367 citations · 1.3k across the 62 of their papers we have counts for
7 papers · 1 filter
Understanding and Comparing Scalable Gaussian Process Regression for Big Data
Haitao Liu, Jianfei Cai, Yew-Soon Ong +1
As a non-parametric Bayesian model which produces informative predictive distribution, Gaussian process (GP) has been widely used in various fields, like regression, classification…
Towards Safer Smart Contracts: A Sequence Learning Approach to Detecting Security Threats
Wesley Joon-Wie Tann, Xing Jie Han, Sourav Sen Gupta +1
Symbolic analysis of security exploits in smart contracts has demonstrated to be valuable for analyzing predefined vulnerability properties. While some symbolic tools perform compl…
Large-scale Heteroscedastic Regression via Gaussian Process
Haitao Liu, Yew-Soon Ong, Jianfei Cai
Heteroscedastic regression considering the varying noises among observations has many applications in the fields like machine learning and statistics. Here we focus on the heterosc…
Ockham's Razor in Memetic Computing: Three Stage Optimal Memetic Exploration
G. Iacca, F. Neri, E. Mininno +2
Memetic Computing is a subject in computer science which considers complex structures as the combination of simple agents, memes, whose evolutionary interactions lead to intelligen…
Autonomous Deep Learning: Incremental Learning of Denoising Autoencoder for Evolving Data Streams
Mahardhika Pratama, Andri Ashfahani, Yew Soon Ong +2
The generative learning phase of Autoencoder (AE) and its successor Denosing Autoencoder (DAE) enhances the flexibility of data stream method in exploiting unlabelled samples. None…
When Gaussian Process Meets Big Data: A Review of Scalable GPs
Haitao Liu, Yew-Soon Ong, Xiaobo Shen +1
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 po…
Generalized Robust Bayesian Committee Machine for Large-scale Gaussian Process Regression
Haitao Liu, Jianfei Cai, Yi Wang +1
In order to scale standard Gaussian process (GP) regression to large-scale datasets, aggregation models employ factorized training process and then combine predictions from distrib…