Knowledge Transfer

High-Dimensional Multivariate Linear Regression with Weighted Nuclear Norm Regularization

Namjoon Suh, Li-Hsiang Lin, Xiaoming Huo

 Journal of Computational and Graphical Statistics (2024): 1-12.

Adjusted Wasserstein Distributionally Robust Estimator in Statistical Learning

Yiling Xie , Xiaoming Huo

To appear in Journal of Machine Learning Research

Continual learning: Applications and the road forward

Eli Verwimp , Shai Ben-David , Matthias Bethge, Andrea Cossu, Alexander Gepperth, Tyler L. Hayes, Eyke Hüllermeier

To appear in  Transaction on Machine Learning Research

Jatmo: Prompt Injection Defense by Task-Specific Finetuning

Julien Piet, Maha Alrashed, Chawin Sitawarin, Sizhe Chen, Zeming Wei, Elizabeth Sun , Basel Alomair, David Wagner

European Symposium on Research in Computer Security (ESORICS 2024), Bydgoszcz, Poland, September 16, 2024

Remote Keylogging Attacks in Multi-user VR Applications

Zihao Su, Kunlin Cai, Reuben Beeler, Lukas Dresel, Allan Garcia, Ilya Grishchenko, Yuan Tian, Christopher Kruegel, Giovanni Vigna

33rd USENIX Security Symposium (USENIX Security 24), Philadelphia, PA, August 14-16, 2024
 

SAIN: Improving ICS Attack Detection Sensitivity via State-Aware Invariants

Syed Ghazanfar Abbas, Muslum Ozgur Ozmen, Abdulellah Alsaheel, Arslan Khan,, Z. Berkay Celik, Dongyan Xu

33rd USENIX Security Symposium (USENIX Security 24), Philadelphia, PA, August 14-16, 2024
 

True Attacks, Attack Attempts, or Benign Triggers? An Empirical Measurement of Network Alerts in a Security Operations Center

Limin Yang, Zhi Chen, Chenkai Wang, Zhenning Zhang, Sushruth Booma, Phuong Cao, Constantin Adam, Alex Withers, Zbigniew Kalbarczyk, Ravishankar K. Iyer, Gang Wang

33rd USENIX Security Symposium (USENIX Security 24), Philadelphia, PA, August 14-16, 2024
 

MIST: Defending Against Membership Inference Attacks Through Membership-Invariant Subspace Training

Jiacheng Li, Ninghui Li, Bruno Ribeiro

33rd USENIX Security Symposium (USENIX Security 24), Philadelphia, PA, August 14-16, 2024

RICE: Breaking Through the Training Bottlenecks of Reinforcement Learning with Explanation

Zelei Cheng, Xian Wu, Jiahao Yu, Sabrina Yang, Gang Wang, Xinyu Xing

International Conference on Machine Learning (ICML 2024), Vienna, Austria, July 21-27, 2024

Optimally Improving Cooperative Learning in a Social Setting

Shahrzad Haddadan, Cheng Xin, Jie Gao

International Conference on Machine Learning (ICML 2024), Vienna, Austria, July 21-27, 2024

Adversarial Images can Control Generative Models at Runtime

Luke Bailey, Euan Ong, Stuart Russell, Scott Emmons

International Conference on Machine Learning (ICML 2024), Vienna, Austria, July 21-27, 2024

InstructRetro: Instruction tuning post retrieval-augmented pretraining

Boxin Wang, Wei Ping, Lawrence McAfee, Peng Xu, Bo Li, Mohammad Shoeybi, Bryan Catanzaro

International Conference on Machine Learning (ICML 2024), Vienna Austria, July 21-27, 2024
 

Differentially private synthetic data via foundation model apis 2: Text

Chulin Xie, Zinan Lin, Arturs Backurs, Sivakanth Gopi, Da Yu, Huseyin A. Inan, Harsha Nori, Haotian Jiang, Huishuai Zhang, Yin Tat Lee, Bo Li, Sergey Yekhanin

International Conference on Machine Learning (ICML 2024), Vienna Austria, July 21-27, 2024
 

Universal consistency of wide and deep ReLU neural networks and minimax optimal convergence rates for Kolmogorov-Donoho optimal function classes

Hyunouk Ko, Xiaoming Huo

International Conference on Machine Learning (ICML 2024), Vienna Austria, July 21-27, 2024
 

Decoding Compressed Trust: Scrutinizing the Trustworthiness of Efficient LLMs Under Compression

Junyuan Hong, Jinhao Duan, Chenhui Zhang, Zhangheng Li, Chulin Xie, Kelsey Lieberman, James Diffenderfer , Brian Bartoldson , Ajay Jaiswal , Kaidi Xu , Bhavya Kailkhura , Dan Hendrycks , Dawn Song , Zhangyang Wang , Bo Li

