Skip to main content

    Preprint#

  1. Yingjie Fu, Bozhou Li, Linyi Li, Wentao Zhang, Tao Xie
    The First Prompt Counts the Most! An Evaluation of Large Language Models on Iterative Example-based Code Generation
    arXiv
    [Full Version]  
    @article{fu2024first,
    title={The First Prompt Counts the Most! An Evaluation of Large Language Models on Iterative Example-based Code Generation},
    author={Fu, Yingjie and Li, Bozhou and Li, Linyi and Zhang, Wentao and Xie, Tao},
    journal={arXiv preprint arXiv:2411.06774},
    year={2024}
    }

    Topic: LLM prompting benchmark code

  2. Yiming Zhang, Baoyi He, Shengyu Zhang, Yuhao Fu, Qi Zhou, Zhijie Sang, Zijin Hong, Kejing Yang, Wenjun Wang, Jianbo Yuan, Guanghan Ning, Linyi Li, Chunlin Ji, Fei Wu, Hongxia Yang
    Unconstrained Model Merging for Enhanced LLM Reasoning
    arXiv
    [Full Version]  
    @article{zhang2024unconstrained,
    title={Unconstrained Model Merging for Enhanced LLM Reasoning},
    author={Zhang, Yiming and He, Baoyi and Zhang, Shengyu and Fu, Yuhao and Zhou, Qi and Sang, Zhijie and Hong, Zijin and Yang, Kejing and Wang, Wenjun and Yuan, Jianbo and others},
    journal={arXiv preprint arXiv:2410.13699},
    year={2024}
    }

    Topic: LLM merging reasoning

  3. Jingxuan Xu, Wuyang Chen, Linyi Li, Yao Zhao, Yunchao Wei
    Collapsed Language Models Promote Fairness
    arXiv
    [Full Version]  
    @article{xu2024collapsed,
    title={Collapsed Language Models Promote Fairness},
    author={Xu, Jingxuan and Chen, Wuyang and Li, Linyi and Zhao, Yao and Wei, Yunchao},
    journal={arXiv preprint arXiv:2410.04472},
    year={2024}
    }

    Topic: LLM fairness

    4. 2025#

  4. Linyi Li
    Certified Trustworthiness in the Era of Large Language Models
    The 39th Annual AAAI Conference on Artificial Intelligence (AAAI 2025) New Faculty Highlights
    [Talk Abstract in Preparation]  
    @inproceedings{
    li2025certified,
    title={Certified Trustworthiness in the Era of Large Language Models},
    author={Linyi Li},
    booktitle={The Thirty-ninth Annual AAAI Conference on Artificial Intelligence New Faculty Highlights},
    year={2025},
    }

    Topic: certified ML

  5. Zijian Huang, Wenda Chu, Linyi Li, Chejian Xu, Bo Li
    COMMIT: Certifying Robustness of Multi-Sensor Fusion Systems against Semantic Attacks
    The 39th Annual AAAI Conference on Artificial Intelligence (AAAI 2025)
    [Full Version in Preparation]  
    @inproceedings{
    huang2025commit,
    title={COMMIT: Certifying Robustness of Multi-Sensor Fusion Systems against Semantic Attacks},
    author={Zijian Huang and Wenda Chu and Linyi Li and Chejian Xu and Bo Li},
    booktitle={The Thirty-ninth Annual AAAI Conference on Artificial Intelligence},
    year={2025},
    }

    Topic: certified ML

  6. Zhangheng Li, Tianlong Chen, Linyi Li, Bo Li, Zhangyang Wang
    Sparse Transfer Learning Accelerates and Enhances Certified Robustness: A Comprehensive Study
    The 39th Annual AAAI Conference on Artificial Intelligence (AAAI 2025)
    [Full Version in Preparation]  
    @inproceedings{
    li2025sparse,
    title={Sparse Transfer Learning Accelerates and Enhances Certified Robustness: A Comprehensive Study},
    author={Zhangheng Li and Tianlong Chen and Linyi Li and Bo Li and Zhangyang Wang},
    booktitle={The Thirty-ninth Annual AAAI Conference on Artificial Intelligence},
    year={2025},
    }

