The Experts below are selected from a list of 139554 Experts worldwide ranked by ideXlab platform

James Geddes - One of the best experts on this subject based on the ideXlab platform.

  • ptype: probabilistic type Inference
    Data Mining and Knowledge Discovery, 2020
    Co-Authors: Taha Ceritli, Christopher K. I. Williams, James Geddes
    Abstract:

    Type Inference refers to the task of inferring the data type of a given column of data. Current approaches often fail when data contains missing data and anomalies, which are found commonly in real-world data sets. In this paper, we propose ptype , a probabilistic robust type Inference Method that allows us to detect such entries, and infer data types. We further show that the proposed Method outperforms existing Methods.

  • ptype: Probabilistic Type Inference
    Data Mining and Knowledge Discovery, 2020
    Co-Authors: Taha Ceritli, Christopher K. I. Williams, James Geddes
    Abstract:

    Type Inference refers to the task of inferring the data type of a given column of data. Current approaches often fail when data contains missing data and anomalies, which are found commonly in real-world data sets. In this paper, we propose ptype, a probabilistic robust type Inference Method that allows us to detect such entries, and infer data types. We further show that the proposed Method outperforms the existing Methods.

Tatseng Chua - One of the best experts on this subject based on the ideXlab platform.

  • should graph convolution trust neighbors a simple causal Inference Method
    International ACM SIGIR Conference on Research and Development in Information Retrieval, 2021
    Co-Authors: Fuli Feng, Weiran Huang, Xin Xin, Qifan Wang, Tatseng Chua
    Abstract:

    Graph Convolutional Network (GCN) is an emerging technique for information retrieval (IR) applications. While GCN assumes the homophily property of a graph, real-world graphs are never perfect: the local structure of a node may contain discrepancy, e.g., the labels of a node's neighbors could vary. This pushes us to consider the discrepancy of local structure in GCN modeling. Existing work approaches this issue by introducing an additional module such as graph attention, which is expected to learn the contribution of each neighbor. However, such module may not work reliably as expected, especially when there lacks supervision signal, e.g., when the labeled data is small. Moreover, existing Methods focus on modeling the nodes in the training data, and never consider the local structure discrepancy of testing nodes. This work focuses on the local structure discrepancy issue for testing nodes, which has received little scrutiny. From a novel perspective of causality, we investigate whether a GCN should trust the local structure of a testing node when predicting its label. To this end, we analyze the working mechanism of GCN with causal graph, estimating the causal effect of a node's local structure for the prediction. The idea is simple yet effective: given a trained GCN model, we first intervene the prediction by blocking the graph structure; we then compare the original prediction with the intervened prediction to assess the causal effect of the local structure on the prediction. Through this way, we can eliminate the impact of local structure discrepancy and make more accurate prediction. Extensive experiments on seven node classification datasets show that our causality-based Method effectively enhances the Inference stage of GCN.

  • should graph convolution trust neighbors a simple causal Inference Method
    arXiv: Learning, 2020
    Co-Authors: Fuli Feng, Weiran Huang, Xin Xin, Qifan Wang, Tatseng Chua
    Abstract:

    Graph Convolutional Network (GCN) is an emerging technique for information retrieval (IR) applications. While GCN assumes the homophily property of a graph, real-world graphs are never perfect: the local structure of a node may contain discrepancy, e.g., the labels of a node's neighbors could vary. This pushes us to consider the discrepancy of local structure in GCN modeling. Existing work approaches this issue by introducing an additional module such as graph attention, which is expected to learn the contribution of each neighbor. However, such module may not work reliably as expected, especially when there lacks supervision signal, e.g., when the labeled data is small. Moreover, existing Methods focus on modeling the nodes in the training data, and never consider the local structure discrepancy of testing nodes. This work focuses on the local structure discrepancy issue for testing nodes, which has received little scrutiny. From a novel perspective of causality, we investigate whether a GCN should trust the local structure of a testing node when predicting its label. To this end, we analyze the working mechanism of GCN with causal graph, estimating the causal effect of a node's local structure for the prediction. The idea is simple yet effective: given a trained GCN model, we first intervene the prediction by blocking the graph structure; we then compare the original prediction with the intervened prediction to assess the causal effect of the local structure on the prediction. Through this way, we can eliminate the impact of local structure discrepancy and make more accurate prediction. Extensive experiments on seven node classification datasets show that our Method effectively enhances the Inference stage of GCN.

Taha Ceritli - One of the best experts on this subject based on the ideXlab platform.

  • ptype: probabilistic type Inference
    Data Mining and Knowledge Discovery, 2020
    Co-Authors: Taha Ceritli, Christopher K. I. Williams, James Geddes
    Abstract:

    Type Inference refers to the task of inferring the data type of a given column of data. Current approaches often fail when data contains missing data and anomalies, which are found commonly in real-world data sets. In this paper, we propose ptype , a probabilistic robust type Inference Method that allows us to detect such entries, and infer data types. We further show that the proposed Method outperforms existing Methods.

