Minhao Cheng

PhD at UCLA CS

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About Me

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 focus 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
  • [Sept 2020] I am actively looking for jobs both on industry and academia.

Education

  • Univerisity of California, Los Angeles
    PhD in Computer Science 2018 - 2021

  • Univerisity of California, Davis
    PhD in Computer Science 2015 - 2018 (transferred)

  • Univerisity of Electronic Science and Technology of China
    BEng in Computer Science 2011 - 2015

Publications

* denote equal contribution

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]

CAT: Customized Adversarial Training for Improved Robustness

Minhao Cheng, Qi Lei, Pin-Yu Chen, Inderjit Dhillon, Cho-Jui Hsieh

[arXiv]

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]

Awards & Honors

  • ICLR 2021 Outstanding Paper Award