    7. 2024#

  7. Linyi Li, Shijie Geng, Zhenwen Li, Yibo He, Hao Yu, Ziyue Hua, Guanghan Ning, Siwei Wang, Tao Xie, Hongxia Yang
    InfiBench: Evaluating the Question-Answering Capabilities of Code Large Language Models
    38th Conference on Neural Information Processing Systems Datasets and Benchmarks Track (NeurIPS 2024 D&B)
    [Full Version]   [Conference Version]   [Code]   [Project Website]   [Slides]  
    @inproceedings{
    li2024infibench,
    title={InfiBench: Evaluating the Question-Answering Capabilities of Code Large Language Models},
    author={Linyi Li and Shijie Geng and Zhenwen Li and Yibo He and Hao Yu and Ziyue Hua and Guanghan Ning and Siwei Wang and Tao Xie and Hongxia Yang},
    booktitle={The Thirty-eight Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
    year={2024},
    }

    Topic: LLM benchmark code

  8. Youwei Shu, Xi Xiao, Derui Wang, Yuxin Cao, Siji Chen, Jason Xue, Linyi Li, Bo Li
    Effects of Exponential Gaussian Distribution on (Double Sampling) Randomized Smoothing
    41st International Conference on Machine Learning (ICML 2024)
    [Full Version]   [Conference Version]   [Code]  
    @inproceedings{shu2024effects,
    title={Effects of Exponential Gaussian Distribution on (Double Sampling) Randomized Smoothing},
    author={Shu, Youwei and Xiao, Xi and Wang, Derui and Cao, Yuxin and Chen, Siji and Xue, Jason and Li, Linyi and Li, Bo},
    booktitle={Forty-first International Conference on Machine Learning},
    year={2024}
    }

    Topic: certified ML

  9. Mintong Kang, Nezihe Merve Gürel, Linyi Li, Bo Li.
    COLEP: Certifiably Robust Learning-Reasoning Conformal Prediction via Probabilistic Circuits
    12th International Conference on Learning Representations (ICLR 2024)
    [Full Version]   [Conference Version]   [Code]  
    @inproceedings{
    kang2024colep,
    title={{COLEP}: Certifiably Robust Learning-Reasoning Conformal Prediction via Probabilistic Circuits},
    author={Mintong Kang and Nezihe Merve G{"u}rel and Linyi Li and Bo Li},
    booktitle={The Twelfth International Conference on Learning Representations},
    year={2024},
    url={https://openreview.net/forum?id=XN6ZPINdSg}
    }

    Topic: certified ML reasoning

  10. Hanjiang Hu, Zuxin Liu, Linyi Li, Jiacheng Zhu, Ding Zhao
    Pixel-wise Smoothing for Certified Robustness against Camera Motion Perturbations
    27th International Conference on Artificial Intelligence and Statistics (AISTATS 2024)
    [Full Version]   [Conference Version]   [Code]  
    @inproceedings{hu2024pixel,
    title={Pixel-wise Smoothing for Certified Robustness against Camera Motion Perturbations},
    author={Hu, Hanjiang and Liu, Zuxin and Li, Linyi and Zhu, Jiacheng and Zhao, Ding},
    booktitle={International Conference on Artificial Intelligence and Statistics},
    pages={217--225},
    year={2024},
    organization={PMLR}
    }

    Topic: certified ML

    11. 2023#

  11. Linyi Li
    Certifiably Trustworthy Deep Learning Systems at Scale
    Doctoral Thesis
    [Full Version]   [Official Version]  
    @phdthesis{li2023thesis,
    title = {Certifiably Trustworthy Deep Learning Systems at Scale},
    author = {Linyi Li},
    year = 2023,
    month = {Oct},
    school = {University of Illinois Urbana-Champaign},
    type = {PhD thesis}
    }

