Towards Open-World Feature Extrapolation: An Inductive Graph Learning Approach
Authors: Qitian Wu, Chenxiao Yang, Junchi Yan
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments over several classification datasets and large-scale advertisement click prediction datasets demonstrate that our model can produce effective embeddings for unseen features and significantly outperforms baseline methods that adopt KNN and local aggregation. |
| Researcher Affiliation | Academia | Qitian Wu, Chenxiao Yang, Junchi Yan Department of Computer Science and Engineering Mo E Key Lab of Artificial Intelligence, AI Institute Shanghai Jiao Tong University {echo740, chr26195, yanjunchi}@sjtu.edu.cn |
| Pseudocode | Yes | We present the complete training algorithm in Appendix B where the model is trained end-to-end using self-supervised or inductive learning approaches. |
| Open Source Code | Yes | The implementation codes are public available at https://github.com/qitianwu/FATE. |
| Open Datasets | Yes | First, we consider six classification datasets from UCI Machine Learning Repository [1]: Gene, Protein, Robot, Drive, Calls and Github, as collected from domains like biology, engineering and social domains with diverse features, as well as two large-scale datasets Avazu and Criteo from real-world online advertisement system whose goal is to predict the Click-Through Rate (CTR) of exposed advertisement to users. |
| Dataset Splits | Yes | We randomly split all the instances into training/validation/test data with the ratio 6:2:2. |
| Hardware Specification | No | The paper does not provide specific hardware details (such as exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions that the model is implemented in Python and PyTorch, but does not provide specific version numbers for these or any other ancillary software components. |
| Experiment Setup | Yes | We specify FATE in the following ways. 1) Backbone: a 3-layer feedforward NN. 2) GNN: a 4-layer GCN. 3) Training: self-supervised learning with n-fold splits. |