Starting from the year 2000 to the present, a revision of the methods used in the tasks of classification and regression for the diagnosis of electric equipment is carried out. Current research on data mining techniques is also listed and discussed according to the results obtained by different authors.

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“…In particular, we use a bidirectional recurrent neural network (BRNN) to summarize a sequence of machine instructions in binary software into a representation vector. This vector is then mapped into a Fourier random feature space via a finitedimensional random feature map [9,11,14,17,19]. Our proposed Cost-sensitive Kernel Machine (CKM) is invoked in the random feature space to detect vulnerable binary software.…”

Section: Our Approach: Deep Cost-sensitive Kernel Machinementioning

confidence: 99%

Deep Cost-Sensitive Kernel Machine for Binary Software Vulnerability Detection

Nguyen

Nguyen³

et al. 2020

Lecture Notes in Computer Science

Self Cite

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Owing to the sharp rise in the severity of the threats imposed by software vulnerabilities, software vulnerability detection has become an important concern in the software industry, such as the embedded systems industry, and in the field of computer security. Software vulnerability detection can be carried out at the source code or binary level. However, the latter is more impactful and practical since when using commercial software, we usually only possess binary software. In this paper, we leverage deep learning and kernel methods to propose the Deep Cost-sensitive Kernel Machine, a method that inherits the advantages of deep learning methods in efficiently tackling structural data and kernel methods in learning the characteristic of vulnerable binary examples with high generalization capacity. We conduct experiments on two real-world binary datasets. The experimental results have shown a convincing outperformance of our proposed method over the baselines.

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GoGP: Fast Online Regression with Gaussian Processes

Cited by 16 publications

References 9 publications

When Gaussian Process Meets Big Data: A Review of Scalable GPs

When Gaussian Process Meets Big Data: A Review of Scalable GPs

Predictive Data Mining Techniques for Fault Diagnosis of Electric Equipment: A Review

Deep Cost-Sensitive Kernel Machine for Binary Software Vulnerability Detection

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