    Topic: certified ML

  12. Zhangheng Li, Tianlong Chen, Linyi Li, Bo Li, Zhangyang Wang
    Can Pruning Improve Certified Robustness of Neural Networks?
    Transactions on Machine Learning Research (TMLR), 2023
    [Full Version]  
    @article{
    li2023can,
    title={Can Pruning Improve Certified Robustness of Neural Networks?},
    author={Zhangheng LI and Tianlong Chen and Linyi Li and Bo Li and Zhangyang Wang},
    journal={Transactions on Machine Learning Research},
    issn={2835-8856},
    year={2023},
    url={https://openreview.net/forum?id=6IFi2soduD},
    }

    Topic: certified ML pruning

  13. Linyi Li, Tao Xie, Bo Li
    SoK: Certified Robustness for Deep Neural Networks
    44th IEEE Symposium on Security and Privacy (SP 2023)
    [Full Version]   [Conference Version]   [Slides]   [Code]   [Leaderboard]  
    @inproceedings{li2023sok,
    author={Linyi Li and Tao Xie and Bo Li},
    title = {SoK: Certified Robustness for Deep Neural Networks},
    booktitle = {44th {IEEE} Symposium on Security and Privacy, {SP} 2023, San Francisco, CA, USA, 22-26 May 2023},
    publisher = {{IEEE}},
    year = {2023},
    }

    Topic: certified ML

    Summary: A comprehensive systemization of knowledge on DNN certified robustness, including discussion on practical and theoretical implications, findings, main challenges, and future directions, accompanied with an open-source unified platform to evaluate 20+ representative approaches.

  14. Linyi Li, Yuhao Zhang, Luyao Ren, Yingfei Xiong, Tao Xie
    Reliability Assurance for Deep Neural Network Architectures Against Numerical Defects
    45th IEEE/ACM International Conference on Software Engineering (ICSE 2023)
    [Full Version]   [Conference Version]   [Slides]   [Code]  
    @inproceedings{li2023reliability,
    author={Linyi Li and Yuhao Zhang and Luyao Ren and Yingfei Xiong and Tao Xie},
    title = {Reliability Assurance for Deep Neural Network Architectures Against Numerical Defects},
    booktitle = {45th International Conference on Software Engineering, {ICSE} 2023, Melbourne, Australia, 14-20 May 2023},
    publisher = {{IEEE/ACM}},
    year = {2023},
    }

    Topic: certified ML numerical reliability

    Summary: An effective and efficient white-box framework for generic DNN architectures, named RANUM, for certifying numerical reliability (e.g., not output NaN or INF), generating failure-exhibiting system tests, and suggesting fixes, where RANUM is the first automated framework for the last two tasks.

  15. Jiawei Zhang, Linyi Li, Ce Zhang, Bo Li
    CARE: Certifiably Robust Learning with Reasoning via Variational Inference
    First IEEE Conference on Secure and Trustworthy Machine Learning (SatML 2023)
    [Full Version]   [Conference Version]  
    @inproceedings{
    zhang2023care,
    title={{CARE}: Certifiably Robust Learning with Reasoning via Variational Inference},
    author={Jiawei Zhang and Linyi Li and Ce Zhang and Bo Li},
    booktitle={First IEEE Conference on Secure and Trustworthy Machine Learning},
    year={2023},
    url={https://openreview.net/forum?id=1n6oWTTV1n}
    }

    Topic: certified ML reasoning

  16. Mintong Kang, Linyi Li, Bo Li
    FaShapley: Fast and Approximated Shapley Based Model Pruning Towards Certifiably Robust DNNs
    First IEEE Conference on Secure and Trustworthy Machine Learning (SatML 2023)
    [Conference Version]  
    @inproceedings{
    kang2023fashapley,
    title={FaShapley: Fast and Approximated Shapley Based Model Pruning Towards Certifiably Robust {DNN}s},
    author={Mintong Kang and Linyi Li and Bo Li},
    booktitle={First IEEE Conference on Secure and Trustworthy Machine Learning},
    year={2023},
    url={https://openreview.net/forum?id=mJF9_Fs52ut}
    }