  • ptype: Probabilistic Type Inference
    Data Mining and Knowledge Discovery, 2020
    Co-Authors: Taha Ceritli, Christopher K. I. Williams, James Geddes
    Abstract:

    Type Inference refers to the task of inferring the data type of a given column of data. Current approaches often fail when data contains missing data and anomalies, which are found commonly in real-world data sets. In this paper, we propose ptype, a probabilistic robust type Inference Method that allows us to detect such entries, and infer data types. We further show that the proposed Method outperforms the existing Methods.

Christopher K. I. Williams - One of the best experts on this subject based on the ideXlab platform.

  • ptype: probabilistic type Inference
    Data Mining and Knowledge Discovery, 2020
    Co-Authors: Taha Ceritli, Christopher K. I. Williams, James Geddes
    Abstract:

    Type Inference refers to the task of inferring the data type of a given column of data. Current approaches often fail when data contains missing data and anomalies, which are found commonly in real-world data sets. In this paper, we propose ptype , a probabilistic robust type Inference Method that allows us to detect such entries, and infer data types. We further show that the proposed Method outperforms existing Methods.

  • ptype: Probabilistic Type Inference
    Data Mining and Knowledge Discovery, 2020
    Co-Authors: Taha Ceritli, Christopher K. I. Williams, James Geddes
    Abstract:

    Type Inference refers to the task of inferring the data type of a given column of data. Current approaches often fail when data contains missing data and anomalies, which are found commonly in real-world data sets. In this paper, we propose ptype, a probabilistic robust type Inference Method that allows us to detect such entries, and infer data types. We further show that the proposed Method outperforms the existing Methods.

Fuli Feng - One of the best experts on this subject based on the ideXlab platform.

  • should graph convolution trust neighbors a simple causal Inference Method
    International ACM SIGIR Conference on Research and Development in Information Retrieval, 2021
    Co-Authors: Fuli Feng, Weiran Huang, Xin Xin, Qifan Wang, Tatseng Chua
    Abstract:

    Graph Convolutional Network (GCN) is an emerging technique for information retrieval (IR) applications. While GCN assumes the homophily property of a graph, real-world graphs are never perfect: the local structure of a node may contain discrepancy, e.g., the labels of a node's neighbors could vary. This pushes us to consider the discrepancy of local structure in GCN modeling. Existing work approaches this issue by introducing an additional module such as graph attention, which is expected to learn the contribution of each neighbor. However, such module may not work reliably as expected, especially when there lacks supervision signal, e.g., when the labeled data is small. Moreover, existing Methods focus on modeling the nodes in the training data, and never consider the local structure discrepancy of testing nodes. This work focuses on the local structure discrepancy issue for testing nodes, which has received little scrutiny. From a novel perspective of causality, we investigate whether a GCN should trust the local structure of a testing node when predicting its label. To this end, we analyze the working mechanism of GCN with causal graph, estimating the causal effect of a node's local structure for the prediction. The idea is simple yet effective: given a trained GCN model, we first intervene the prediction by blocking the graph structure; we then compare the original prediction with the intervened prediction to assess the causal effect of the local structure on the prediction. Through this way, we can eliminate the impact of local structure discrepancy and make more accurate prediction. Extensive experiments on seven node classification datasets show that our causality-based Method effectively enhances the Inference stage of GCN.

  • should graph convolution trust neighbors a simple causal Inference Method
    arXiv: Learning, 2020
    Co-Authors: Fuli Feng, Weiran Huang, Xin Xin, Qifan Wang, Tatseng Chua
    Abstract:

    Graph Convolutional Network (GCN) is an emerging technique for information retrieval (IR) applications. While GCN assumes the homophily property of a graph, real-world graphs are never perfect: the local structure of a node may contain discrepancy, e.g., the labels of a node's neighbors could vary. This pushes us to consider the discrepancy of local structure in GCN modeling. Existing work approaches this issue by introducing an additional module such as graph attention, which is expected to learn the contribution of each neighbor. However, such module may not work reliably as expected, especially when there lacks supervision signal, e.g., when the labeled data is small. Moreover, existing Methods focus on modeling the nodes in the training data, and never consider the local structure discrepancy of testing nodes. This work focuses on the local structure discrepancy issue for testing nodes, which has received little scrutiny. From a novel perspective of causality, we investigate whether a GCN should trust the local structure of a testing node when predicting its label. To this end, we analyze the working mechanism of GCN with causal graph, estimating the causal effect of a node's local structure for the prediction. The idea is simple yet effective: given a trained GCN model, we first intervene the prediction by blocking the graph structure; we then compare the original prediction with the intervened prediction to assess the causal effect of the local structure on the prediction. Through this way, we can eliminate the impact of local structure discrepancy and make more accurate prediction. Extensive experiments on seven node classification datasets show that our Method effectively enhances the Inference stage of GCN.