ORCID Profile
0000-0002-0383-1462
Current Organisation
University of South Australia
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
Publisher: Elsevier BV
Date: 06-2016
Publisher: Springer Science and Business Media LLC
Date: 06-2016
Publisher: Springer Science and Business Media LLC
Date: 13-06-2016
Publisher: Elsevier BV
Date: 08-2017
Publisher: Springer Science and Business Media LLC
Date: 20-09-2016
Publisher: Elsevier BV
Date: 2017
Publisher: SAGE Publications
Date: 31-10-2014
Publisher: International Joint Conferences on Artificial Intelligence Organization
Date: 07-2022
Abstract: Unobserved confounding is the main obstacle to causal effect estimation from observational data. Instrumental variables (IVs) are widely used for causal effect estimation when there exist latent confounders. With the standard IV method, when a given IV is valid, unbiased estimation can be obtained, but the validity requirement on a standard IV is strict and untestable. Conditional IVs have been proposed to relax the requirement of standard IVs by conditioning on a set of observed variables (known as a conditioning set for a conditional IV). However, the criterion for finding a conditioning set for a conditional IV needs a directed acyclic graph (DAG) representing the causal relationships of both observed and unobserved variables. This makes it challenging to discover a conditioning set directly from data. In this paper, by leveraging maximal ancestral graphs (MAGs) for causal inference with latent variables, we study the graphical properties of ancestral IVs, a type of conditional IVs using MAGs, and develop the theory to support data-driven discovery of the conditioning set for a given ancestral IV in data under the pretreatment variable assumption. Based on the theory, we develop an algorithm for unbiased causal effect estimation with a given ancestral IV and observational data. Extensive experiments on synthetic and real-world datasets demonstrate the performance of the algorithm in comparison with existing IV methods.
Publisher: Springer Science and Business Media LLC
Date: 20-04-2022
DOI: 10.1007/S10618-022-00832-5
Abstract: A large number of covariates can have a negative impact on the quality of causal effect estimation since confounding adjustment becomes unreliable when the number of covariates is large relative to the number of s les. Propensity score is a common way to deal with a large covariate set, but the accuracy of propensity score estimation (normally done by logistic regression) is also challenged by the large number of covariates. In this paper, we prove that a large covariate set can be reduced to a lower dimensional representation which captures the complete information for adjustment in causal effect estimation. The theoretical result enables effective data-driven algorithms for causal effect estimation. Supported by the result, we develop an algorithm that employs a supervised kernel dimension reduction method to learn a lower dimensional representation from the original covariate space, and then utilises nearest neighbour matching in the reduced covariate space to impute the counterfactual outcomes to avoid the large sized covariate set problem. The proposed algorithm is evaluated on two semisynthetic and three real-world datasets and the results show the effectiveness of the proposed algorithm.
Publisher: American Society of Tropical Medicine and Hygiene
Date: 07-2010
Publisher: Springer Science and Business Media LLC
Date: 17-10-2015
Publisher: Springer Science and Business Media LLC
Date: 23-11-2016
Publisher: IEEE
Date: 12-2022
Publisher: Springer Science and Business Media LLC
Date: 08-10-2016
Publisher: arXiv
Date: 2022
Publisher: Springer Science and Business Media LLC
Date: 13-10-2017
Publisher: arXiv
Date: 2022
Publisher: Springer International Publishing
Date: 2014
Publisher: Elsevier BV
Date: 06-2016
Publisher: Springer Science and Business Media LLC
Date: 21-12-2016
Publisher: Elsevier BV
Date: 03-2020
Publisher: Springer International Publishing
Date: 2014
Publisher: Springer Science and Business Media LLC
Date: 12-06-2023
Publisher: Elsevier BV
Date: 05-2017
Publisher: Elsevier BV
Date: 07-2018
Publisher: Association for Computing Machinery (ACM)
Date: 12-01-2017
DOI: 10.1145/2990508
Abstract: The K Nearest Neighbor (kNN) method has widely been used in the applications of data mining and machine learning due to its simple implementation and distinguished performance. However, setting all test data with the same k value in the previous kNN methods has been proven to make these methods impractical in real applications. This article proposes to learn a correlation matrix to reconstruct test data points by training data to assign different k values to different test data points, referred to as the Correlation Matrix kNN (CM-kNN for short) classification. Specifically, the least-squares loss function is employed to minimize the reconstruction error to reconstruct each test data point by all training data points. Then, a graph Laplacian regularizer is advocated to preserve the local structure of the data in the reconstruction process. Moreover, an ℓ 1 -norm regularizer and an ℓ 2, 1 -norm regularizer are applied to learn different k values for different test data and to result in low sparsity to remove the redundant/noisy feature from the reconstruction process, respectively. Besides for classification tasks, the kNN methods (including our proposed CM-kNN method) are further utilized to regression and missing data imputation. We conducted sets of experiments for illustrating the efficiency, and experimental results showed that the proposed method was more accurate and efficient than existing kNN methods in data-mining applications, such as classification, regression, and missing data imputation.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2023
Publisher: Elsevier BV
Date: 06-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: IEEE
Date: 12-2022
Publisher: IEEE
Date: 12-2022
Publisher: International Joint Conferences on Artificial Intelligence Organization
Date: 07-2022
Abstract: Although meta-learning and metric learning have been widely applied for few-shot node classification (FSNC), some limitations still need to be addressed, such as expensive time costs for the meta-train and difficult of exploring the complex structure inherent the graph data. To address in issues, this paper proposes a new data augmentation method to conduct FSNC on the graph data including parameter initialization and parameter fine-tuning. Specifically, parameter initialization only conducts a multi-classification task on the base classes, resulting in good generalization ability and less time cost. Parameter fine-tuning designs two data augmentation methods (i.e., support augmentation and shot augmentation) on the novel classes to generate sufficient node features so that any traditional supervised classifiers can be used to classify the query set. As a result, the proposed method is the first work of data augmentation for FSNC. Experiment results show the effectiveness and the efficiency of our proposed method, compared to state-of-the-art methods, in terms of different classification tasks.
Publisher: Springer International Publishing
Date: 2022
Publisher: Springer International Publishing
Date: 2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2023
Publisher: Springer Science and Business Media LLC
Date: 15-12-2016
No related grants have been discovered for Debo Cheng.