    Topic: certified ML pruning

    17. 2022#

  17. Mintong Kang*, Linyi Li*, Maurice Weber, Yang Liu, Ce Zhang, Bo Li
    Certifying Some Distributional Fairness with Subpopulation Decomposition
    Advances in Neural Information Processing Systems (NeurIPS) 2022
    [Full Version]   [Conference Version]   [Code]   [Poster]  
    @inproceedings{kang2022certifying,
    title = {Certifying Some Distributional Fairness with Subpopulation Decomposition},
    author = {Mintong Kang and Linyi Li and Maurice Weber and Yang Liu and Ce Zhang and Bo Li},
    booktitle = {Advances in Neural Information Processing Systems 35 (NeurIPS 2022)},
    year = {2022}
    }

    Topic: certified ML fairness

    Summary: A practical and scalable certification approach to provide fairness bound for a given model when distribution shifts from training, based on subpopulation decomposition.

  18. Xiaojun Xu, Linyi Li, Bo Li
    LOT: Layer-wise Orthogonal Training on Improving \(\ell_2\) Certified Robustness
    Advances in Neural Information Processing Systems (NeurIPS) 2022
    [Full Version]   [Conference Version]   [Code]  
    @inproceedings{xu2022lot,
    title = {LOT: Layer-wise Orthogonal Training on Improving l2 Certified Robustness},
    author = {Xiaojun Xu and Linyi Li and Bo Li},
    booktitle = {Advances in Neural Information Processing Systems 35 (NeurIPS 2022)},
    year = {2022}
    }

    Topic: certified ML

  19. Bhaskar Ray Chaudhury, Linyi Li, Mintong Kang, Bo Li, Ruta Mehta
    Fairness in Federated Learning via Core-Stability
    Advances in Neural Information Processing Systems (NeurIPS) 2022
    [Full Version]   [Conference Version]   [Code]   [Poster]  
    @inproceedings{bhaskar2022fairness,
    title = {Fairness in Federated Learning via Core-Stability},
    author = {Bhaskar Ray Chaudhury and Linyi Li and Mintong Kang and Bo Li and Ruta Mehta},
    booktitle = {Advances in Neural Information Processing Systems 35 (NeurIPS 2022)},
    year = {2022}
    }

    Topic: fairness

  20. Huan Zhang*, Shiqi Wang*, Kaidi Xu*, Linyi Li, Bo Li, Suman Jana, Cho-Jui Hsieh, J. Zico Kolter
    General Cutting Planes for Bound-Propagation-Based Neural Network Verification
    Advances in Neural Information Processing Systems (NeurIPS) 2022
    [Full Version]   [Conference Version]   [Code]   [Poster]  
    @inproceedings{zhang2022general,
    title = {General Cutting Planes for Bound-Propagation-Based Neural Network Verification},
    author = {Huan Zhang and Shiqi Wang and Kaidi Xu and Linyi Li and Bo Li and Suman Jana and Cho-Jui Hsieh and J. Zico Kolter},
    booktitle = {Advances in Neural Information Processing Systems 35 (NeurIPS 2022)},
    year = {2022}
    }

    Topic: certified ML

  21. Zhuolin Yang*, Zhikuan Zhao*, Boxin Wang, Jiawei Zhang, Linyi Li, Hengzhi Pei, Bojan Karlaš, Ji Liu, Heng Guo, Ce Zhang, Bo Li
    Improving Certified Robustness via Statistical Learning with Logical Reasoning
    Advances in Neural Information Processing Systems (NeurIPS) 2022
    [Full Version]   [Conference Version]   [Code]  
    @inproceedings{yang2022improving,
    title = {Improving Certified Robustness via Statistical Learning with Logical Reasoning},
    author = {Zhuolin Yang and Zhikuan Zhao and Boxin Wang and Jiawei Zhang and Linyi Li and Hengzhi Pei and Bojan Karlaš and Ji Liu and Heng Guo and Ce Zhang and Bo Li},
    booktitle = {Advances in Neural Information Processing Systems 35 (NeurIPS 2022)},
    year = {2022}
    }

