10. 因果 Debiasing Graph Neural Networks via Learning Disentangled Causal Substructure CLEAR: Generative Counterfactual Explanations on Graphs Counterfactual Fairness with Partially Known Causal Graph Large-Scale Differentiable Causal ...
[3] Kevin Xia, Kai-Zhan Lee, Yoshua Bengio, Elias Bareinboim. The Causal-Neural Connection: Expressiveness, Learnability, and inference 2021 [4] Xun Zheng, Bryon Aragam, Pradeep Ravikumar, Eric P Xing. Dags with no tears: continuous optimization for structure learning 2018...
Causal inference in statistics. Judea Pearl 因果推断学习分享 6556播放 【白嫖】3h学完一遍同济线代,档次极高!(完结撒花~) 20.9万播放 【因果推断入门】第1季第2集 辛普森悖论 上 Simpson's Paradox【Introduction to Causal Inference】 3.8万播放 24考研数学 睡前系列【基础篇】第1题|含变限函数的极限计算...
The first stage concentrates on the generation of a causal graph between BP and wearable features from the the perspective of causal inference, so as to identify features that are causally related to BP variations. This stage is pivotal for the identification of novel causal features from the ...
2020 年发表的「Amortised Causal Discovery」是该领域的一个颇有影响力的例子[19,20],它使用了神经关系推理技术根据时间序列数据来推理因果图。该领域的其它重要工作还包括:具有可学习指针的 GNN[21,15]、具有关系机制的GNN[22,23]、通过自适应的计算图学习基于网格的物理仿真器[24]、学习推理执行计算的抽象节点的...
CIDER: Counterfactual-Invariant Diffusion-based GNN Explainer for Causal Subgraph Inference CIDER：用于...
CausalGNN: Causal-Based Graph Neural Networks for Spatio-Temporal Epidemic Forecasting. AAAI 2022. paper LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networks. AAAI 2022. paper DDGCN: Dual Dynamic Graph Convolutional Networks for Rumor Detection on Social Media. AAAI 2022. p...
Generative Causal Explanations for Graph Neural Networks. ICML 2021. paper Wanyu Lin, Hao Lan, Baochun Li. GraphSVX: Shapley Value Explanations for Graph Neural Networks. ECML PKDD 2021. paper Alexandre Duval, Fragkiskos D. Malliaros. Applications Physics Discovering objects and their relations from...
代码链接:https://github.com/VITA-Group/Unified-LTH-GNN Highlight: To this end, this paper first presents a unified GNNsparsification(UGS) framework that simultaneously prunes the graph adjacency matrix and the model weights, for effectively accelerating GNN inference on large-scale graphs. Leveraging...
Understanding Non-linearity in Graph Neural Networks from the Bayesian-Inference Perspective Understanding and Extending Subgraph GNNs by Rethinking Their Symmetries A Practical, Progressively-Expressive GNN 泛化性分析 Generalization Analysis of Message Passing Neural Networks on Large Random Graphs ...