International Conference on Machine Learning (ICML 2024), Vienna Austria, July 21-27, 2024
 

Harmbench: A standardized evaluation framework for automated red teaming and robust refusal

Mantas Mazeika, Long Phan, Xuwang Yin, Andy Zou, Zifan Wang, Norman Mu, Elham Sakhaee, Nathaniel Li , Steven Basart, Bo Li , David Forsyth , Dan Hendrycks

International Conference on Machine Learning (ICML 2024), Vienna Austria, July 21-27, 2024
 

C-RAG: Certified Generation Risks for Retrieval-Augmented Language Models

Kang, Mintong, Nezihe Merve Gürel, Ning Yu, Dawn Song, Bo Li

International Conference on Machine Learning (ICML 2024), Vienna Austria, July 21-27, 2024
 

Certifiably Byzantine-Robust Federated Conformal Prediction

Mintong Kang, Zhen Lin, Jimeng Sun, Cao Xiao, Bo Li

International Conference on Machine Learning (ICML 2024), Vienna Austria, July 21-27, 2024
 

Grath: Gradual self-truthifying for large language models

Weixin Chen, Bo Li

International Conference on Machine Learning (ICML 2024), Vienna Austria, July 21-27, 2024
 

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

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

International Conference on Machine Learning (ICML 2024), Vienna Austria, July 21-27, 2024
 

HALC: Object Hallucination Reduction via Adaptive Focal-Contrast Decoding

Zhaorun Chen, Zhuokai Zhao, Hongyin Luo, Huaxiu Yao, Bo Li, Jiawei Zhou

International Conference on Machine Learning (ICML 2024), Vienna Austria, July 21-27, 2024
 

SHINE: Shielding Backdoors in Deep Reinforcement Learning

Wenbo Guo, Zhuowen Yuan, Jinyuan Jia, Bo Li, Dawn Song

International Conference on Machine Learning (ICML 2024), Vienna Austria, July 21-27, 2024
 

RigorLLM: Resilient Guardrails for Large Language Models against Undesired Content

Yuan, Zhuowen, Zidi Xiong, Yi Zeng, Ning Yu, Ruoxi Jia, Dawn Song, and Bo Li

International Conference on Machine Learning (ICML 2024), Vienna Austria, July 21-27, 2024
 

DPzero: Dimension-independent and differentially private zeroth-order optimization

Liang Zhang, Kiran Koshy Thekumparampil, Sewoong Oh, Niao He

International Conference on Machine Learning (ICML 2024), Vienna Austria, July 21-27, 2024
 

Insufficient Statistics Perturbation: Stable Estimators for Private Least Squares

Gavin Brown, Jonathan Hayase, Samuel Hopkins, Weihao Kong, Xiyang Liu, Sewoong Oh, Juan C. Perdomo, Adam Smith

To appear in the 37th Annual Conference on Learning Theory (COLT 2024), June 30th-July 3rd, 2024 in Edmonton, Canada.

Defending Membership Inference Attack on Edge using Trusted Execution Environments

Cheng-Yun Yang, Gowri Ramshankar, Sudarshan Nambiar, Evan Miller, Xun Zhang, Nicholas Eliopoulos, Purvish Jajal, Dave Tian, Shuo-Han Chen, Chiy-Ferng Perng, Yung-Hsiang Lu

Poster at Design Automation Conference (DAC 2024), San Francisco, CA, June 23 - 27, 2024

Practical Region-level Attack against Segment Anything Models

Yifan Shen, Zhengyuan Li, Gang Wang

In Proceedings of the IEEE CVPR Workshop on Fair, Data-efficient, and Trusted Computer Vision (TCV), in conjunction with IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR), Seattle, WA, June 17-20, 2024

MMSum: A Dataset for Multimodal Summarization and Thumbnail Generation of Videos

Jielin Qiu, Jiacheng Zhu, William Han, Aditesh Kumar, Karthik Mittal, Claire Jin, Zhengyuan Yang, Linjie Li, Jianfeng Wang, Ding Zhao, Bo Li, Lijuan Wang

Computer Vision and Pattern Recognition Conference (CVPR 2024), Seattle, WA, June 17 - 21, 2024

ChatScene: Knowledge-Enabled Safety-Critical Scenario Generation for Autonomous Vehicles.

Jiawei Zhang, Chejian Xu, Bo Li

Computer Vision and Pattern Recognition Conference (CVPR 2024), Seattle, WA, June 17 - 21, 2024

PerAda: Parameter-efficient and generalizable federated learning personalization with guarantees

Chulin Xie, De-An Huang, Wenda Chu, Daguang Xu, Chaowei Xiao, Bo Li, Anima Anandkumar

Computer Vision and Pattern Recognition Conference (CVPR 2024), Seattle, WA, June 17 - 21, 2024