Publications

Yi-Xiao He, Shen-Huan Lyu, Yuan Jiang (2024). Interpreting Deep Forest through Feature Contribution and MDI Feature Importance. In ACM Transactions on Knowledge Discovery from Data, in press.

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Shen-Huan Lyu, Yi-He Chen, Yuan Jiang (2023). Interaction Representations Based Deep Forest Method in Multi-Label Learning. In Journal of Software, in press.

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Qin-Cheng Zheng, Shen-Huan Lyu, Shao-Qun Zhang, Yuan Jiang, Zhi-Hua Zhou (2023). On the Consistency Rate of Decision Tree Learning Algorithms. In Proceedings of the 26th International Conference on Artificial Intelligence and Statistics (AISTATS), pp. 7824-7848, Valencia, ES.

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Shen-Huan Lyu, Yi-Xiao He, Zhi-Hua Zhou (2022). Depth is More Powerful than Width with Prediction Concatenation in Deep Forests. In Advances in Neural Information Processing Systems 35 (NeurIPS Oral), pp. 29719-29732, New Orleans, US.

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Shen-Huan Lyu, Yi-He Chen, Zhi-Hua Zhou (2022). A Region-Based Analysis for the Feature Concatenation in Deep Forests. In Chinese Journal of Electronics, 31(6):1072-1080.

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Shen-Huan Lyu, Lu Wang, Zhi-Hua Zhou (2022). Improving Generalization of Deep Neural Networks by Leveraging Margin Distributions. In Neural Networks, 151:48-60.

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Yi-He Chen, Shen-Huan Lyu, Yuan Jiang (2021). Improving Deep Forest by Exploiting High-Order Interactions. In Proceedings of the 21th IEEE International Conference on Data Mining (ICDM), pp. 1030-1035, Auckland, NZ.

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Shen-Huan Lyu, Liang Yang, Zhi-Hua Zhou (2019). A Refined Margin Distribution Analysis for Forest Representation Learning. In Advances in Neural Information Processing Systems 32 (NeurIPS), pp. 5531-5541, Vancouver, CA.

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