Yaochen Hu's Home Page

Everything with form is unreal.

Email: yaochen dot hu at huawei dot com

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I am a researcher with Huawei Noah's Ark Lab Montreal Research Center. I received my bachelor's degree from the Department of Electrical and Information Engineering at Huazhong University of Science and Technology and my Ph.D. degree from the Department of Electrical and Computing Engineering at the University of Alberta.

I always admire the insightful science or art achievements that result from the inspiration and perspiration of people who have a relentless pursuit of truth and beauty.

I am interested in a wide area of topics, from applicational scenarios to fundamental theoretical problems. Specifically, from the application side, I am interested in realistic large-scale recommender systems, distributed systems and parallel computation. I have also worked on the load balancing problem for distributed erasure-coded systems. From the theoretical side, I am interested in the methodologies related to computer science, including optimization, approximation algorithms, etc. Recently, I started to explore the application of Bayesian analysis. In the era of machine learning, I am actively working on projects related to graph neural networks.

Selected Publications

  1. *Kumar, Ishaan, *Yaochen Hu, and Yingxue Zhang. "EFLEC: Efficient Feature-LEakage Correction in GNN based Recommendation Systems." In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1885-1889. 2022. [paper] [slides] [poster]
  2. Hu, Yaochen, Peng Liu, Linglong Kong, and Di Niu. "Learning privately over distributed features: An ADMM sharing approach." arXiv preprint arXiv:1907.07735 (2019). [paper] [code]
  3. Hu, Yaochen, Di Niu, Jianming Yang and Shengping Zhou. "FDML: A collaborative Machine Learning Framework for Distributed Features," in Proceedings of the 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'19), August 4-8, 2019, Anchorage, AK, USA. [paper] [slides] [poster] [codes]
  4. Hu, Yaochen, Di Niu, and Jianming Yang. "A Fast Linear Computational Framework for User Action Prediction in Tencent MyApp," in Proceedings of the 27th ACM International Conference on Information and Knowledge.
  5. Hu, Yaochen, Yushi Wang, Bang Liu, D Niu, and Cheng Huang. "Latency reduction and load balancing in coded storage systems," in Proceedings of the 2017 Symposium on Cloud Computing. ACM, 2017. [paper]
  6. Hu, Yaochen, and Di Niu. "Reducing access latency in erasure coded cloud storage with local block migration," in Proceedings of INFOCOM 2016-The 35th Annual IEEE International Conference on Computer Communications, IEEE. IEEE, 2016. [paper] [slides][best in session presentation]

Full Publication List

*denotes equal contribution.

  1. *Kumar, Ishaan, *Yaochen Hu, and Yingxue Zhang. "EFLEC: Efficient Feature-LEakage Correction in GNN based Recommendation Systems." In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1885-1889. 2022. [paper] [slides] [poster]
  2. Li, Yunhe, Yaochen Hu, and Yingxue Zhang. "Dual Path Graph Convolutional Networks." In ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 5563-5567. IEEE, 2022. [paper]
  3. Li, Yunhe, Yaochen Hu, and Yingxue Zhang. "Graph representation learning via adversarial variational bayes." Proceedings of the 30th ACM International Conference on Information & Knowledge Management. 2021. [paper]
  4. Guo, Wei, Yang Yang, Yaochen Hu, Chuyuan Wang, Huifeng Guo, Yingxue Zhang, Ruiming Tang, Weinan Zhang, and Xiuqiang He. "Deep graph convolutional networks with hybrid normalization for accurate and diverse recommendation," in Proceedings of 3rd Workshop on Deep Learning Practice for High-Dimensional Sparse Data with KDD. 2021. [paper]
  5. Sun, Jianing, Wei Guo, Dengcheng Zhang, Yingxue Zhang, Florence Regol, Yaochen Hu, Huifeng Guo et al. "A framework for recommending accurate and diverse items using bayesian graph convolutional neural networks." In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2030-2039. 2020. [paper]
  6. Hu, Yaochen, Peng Liu, Linglong Kong, and Di Niu. "Learning privately over distributed features: An ADMM sharing approach." arXiv preprint arXiv:1907.07735 (2019). [paper] [code]
  7. Hu, Yaochen, Di Niu, Jianming Yang and Shengping Zhou. "FDML: A collaborative Machine Learning Framework for Distributed Features," in Proceedings of the 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'19), August 4-8, 2019, Anchorage, AK, USA. [paper] [slides] [poster] [codes]
  8. Hu, Yaochen, Di Niu, and Jianming Yang. "A Fast Linear Computational Framework for User Action Prediction in Tencent MyApp," in Proceedings of the 27th ACM International Conference on Information and Knowledge Management. ACM, 2018. [paper]
  9. Hu, Yaochen, Yushi Wang, Bang Liu, D Niu, and Cheng Huang. "Latency reduction and load balancing in coded storage systems," in Proceedings of the 2017 Symposium on Cloud Computing. ACM, 2017. [paper]
  10. Hu, Yaochen, Di Niu, and Zongpeng Li. "A geometric approach to server selection for interactive video streaming," IEEE Transactions on Multimedia 18.5 (2016): 840-851. [paper]
  11. Hu, Yaochen, and Di Niu. "Reducing access latency in erasure coded cloud storage with local block migration," in Proceedings of INFOCOM 2016-The 35th Annual IEEE International Conference on Computer Communications, IEEE. IEEE, 2016. [paper] [slides][best in session presentation]
  12. Zhang, Shuopeng, Di Niu, Yaochen Hu, Fangming Liu. "Server selection and topology control for multi-party video conferences," in Proceedings of Network and Operating System Support on Digital Audio and Video Workshop. ACM, 2014. [paper]
  13. Yaochen Hu, Di Niu, Zongpeng Li. "Internet Video Multicast via Constrained Space Information Flow," in IEEE MMTC E-letter, 9(3), April 2014. [paper]

Misc.

  1. Five in Row I developed a python-based board game, five in a row, equipped with the alpha-beta search algorithm. To evaluate the board value, I invented a simple hashing algorithm that convolves the connected pieces in each line segment of 5 points in horizontal, vertical and diagonal directions. The algorithm is so strong that it can beat me (I believe I should be at an intermediate to advanced level in this game).

Research Skills

Last update: October 2022