随笔分类 - Robust Learning
摘要:目录概符号说明AirGNN代码 Liu X., Ding J., Jin W., Xu H., Ma Y., Liu Z. and Tang J. Graph neural networks with adaptive residual. NIPS, 2021. 概 基于 UGNN 框架的一个更加鲁
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摘要:Wang L. and Joachims T. Uncertainty quantification for fairness in two-stage recommender systems. In International World Wide Web Conference (WWW), 20
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摘要:Duchi J. C. and Namkoong H. Learning models with uniform performance via distributionally robust optimization. The Annals of Statistics, 49(3), 1378-1
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摘要:Ren Y., Tang H. and Zhu S. Unbiased learning to rank with biased continuous feedback. In International Conference on Information and Knowledge Managem
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摘要:Liu S., Ying R., Dong H., Lin L., Chen J., Wu D. How powerful is implicit denoising in graph neural networks? arXiv preprint arXiv: 2209.14514, 2022.
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摘要:Zhu M., Wang X., Shi C., Ji H. and Cui P. Interpreting and unifying graph neural networks with an optimization framework. In International World Wide
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摘要:Zhao L. and Akoglu L. Connecting graph convolution and graph pca. 2022. 概 从 graph-regularized PCA 角度提出一种 GCN 的 message passing layer. 符号说明 ˜A
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摘要:Liu X., Jin W., Ma Y., Li Y., Li Y., Liu H., Wang Y., Yan M. and Tang J. Elastic graph neural networks. In International Conference on Machine Learnin
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摘要:Tan J., Geng S., Fu Z., Ge Y., Xu S., Li Y. and Zhang Y. Learning and evaluating graph neural network explanations based on counterfactual and factual
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摘要:Ying R., Bourgeois D., You J., Zitnik M. and Leskovec J. GNNExplainer: generating explanations for graph neural networks. In Advances in Neural Inform
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摘要:Dong Y., Liu N., Jalaian B. and Li J. EDITS: modeling and mitigating data bias for graph neural networks. In International World Wide Web Conference (
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摘要:Lahoti P., Beutel A., Chen J., Lee K., Prost F., Thain N., Wang X. and CHi E. H. Fairness without demographics through adversarially reweighted learni
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摘要:Dai E. and Wang S. Towards self-explainable graph neural network. In International Conference on Information and Knowledge Management (CIKM), 2021. 概
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摘要:Zhu D., Zhang Z., Cui P. and Zhu W. Robust graph convolutional networks against adversarial attacks. In ACM International Conference on Knowledge Disc
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摘要:Xu K., Chen H., Liu S., Chen P., Weng T., Hong M. and Lin X. Topology attack and defense for graph neural networks: an optimization perspective. In In
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摘要:Olatunji I. E., Funke T. and Khosla M. Releasing graph neural networks with differential privacy guarantees. In ACM Symposium on Neural Gaze Detection
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摘要:Papernot N., Abadi M., Erlingsson U., Goodfellow I. and Talwar K. Semi-supervised knowledge transfer for deep learning from private training data. In
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摘要:目录概符号说明MetricsSampled-based ranking例子Sampled metrics Krichene W. and Rendle S. On sampled metrics for item recommendation. KDD, 2020. 概 作者对推荐系统中 sampl
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摘要:Ding S., Wu P., Feng F., Wang Y., He X., Liao Y. and Zhang Y. Addressing unmeasured confounder for recommendation with sensitivity analysis. In ACM SI
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摘要:Chen J., Dong H., Qiu Y., He X., Xin X., Chen L., Lin G. and Yang K. AutoDebias: learning to debias for recommendation. In International ACM SIGIR Con
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