Linyi Li

According to our database, Linyi Li authored at least 35 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

InfiCoder-Eval: Systematically Evaluating the Question-Answering Capabilities of Code Large Language Models.

Linyi Li, Shijie Geng, Zhenwen Li, Yibo He, Hao Yu, Ziyue Hua, Guanghan Ning, Siwei Wang, Tao Xie, Hongxia Yang

CoRR, 2024

COMMIT: Certifying Robustness of Multi-Sensor Fusion Systems against Semantic Attacks.

Zijian Huang, Wenda Chu, Linyi Li, Chejian Xu, Bo Li

CoRR, 2024

Effects of Exponential Gaussian Distribution on (Double Sampling) Randomized Smoothing.

Youwei Shu, Xi Xiao, Derui Wang, Yuxin Cao, Siji Chen, Jason Xue, Linyi Li, Bo Li

Proceedings of the Forty-first International Conference on Machine Learning, 2024

COLEP: Certifiably Robust Learning-Reasoning Conformal Prediction via Probabilistic Circuits.

Mintong Kang, Nezihe Merve Gürel, Linyi Li, Bo Li

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Pixel-wise Smoothing for Certified Robustness against Camera Motion Perturbations.

Hanjiang Hu, Zuxin Liu, Linyi Li, Jiacheng Zhu, Ding Zhao

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023

Can Pruning Improve Certified Robustness of Neural Networks?

Zhangheng Li, Tianlong Chen, Linyi Li, Bo Li, Zhangyang Wang

Trans. Mach. Learn. Res., 2023

SoK: Certified Robustness for Deep Neural Networks.

Linyi Li, Tao Xie, Bo Li

Proceedings of the 44th IEEE Symposium on Security and Privacy, 2023

CARE: Certifiably Robust Learning with Reasoning via Variational Inference.

Jiawei Zhang, Linyi Li, Ce Zhang, Bo Li

Proceedings of the 2023 IEEE Conference on Secure and Trustworthy Machine Learning, 2023

FaShapley: Fast and Approximated Shapley Based Model Pruning Towards Certifiably Robust DNNs.

Mintong Kang, Linyi Li, Bo Li

Proceedings of the 2023 IEEE Conference on Secure and Trustworthy Machine Learning, 2023

Reliability Assurance for Deep Neural Network Architectures Against Numerical Defects.

Linyi Li, Yuhao Zhang, Luyao Ren, Yingfei Xiong, Tao Xie

Proceedings of the 45th IEEE/ACM International Conference on Software Engineering, 2023

2022

FOCUS: Fairness via Agent-Awareness for Federated Learning on Heterogeneous Data.

Wenda Chu, Chulin Xie, Boxin Wang, Linyi Li, Lang Yin, Han Zhao, Bo Li

CoRR, 2022

COPA: Certifying Robust Policies for Offline Reinforcement Learning against Poisoning Attacks.

Fan Wu, Linyi Li, Chejian Xu, Huan Zhang, Bhavya Kailkhura, Krishnaram Kenthapadi, Ding Zhao, Bo Li

CoRR, 2022

General Cutting Planes for Bound-Propagation-Based Neural Network Verification.

Huan Zhang, Shiqi Wang, Kaidi Xu, Linyi Li, Bo Li, Suman Jana, Cho-Jui Hsieh, J. Zico Kolter

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Improving Certified Robustness via Statistical Learning with Logical Reasoning.

Zhuolin Yang, Zhikuan Zhao, Boxin Wang, Jiawei Zhang, Linyi Li, Hengzhi Pei, Bojan Karlas, Ji Liu, Heng Guo, Ce Zhang, Bo Li

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

LOT: Layer-wise Orthogonal Training on Improving l2 Certified Robustness.

Xiaojun Xu, Linyi Li, Bo Li

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Certifying Some Distributional Fairness with Subpopulation Decomposition.

Mintong Kang, Linyi Li, Maurice Weber, Yang Liu, Ce Zhang, Bo Li

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Fairness in Federated Learning via Core-Stability.

Bhaskar Ray Chaudhury, Linyi Li, Mintong Kang, Bo Li, Ruta Mehta

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

SAPIENTML: Synthesizing Machine Learning Pipelines by Learning from Human-Written Solutions.

Ripon K. Saha, Akira Ura, Sonal Mahajan, Chenguang Zhu, Linyi Li, Yang Hu, Hiroaki Yoshida, Sarfraz Khurshid, Mukul R. Prasad

Proceedings of the 44th IEEE/ACM 44th International Conference on Software Engineering, 2022

Certifying Out-of-Domain Generalization for Blackbox Functions.