    Topic: certified ML reasoning

  22. Hanjiang Hu, Zuxin Liu, Linyi Li, Jiacheng Zhu, Ding Zhao
    Robustness Certification of Visual Perception Models via Camera Motion Smoothing
    6th Annual Conference on Robot Learning (CoRL 2022)
    [Paper]   [Forum]   [Code]  
    @inproceedings{
    hu2022robustness,
    title={Robustness Certification of Visual Perception Models via Camera Motion Smoothing},
    author={Hanjiang Hu and Zuxin Liu and Linyi Li and Jiacheng Zhu and Ding Zhao},
    booktitle={6th Annual Conference on Robot Learning},
    year={2022},
    url={https://openreview.net/forum?id=uUxDTZK3o3X}
    }

    Topic: certified ML

  23. Linyi Li, Jiawei Zhang, Tao Xie, Bo Li
    Double Sampling Randomized Smoothing
    39th International Conference on Machine Learning (ICML 2022)
    [Conference Version]   [Full Version]   [Code]  
    @inproceedings{
    li2022double,
    title={Double Sampling Randomized Smoothing},
    author={Linyi Li and Jiawei Zhang and Tao Xie and Bo Li},
    booktitle={39th International Conference on Machine Learning (ICML 2022)},
    year={2022},
    }

    Topic: certified ML

    Summary: A tighter certification approach for randomized smoothing, that for the first time circumvents the well-known curse of dimensionality under mild conditions by leveraging statistics from two strategically-chosen distributions.

  24. Wenda Chu, Linyi Li, Bo Li
    TPC: Transformation-Specific Smoothing for Point Cloud Models
    39th International Conference on Machine Learning (ICML 2022)
    [Full Version]   [Code]  
    @inproceedings{
    chu2022tpc,
    title={TPC: Transformation-Specific Smoothing for Point Cloud Models},
    author={Wenda Chu and Linyi Li and Bo Li},
    booktitle={39th International Conference on Machine Learning (ICML 2022)},
    year={2022},
    }

    Topic: certified ML

    Summary: By extending the methodology for certifying image classifiers against transformations, we provide state-of-the-art certification algorithms for point cloud models with detailed point cloud transformation analyses.

  25. Maurice Weber, Linyi Li, Boxin Wang, Zhikuan Zhao, Bo Li, Ce Zhang
    Certifying Out-of-Domain Generalization for Blackbox Functions
    39th International Conference on Machine Learning (ICML 2022)
    [Conference Version]   [Full Version]   [Code]  
    @inproceedings{
    weber2022certifying,
    title={Certifying Out-of-Domain Generalization for Blackbox Functions},
    author={Maurice Weber and Linyi Li and Boxin Wang and Zhikuan Zhao and Bo Li and Ce Zhang},
    booktitle={39th International Conference on Machine Learning (ICML 2022)},
    year={2022},
    }

    Topic: certified ML

    Summary: A scalable certification algorithm for model generalization against distributional shift which requires no assumption on the model's architecture, as long as the distributional shift is bounded by Hellinger distance, a type of f-divergence. Core methodology is based on the positive semidefinite property of Gramian matrix.

  26. Fan Wu*, Linyi Li*, Chejian Xu, Huan Zhang, Bhavya Kailkhura, Krishnaram Kenthapadi, Ding Zhao, Bo Li
    COPA: Certifying Robust Policies for Offline Reinforcement Learning against Poisoning Attacks
    10th International Conference on Learning Representations (ICLR 2022)
    [Conference Version]   [Full Version]   [Leaderboard]   [Code]  
    @inproceedings{
    wu2022copa,
    title={{COPA}: Certifying Robust Policies for Offline Reinforcement Learning against Poisoning Attacks},
    author={Fan Wu and Linyi Li and Chejian Xu and Huan Zhang and Bhavya Kailkhura and Krishnaram Kenthapadi and Ding Zhao and Bo Li},
    booktitle={International Conference on Learning Representations},
    year={2022},
    url={https://openreview.net/forum?id=psh0oeMSBiF}
    }

    Topic: certified ML deep reinforcement learning

    Summary: The first approach for certifying deep RL robustness against offline training dataset perturbations, i.e., poisoning attacks, by aggregating over policies trained on partitioned datasets and policies for multiple time steps.

