About
Hello, Yuhao here! Starting September 2023, I join SRI lab at ETH Zürich as a PhD student. I got my MSc degree in Computer Science majoring Machine Intelligence and minoring Theoretical Computer Science at ETH Zürich, and my BSc degree in Mathematics and Applied Mathematics with a dual degree in Finance at CKC Honors College, Zhejiang University. I am lucky to receive supervision and/or advice from:
- Dr. G. Zhou, who leads me to the fantastic world of macroeconomics and encouraged me during my early stage of research, which benefits me deeply even after I left the field;
- Dr. S. Ji and Dr. X. Zhang, who showed me the excitement and challenges of trustworthy artificial intelligence and offered valuable guidance and suggestions for years;
- Dr. Y. Zhang, who has always been kind and helpful, especially when I feel lost;
- Dr. F. Yang, who welcomed me since my first semester in a foreign country and continues to help;
- Dr. M. Vechev, who shares numerous good days and continues to be a good friend.
My favorite formula includes:
\[e^{\pi i}=-1\] \[\hat{f}(\xi) = \int_{-\infty}^{\infty}f(x)e^{2\pi i x\xi}dx\]Find my CV here.
Feel free to contact me if you have any questions or interests.
Working Ethics
I solemnly swear that I put 120% effort into making the proofs in all my publications, including those I am not the (co-)first author, correct, rigorous and readable.
Publication
All publications are peer-reviewed in top-tier conferences/journals. Equal contributions are marked by *.
Trustworthy Artificial Intelligence
- Yuhao Mao*, Yani Zhang*, Martin Vechev, Multi-Neuron Unleashes Expressivity of ReLU Networks Under Convex Relaxation, preprint. We have identified problems in the proof of Lemma 3 and are working on a more rigorous proof.
- Chenhao Sun*, Yuhao Mao*, Mark Niklas Müller, Martin Vechev, Average Certified Radius is a Poor Metric for Randomized Smoothing, preprint.
- Yuhao Mao, Stefan Balauca, Martin Vechev, CTBENCH: A Library and Benchmark for Certified Training, preprint.
- Stefan Balauca, Mark Niklas Müller, Yuhao Mao, Maximilian Baader, Marc Fischer, Martin Vechev, Gaussian Loss Smoothing Enables Certified Training with Tight Convex Relaxations, preprint.
- Yuhao Mao, Mark Niklas Müller, Marc Fischer, Martin Vechev, Understanding Certified Training with Interval Bound Propagation, The Twelfth International Conference on Learning Representations (ICLR’24).
- Maximilian Baader*, Mark Niklas Müller*, Yuhao Mao, Martin Vechev, Expressivity of ReLU-Networks under Convex Relaxations, The Twelfth International Conference on Learning Representations (ICLR’24).
- Yuhao Mao, Mark Niklas Müller, Marc Fischer, Martin Vechev, Connecting Certified and Adversarial Training, The Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS’23).
- Yuyou Gan*, Yuhao Mao*, Xuhong Zhang, Shouling Ji, Yuwen Pu, Meng Han, Jianwei Yin, Ting Wang, ``Is your explanation stable?’’: A Robustness Evaluation Framework for Feature Attribution, ACM SIGSAC Conference on Computer and Communications Security 2022 (CCS’22).
- Yuhao Mao, Chong Fu, Saizhuo Wang, Shouling Ji, Xuhong Zhang, Zhenguang Liu, Jun Zhou, Alex X. Liu, Raheem Beyah, Ting Wang, Transfer Attack Revisited: A Large-Scale Empirical Study in Real Computer Vision Settings, IEEE Symposium on Security & Privacy 2022 (SP’22).
Artificial Intelligence for Science
- Chenhao Chu, Yuhao Mao, Hua Wang, Transfer Learning Assisted Fast Design Migration Over Technology Nodes: A Study on Transformer Matching Network, IEEE MTT-S International Microwave Symposium 2024 (IMS’24).
Talk
- Training Certifiably Robust Neural Networks. January 2024 at Zhejiang University, China. [Slide]
Teaching
- Reliable and Trustworthy Artificial Intelligence (Master), Fall 2024, ETH Zürich, Teaching Assistant.
- Rigorous Software Engineering (Bachelor), Spring 2024, ETH Zürich, Teaching Assistant.
- Reliable and Trustworthy Artificial Intelligence (Master), Fall 2023, ETH Zürich, Teaching Assistant.
- Computational Intelligence Lab (Master), Spring 2023, ETH Zürich, Teaching Assistant.
- Introduction to Machine Learning (Bachelor), Spring 2023, ETH Zürich, Teaching Assistant.
Community Contribution
Review for NeurIPS’24 (top reviewer award), ICLR’25.