Maurice Weber, Linyi Li, Boxin Wang, Zhikuan Zhao, Bo Li, Ce Zhang

Proceedings of the International Conference on Machine Learning, 2022

Double Sampling Randomized Smoothing.

Linyi Li, Jiawei Zhang, Tao Xie, Bo Li

Proceedings of the International Conference on Machine Learning, 2022

TPC: Transformation-Specific Smoothing for Point Cloud Models.

Wenda Chu, Linyi Li, Bo Li

Proceedings of the International Conference on Machine Learning, 2022

On the Certified Robustness for Ensemble Models and Beyond.

Zhuolin Yang, Linyi Li, Xiaojun Xu, Bhavya Kailkhura, Tao Xie, Bo Li

Proceedings of the Tenth International Conference on Learning Representations, 2022

CROP: Certifying Robust Policies for Reinforcement Learning through Functional Smoothing.

Fan Wu, Linyi Li, Zijian Huang, Yevgeniy Vorobeychik, Ding Zhao, Bo Li

Proceedings of the Tenth International Conference on Learning Representations, 2022

COPA: Certifying Robust Policies for Offline Reinforcement Learning against Poisoning Attacks.

Fan Wu, Linyi Li, Huan Zhang, Bhavya Kailkhura, Krishnaram Kenthapadi, Ding Zhao, Bo Li

Proceedings of the Tenth International Conference on Learning Representations, 2022

Robustness Certification of Visual Perception Models via Camera Motion Smoothing.

Hanjiang Hu, Zuxin Liu, Linyi Li, Jiacheng Zhu, Ding Zhao

Proceedings of the Conference on Robot Learning, 2022

2021

TRS: Transferability Reduced Ensemble via Encouraging Gradient Diversity and Model Smoothness.

Zhuolin Yang, Linyi Li, Xiaojun Xu, Shiliang Zuo, Qian Chen, Benjamin I. P. Rubinstein, Ce Zhang, Bo Li

CoRR, 2021

TRS: Transferability Reduced Ensemble via Promoting Gradient Diversity and Model Smoothness.

Zhuolin Yang, Linyi Li, Xiaojun Xu, Shiliang Zuo, Qian Chen, Pan Zhou, Benjamin I. P. Rubinstein, Ce Zhang, Bo Li

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Progressive-Scale Boundary Blackbox Attack via Projective Gradient Estimation.

Jiawei Zhang, Linyi Li, Huichen Li, Xiaolu Zhang, Shuang Yang, Bo Li

Proceedings of the 38th International Conference on Machine Learning, 2021

TSS: Transformation-Specific Smoothing for Robustness Certification.

Linyi Li, Maurice Weber, Xiaojun Xu, Luka Rimanic, Bhavya Kailkhura, Tao Xie, Ce Zhang, Bo Li

Proceedings of the CCS '21: 2021 ACM SIGSAC Conference on Computer and Communications Security, Virtual Event, Republic of Korea, November 15, 2021

Nonlinear Projection Based Gradient Estimation for Query Efficient Blackbox Attacks.

Huichen Li, Linyi Li, Xiaojun Xu, Xiaolu Zhang, Shuang Yang, Bo Li

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

On the Limitations of Denoising Strategies as Adversarial Defenses.

Zhonghan Niu, Zhaoxi Chen, Linyi Li, Yubin Yang, Bo Li, Jinfeng Yi

CoRR, 2020

SoK: Certified Robustness for Deep Neural Networks.

Linyi Li, Xiangyu Qi, Tao Xie, Bo Li

CoRR, 2020

Provable Robust Learning Based on Transformation-Specific Smoothing.

Linyi Li, Maurice Weber, Xiaojun Xu, Luka Rimanic, Tao Xie, Ce Zhang, Bo Li

CoRR, 2020

Clustering test steps in natural language toward automating test automation.

Linyi Li, Zhenwen Li, Weijie Zhang, Jun Zhou, Pengcheng Wang, Jing Wu, Guanghua He, Xia Zeng, Yuetang Deng, Tao Xie

Proceedings of the ESEC/FSE '20: 28th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, 2020

2019

Robustra: Training Provable Robust Neural Networks over Reference Adversarial Space.

Linyi Li, Zexuan Zhong, Bo Li, Tao Xie

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019