  27. Zhuolin Yang*, Linyi Li*, Xiaojun Xu, Bhavya Kailkhura, Tao Xie, Bo Li
    On the Certified Robustness for Ensemble Models and Beyond
    10th International Conference on Learning Representations (ICLR 2022)
    [Conference Version]   [Full Version]   [Code]  
    @inproceedings{
    yang2022on,
    title={On the Certified Robustness for Ensemble Models and Beyond},
    author={Zhuolin Yang and Linyi Li and Xiaojun Xu and Bhavya Kailkhura and Tao Xie and Bo Li},
    booktitle={International Conference on Learning Representations},
    year={2022},
    url={https://openreview.net/forum?id=tUa4REjGjTf}
    }

    Topic: certified ML

    Summary: Based on a curvature bound for randomized smoothing based classifiers, we prove that large confidence margin and gradient diversity are sufficient and necessary condition for certifiably robust ensembles. By regularizing these two factors, we acheive SOTA L2 certified robustness.

  28. Fan Wu, Linyi Li, Zijian Huang, Yevgeniy Vorobeychik, Ding Zhao, Bo Li
    CROP: Certifying Robust Policies for Reinforcement Learning through Functional Smoothing
    10th International Conference on Learning Representations (ICLR 2022)
    [Conference Version]   [Full Version]   [Leaderboard]   [Code]  
    @inproceedings{
    wu2022crop,
    title={{CROP}: Certifying Robust Policies for Reinforcement Learning through Functional Smoothing},
    author={Fan Wu and Linyi Li and Zijian Huang and Yevgeniy Vorobeychik and Ding Zhao and Bo Li},
    booktitle={International Conference on Learning Representations},
    year={2022},
    url={https://openreview.net/forum?id=HOjLHrlZhmx}
    }

    Topic: certified ML deep reinforcement learning

    Summary: The first scalable approach for certifying deep RL robustness against state perturbations, by combining randomized smoothing with a set of trajectory-based search algorithms.

  29. Ripon Saha, Akira Ura, Sonal Mahajan, Chenguang Zhu, Linyi Li, Yang Hu, Hiroaki Yoshida, Sarfraz Khurshid, Mukul R. Prasad
    SapientML: Synthesizing Machine Learning Pipelines by Learning from Human-Written Solutions
    44th International Conference on Software Engineering (ICSE 2022)
    [Conference Version]   [Full Version]  
    @inproceedings{saha2022sapientml,
    title={SapientML: synthesizing machine learning pipelines by learning from human-written solutions},
    author={Ripon Saha, Akira Ura, Sonal Mahajan, Chenguang Zhu, Linyi Li, Yang Hu, Hiroaki Yoshida, Sarfraz Khurshid, Mukul R. Prasad},
    booktitle={2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE)},
    year={2022},
    organization={IEEE}
    }

    Topic: autoML

    30. 2021#

  30. Zhuolin Yang*, Linyi Li*, Xiaojun Xu*, Shiliang Zuo, Qian Chen, Pan Zhou, Benjamin I. P. Rubinstein, Ce Zhang, Bo Li
    TRS: Transferability Reduced Ensemble via Promoting Gradient Diversity and Model Smoothness
    Advances in Neural Information Processing Systems (NeurIPS) 2021
    [Conference Version]   [Full Version]   [Code]  
    @inproceedings{yangli2021trs,
    title = {TRS: Transferability Reduced Ensemble via Promoting Gradient Diversity and Model Smoothness},
    author = {Zhuolin Yang and Linyi Li and Xiaojun Xu and Shiliang Zuo and Qian Chen and Pan Zhou and Benjamin I. P. Rubinstein and Ce Zhang and Bo Li},
    booktitle = {Advances in Neural Information Processing Systems 34 (NeurIPS 2021)},
    year = {2021}
    }

    Topic: robust ML

    Summary: We prove the guaranteed correlation between model diversity and adversarial transferabiltiy given bounded model smoothness, which leads to a strong regularizer that achieves SOTA ensemble robustness against existing strong attacks.

