Minhao Cheng

I am an assistant professor of Computer Science & Engineering at HKUST. I obtained my Ph.D. degree in the Department of Computer Science from the University of California, Los Angeles under the supervision of Prof. Cho-Jui Hsieh. My research interest is broadly on machine learning with a focus on machine learning robustness and AutoML.

News

  • [August 2021] I’m looking for highly motivated students to join my lab in HKUST CSE. Please email me if you are interested.
  • [August 2021] I will join Department of Computer Science and Engineering at Hong Kong Unverisity of Science and Technology (HKUST) in Winter 2022.
  • [April 2021] Our paper on Rethinking Architecture Selection in Differentiable NAS won the outstanding paper award at ICLR 2021.
  • [March 2021] I have passed my PhD defense: On the Robustness of Neural Network: Attacks and Defenses

Education

  • Ph.D. in Computer Science, Univerisity of California, Los Angeles, 2021
  • B.Eng. in Computer Science, Univerisity of Electronic Science and Technology of China, 2015

Work experience

  • Summer 2020: Research Intern, Microsoft, Redmond, WA
  • Summer 2019: Research Intern, IBM Research, Yorktown Heights, NY
  • Summer 2017: Research Intern, Rakuten Slice, San Mateo, CA

Award

  • ICLR 2021 Outstanding Paper Award

Publications

* denote equal contribution

  • CAT: Customized Adversarial Training for Improved Robustness, Minhao Cheng, Qi Lei, Pin-Yu Chen, Inderjit Dhillon, Cho-Jui Hsieh, To appear in International Joint Conference on Artificial Intelligence (IJCAI), 2022. [pdf]

  • Concurrent Adversarial Learning for Large-Batch Training, Yong Liu, Xiangning Chen, Minhao Cheng, Cho-Jui Hsieh, Yang You, To appear in International Conference on Learning Representations (ICLR), 2022. [pdf]

  • A Review of Adversarial Attack and Defense for Classification Methods, Yao Li, Minhao Cheng, Cho-Jui Hsieh, Thomas Lee, In The American Statistician, 2021. [PDF]

  • RANK-NOSH: Efficient Predictor-Based NAS via Non-Uniform Successive Halving, Ruochen Wang, Xiangning Chen, Minhao Cheng, Xiaocheng Tang, Cho-Jui Hsieh, In International Conference on Computer Vision (ICCV), 2021. [PDF]

  • On the Robustness of Neural Network: Attacks and Defenses, Minhao Cheng, PhD Dissertation [PDF]

  • Rethinking Architecture Selection in Differentiable NAS, Ruochen Wang, Minhao Cheng, Xiangning Chen, Xiaocheng Tang, Cho-Jui Hsieh, In International Conference on Learning Representations (ICLR), 2021. (Outstanding Paper Award) [PDF] [code]

  • DrNAS: Dirichlet Neural Architecture Search, Xiangning Chen*, Ruochen Wang*, Minhao Cheng*, Xiaocheng Tang, Cho-Jui Hsieh, In International Conference on Learning Representations (ICLR), 2021. [PDF] [code]

  • Self-Progressing Robust Training, Minhao Cheng, Pin-Yu Chen, Sijia Liu, Shiyu Chang, Cho-Jui Hsieh, Payel Das, In AAAI Conference on Artificial Intelligence (AAAI), 2021. [PDF] [code]

  • Evaluating and enhancing the robustness of neural network-based dependency parsing models with adversarial examples, Xiaoqing Zheng, Jiehang Zeng, Yi Zhou, Cho-Jui Hsieh, Minhao Cheng, Xuanjing, In Proceedings of Association for Computational Linguistics (ACL), 2020. [PDF]

  • Sign-OPT: A Query-Efficient Hard-label Adversarial Attack, Minhao Cheng*, Simranjit Singh*, Patrick H. Chen, Pin-Yu Chen, Sijia Liu, Cho-Jui Hsieh, In International Conference on Learning Representations (ICLR), 2020. [PDF] [code]

  • Seq2Sick: Evaluating the Robustness of Sequence-to-Sequence Models with Adversarial Examples, Minhao Cheng, Jinfeng Yi, Pin-Yu Chen, Huan Zhang, Cho-Jui Hsieh, In AAAI Conference on Artificial Intelligence (AAAI), 2020. [PDF] [code]

  • On the Robustness of Self-Attentive Models, Yu-Lun Hsieh, Minhao Cheng, Da-Cheng Juan, Wei Wei, Wen-Lian Hsu, Cho-Jui Hsieh, In Proceedings of Association for Computational Linguistics (ACL), 2019. [PDF]

  • Evaluating and Enhancing the Robustness of Dialogue Systems: A Case Study on a Negotiation Agent, Minhao Cheng, Wei Wei, Cho-Jui Hsieh, In Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT), 2019. [PDF] [code]

  • Query-Efficient Hard-label Black-box Attack:An Optimization-based Approach, Minhao Cheng, Thong Le, Pin-Yu Chen, Jinfeng Yi, Huan Zhang, Cho-Jui Hsieh, In International Conference on Learning Representations (ICLR), 2019. [PDF] [code]

  • Fast Training for Large-Scale One-versus-All Linear Classifiers using Tree-Structured Initialization, Huang Fang, Minhao Cheng, Cho-Jui Hsieh, Michael Friedlander, In SIAM International Conference on Data Mining (SDM), 2019. [PDF]

  • Learning from Group Comparisons: Exploiting Higher Order Interactions, Yao Li, Minhao Cheng, Kevin Fujii, Fushing Hsieh, Cho-Jui Hsieh, In Advances in Neural Information Processing Systems (NeurIPS), 2018. [PDF]

  • Towards Robust Neural Networks via Random Self-ensemble, Xuanqing Liu, Minhao Cheng, Huan Zhang, Cho-Jui Hsieh, In European Conference on Computer Vision (ECCV), 2018. [PDF]

  • Distributed Primal-Dual Optimization for Non-uniformly Distributed Data, Minhao Cheng, Cho-Jui Hsieh, In International Joint Conference on Artificial Intelligence (IJCAI), 2018. [PDF]

  • Extreme Learning to Rank via Low Rank Assumption, Minhao Cheng, Ian Davidson, Cho-Jui Hsieh, In International Conference on Machine Learning (ICML), 2018. [PDF]

  • A Hyperplane-based Algorithm for Semi-supervised Dimension Reduction, Huang Fang, Minhao Cheng, Cho-Jui Hsieh, In IEEE International Conference on Data Mining (ICDM), 2017. [PDF]

Preprints

  • Voting based ensemble improves robustness of defensive models, Devvrit, Minhao Cheng, Cho-Jui Hsieh, Inderjit Dhillon, [arXiv]

  • Adversarial Masking: Towards Understanding Robustness Trade-off for Generalization, Minhao Cheng, Zhe Gan, Yu Cheng, Shuohang Wang, Cho-Jui Hsieh, Jingjing Liu, [Link]

  • Fake Node Attacks on Graph Convolutional Networks, Xiaoyun Wang, Minhao Cheng, Joe Eaton, Cho-Jui Hsieh, S.Felix Wu [arXiv]

  • Enhancing Certifiable Robustness via a Deep Model Ensemble, Huan Zhang, Minhao Cheng, Cho-Jui Hsieh [arXiv]

  • Stochastic Zeroth-order Optimization via Variance Reduction method, Liu Liu, Minhao Cheng, Cho-Jui Hsieh, Dacheng Tao [arXiv]