Currently, my research mainly aims at
- providing rigorous guarantees of various trustworthy properties (robustness, fairness, reliabiltiy, etc) for a given deep neural network system;
- improving such guaranteed trustworthiness for machine learning via strategic architecture design, dataset building, model training, post-processing, etc.
Click or touch to browse details.
(* denotes to equal contribution)
-
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]
[BibTex]
@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.
-
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]
[BibTex]
@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.
-
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]
[BibTex]
@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.
-
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]
[BibTex]
@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.
-
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]
[BibTex]
@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.
-
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]
[BibTex]
@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.
-
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]
[BibTex]
@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.
-
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]
[BibTex]
@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.
-
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]
[BibTex]
@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.
-
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]
[BibTex]
@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.
-
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]
[BibTex]
@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.
-
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]
[BibTex]
@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.
(* denotes to equal contribution)
-
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]
[BibTex]
@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.
-
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]
[BibTex]
@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.
-
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]
[BibTex]
@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.
-
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]
[BibTex]
@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.
-
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]
[BibTex]
@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.
-
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]
[BibTex]
@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.
-
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]
[BibTex]
@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.
-
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]
[BibTex]
@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.
-
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]
[BibTex]
@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.
-
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]
[BibTex]
@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.
-
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]
[BibTex]
@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.
-
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]
[BibTex]
@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.
(* denotes to equal contribution)
2023
-
Linyi Li
Certifiably Trustworthy Deep Learning Systems at Scale
Doctoral Thesis
[Full Version]
[BibTex]
@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}
}
-
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]
[BibTex]
@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
-
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]
[BibTex]
@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
-
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]
[BibTex]
@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
-
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]
[BibTex]
@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
-
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]
[BibTex]
@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
2022
-
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]
[BibTex]
@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
-
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]
[BibTex]
@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
-
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]
[BibTex]
@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
-
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]
[BibTex]
@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
-
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]
[BibTex]
@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
-
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]
[BibTex]
@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
-
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]
[BibTex]
@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
-
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]
[BibTex]
@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
-
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]
[BibTex]
@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
-
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]
[BibTex]
@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
-
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]
[BibTex]
@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
-
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]
[BibTex]
@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
-
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]
[BibTex]
@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
2021
-
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]
[BibTex]
@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
-
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]
[BibTex]
@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
-
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]
[BibTex]
@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
-
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]
[BibTex]
@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
2020
-
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]
[BibTex]
@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
2019
-
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]
[BibTex]
@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
2018
-
Klas Leino, Shayak Sen, Anupam Datta, Matt Fredrikson, Linyi Li
Influence-Directed Explanations for Deep Convolutional Networks
IEEE International Test Conference (ITC) 2018
[Paper]
[BibTex]
@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
2017
-
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]
[BibTex]
@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
-
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]
[BibTex]
@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
Preprints can be found in Google Scholar profile.