  31. Jiawei Zhang*, Linyi Li*, Huichen Li, Xiaolu Zhang, Shuang Yang, Bo Li
    Progressive-Scale Boundary Blackbox Attack via Projective Gradient Estimation
    International Conference on Machine Learning (ICML) 2021
    [Conference Version]   [Full Version]   [Code]   [Slides]  
    @inproceedings{zhangli2021progressive,
    title = {Progressive-Scale Boundary Blackbox Attack via Projective Gradient Estimation},
    author = {Zhang, Jiawei and Li, Linyi and Li, Huichen and Zhang, Xiaolu and Yang, Shuang and Li, Bo},
    booktitle = {Proceedings of the 38th International Conference on Machine Learning (ICML 2021)},
    pages = {12479--12490},
    year = {2021},
    editor = {Meila, Marina and Zhang, Tong},
    volume = {139},
    series = {Proceedings of Machine Learning Research},
    month = {18--24 Jul},
    publisher = {PMLR},
    }

    Topic: attacks for ML

    Summary: We systematically analyzed the gradient estimator that guides black-box attacks for DNNs, which reveals several key factors that can lead to more accurate gradient estimation with fewer queries. One way to realize these key factors is to conduct the attack with gradient estimation on a particularly scaled version of the image, which leads to the PSBA black-box attack with SOTA query effciency.

  32. Linyi Li*, Maurice Weber*, Xiaojun Xu, Luka Rimanic, Bhavya Kailkhura, Tao Xie, Ce Zhang, Bo Li
    TSS: Transformation-Specific Smoothing for Robustness Certification
    ACM Conference on Computer and Communications Security (CCS) 2021
    [Conference Version]   [Full Version]   [Code]   [Slides]  
    @inproceedings{li2021tss,
    title={TSS: Transformation-Specific Smoothing for Robustness Certification},
    author={Linyi Li and Maurice Weber and Xiaojun Xu and Luka Rimanic and Bhavya Kailkhura and Tao Xie and Ce Zhang and Bo Li},
    year={2021},
    booktitle={ACM Conference on Computer and Communications Security (CCS 2021)}
    }

    Topic: certified ML

    Summary: Natural transformations such as rotation and scaling are common in the physical world. We propose the first scalable certification approach against natural transformations based on randomzied smoothing, rigorous Lipschitz analysis, and stratified sampling. For the first time, we certify non-trivial robustness (>30% certified robust accuracy) on the large-scale ImageNet dataset.

  33. Huichen Li*, Linyi Li*, Xiaojun Xu, Xiaolu Zhang, Shuang Yang, Bo Li
    Nonlinear Projection Based Gradient Estimation for Query Efficient Blackbox Attacks
    International Conference on Artificial Intelligence and Statistics (AISTATS) 2021
    [Conference Version]   [Full Version]   [Code]  
    @inproceedings{li2020nolinear,
    title={Nonlinear Gradient Estimation for Query Efficient Blackbox Attack},
    author={Huichen Li and Linyi Li and Xiaojun Xu and Xiaolu Zhang and Shuang Yang and Bo Li},
    year={2021},
    booktitle = {International Conference on Artificial Intelligence and Statistics (AISTATS 2021)},
    series = {Proceedings of Machine Learning Research},
    month = {13--15 Apr},
    publisher = {PMLR},
    }

    Topic: attacks for ML

    Summary: We analyze the outcome of using nonlinear projections for black-box gradient-estimation-based attacks, which shows that proper nonlinear projections can help to improve the attack efficiency.

