Shen-Huan Lyu

Orcid: 0000-0002-0173-8408

According to our database1, Shen-Huan Lyu authored at least 11 papers between 2019 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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PhD thesis 
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Links

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Bibliography

2024
Multi-class imbalance problem: A multi-objective solution.
Inf. Sci., 2024

Identifying Key Tag Distribution in Large-Scale RFID Systems.
Proceedings of the 32nd IEEE/ACM International Symposium on Quality of Service, 2024

Confidence-aware Contrastive Learning for Selective Classification.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

Mask-Encoded Sparsification: Mitigating Biased Gradients in Communication-Efficient Split Learning.
Proceedings of the ECAI 2024 - 27th European Conference on Artificial Intelligence, 19-24 October 2024, Santiago de Compostela, Spain, 2024

The Role of Depth, Width, and Tree Size in Expressiveness of Deep Forest.
Proceedings of the ECAI 2024 - 27th European Conference on Artificial Intelligence, 19-24 October 2024, Santiago de Compostela, Spain, 2024

2023
Interpreting Deep Forest through Feature Contribution and MDI Feature Importance.
CoRR, 2023

On the Consistency Rate of Decision Tree Learning Algorithms.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022
Improving generalization of deep neural networks by leveraging margin distribution.
Neural Networks, 2022

Depth is More Powerful than Width with Prediction Concatenation in Deep Forest.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021
Improving Deep Forest by Exploiting High-order Interactions.
Proceedings of the IEEE International Conference on Data Mining, 2021

2019
A Refined Margin Distribution Analysis for Forest Representation Learning.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019


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