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GINT: A Generative Interpretability method via perturbation in the latent space.

C. Tang, Q. Cui, L. Li, and J. Zhou. Expert Syst. Appl., (December 2023)

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Tang Tang

Fei Tang

Junhua Tang

Jian Tang

Yubo Tang

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DGBCT: A Scalable Distributed Gradient Boosting Causal Tree at Alipay.J. Zhou, C. Tang, Q. Cui, Y. Ding, L. Li, and F. Wu. WWW (Companion Volume), page 447-451. ACM, (2023)Difference-in-Differences Meets Tree-based Methods: Heterogeneous Treatment Effects Estimation with Unmeasured Confounding.C. Tang, H. Wang, X. Li, Q. Cui, L. Li, and J. Zhou. ICML, volume 202 of Proceedings of Machine Learning Research, page 33792-33803. PMLR, (2023)GINT: A Generative Interpretability method via perturbation in the latent space.C. Tang, Q. Cui, L. Li, and J. Zhou. Expert Syst. Appl., (December 2023)AutoRec: A Comprehensive Platform for Building Effective and Explainable Recommender Models.Q. Cui, Q. Shi, H. Qian, C. Tang, X. Li, Y. Zhao, T. Jiang, L. Li, and J. Zhou. ECML/PKDD (5), volume 12461 of Lecture Notes in Computer Science, page 541-545. Springer, (2020)Alleviating Matching Bias in Marketing Recommendations.J. Fang, Q. Cui, G. Zhang, C. Tang, L. Gu, L. Li, J. Gu, J. Zhou, and F. Wu. SIGIR, page 3359-3363. ACM, (2023)Invariant Graph Learning for Causal Effect Estimation.Y. Sui, C. Tang, Z. Chu, J. Fang, Y. Gao, Q. Cui, L. Li, J. Zhou, and X. Wang. WWW, page 2552-2562. ACM, (2024)Backdoor Adjustment via Group Adaptation for Debiased Coupon Recommendations.J. Fang, G. Zhang, Q. Cui, C. Tang, L. Gu, L. Li, J. Gu, and J. Zhou. AAAI, page 11944-11952. AAAI Press, (2024)Semi-Supervised Learning with Data Augmentation for Tabular Data.J. Fang, C. Tang, Q. Cui, F. Zhu, L. Li, J. Zhou, and W. Zhu. CIKM, page 3928-3932. ACM, (2022)Debiased Causal Tree: Heterogeneous Treatment Effects Estimation with Unmeasured Confounding.C. Tang, H. Wang, X. Li, Q. Cui, Y. Zhang, F. Zhu, L. Li, J. Zhou, and L. Jiang. NeurIPS, (2022)Domain Level Interpretability: Interpreting Black-box Model with Domain-specific Embedding.Y. Zhang, C. Tang, L. Yu, J. Zhou, L. Li, Q. Cui, F. Fan, L. Jiang, and X. Zhao. WSDM, page 1102-1105. ACM, (2024)

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Disambiguation of "Tang, Caizhi"

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GINT: A Generative Interpretability method via perturbation in the latent space.

Please choose a person to relate this publication to

Tang Tang

Fei Tang

Junhua Tang

Jian Tang

Yubo Tang

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Tang, Caizhi"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML GINT: A Generative Interpretability method via perturbation in the latent space.

Please choose a person to relate this publication to

Tang Tang

Fei Tang

Junhua Tang

Jian Tang

Yubo Tang

Other publications of authors with the same name

Disambiguation

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GINT: A Generative Interpretability method via perturbation in the latent space.