    34. Before 2021#

  34. Linyi Li, Zhenwen Li, Weijie Zhang, Jun Zhou, Pengcheng Wang, Jing Wu, Guanghua He, Xia Zeng, Yuetang Deng, Tao Xie
    Clustering Test Steps in Natural Language toward Automating Test Automation
    ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) 2020, Industry Track
    [Paper]   [Video]  
    @inproceedings{li2020clustep,
    title = {Clustering Test Steps in Natural Language toward Automating Test Automation},
    author = {Li, Linyi and Li, Zhenwen and Zhang, Weijie and Zhou, Jun and Wang, Pengcheng and Wu, Jing and He, Guanghua and Zeng, Xia and Deng, Yuetang and Xie, Tao},
    booktitle = {Proceedings of the 28th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering {(ESEC/FSE 2020)}},
    year = {2020},
    doi = {10.1145/3368089.3417067},
    url = {https://doi.org/10.1145/3368089.3417067}
    }

    Topic: ML for software testing

    Summary: We provide an effective pipeline to cluster test steps in natural language and then synthesize executable test cases, deployed for WeChat testing.

  35. Linyi Li*, Zexuan Zhong*, Bo Li, Tao Xie
    Robustra: Training Provable Robust Neural Networks over Reference Adversarial Space
    International Joint Conference on Artificial Intelligence (IJCAI) 2019
    [Paper]   [Code]  
    @inproceedings{li2019robustra,
    title = {Robustra: Training Provable Robust Neural Networks over Reference Adversarial Space},
    author = {Li, Linyi and Zhong, Zexuan and Li, Bo and Xie, Tao},
    booktitle = {Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI 2019)},
    publisher = {International Joint Conferences on Artificial Intelligence Organization},
    pages = {4711--4717},
    year = {2019},
    month = {7},
    doi = {10.24963/ijcai.2019/654},
    url = {https://doi.org/10.24963/ijcai.2019/654}
    }

    Topic: certified ML

    Summary: We propose a training method for achieving certified robustness by regularizing only within the reference adversarial space from a jointly trained model to alleviate the optimization hardness and achieve higher certified robustness.

  36. Klas Leino, Shayak Sen, Anupam Datta, Matt Fredrikson, Linyi Li
    Influence-Directed Explanations for Deep Convolutional Networks
    IEEE International Test Conference (ITC) 2018
    [Paper]  
    @inproceedings{leino2018influence,
    author={Leino, Klas and Sen, Shayak and Datta, Anupam and Fredrikson, Matt and Li, Linyi},
    booktitle={2018 IEEE International Test Conference (ITC)},
    title={Influence-Directed Explanations for Deep Convolutional Networks},
    year={2018},
    pages={1-8},
    }

    Topic: intepretable ML undergrad research

  37. Junyi Wang, Xiaoying Bai, Linyi Li, Zhicheng Ji, Haoran Ma
    A Model-Based Framework For Cloud API Testing
    IEEE 41st Annual Computer Software and Applications Conference (COMPSAC) 2017
    [Paper]  
    @inproceedings{wang2017model,
    author={Wang, Junyi and Bai, Xiaoying and Li, Linyi and Ji, Zhicheng and Ma, Haoran},
    booktitle={2017 IEEE 41st Annual Computer Software and Applications Conference (COMPSAC)},
    title={A Model-Based Framework for Cloud API Testing},
    year={2017},
    volume={2},
    pages={60-65},
    doi={10.1109/COMPSAC.2017.24},
    ISSN={0730-3157},
    month={July},
    }

    Topic: software testing undergrad research

  38. Junyi Wang, Xiaoying Bai, Haoran Ma, Linyi Li, Zhicheng Ji
    Cloud API Testing
    IEEE International Conference on Software Verification and Validation Workshops (ICSTW) 2017
    [Paper]  
    @inproceedings{wang2017cloud,
    title={Cloud API testing},
    author={Wang, Junyi and Bai, Xiaoying and Ma, Haoran and Li, Linyi and Ji, Zhicheng},
    booktitle={2017 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW)},
    pages={385--386},
    year={2017},
    organization={IEEE}
    }

    Topic: software testing undergrad research