Yangsibo Huang

According to our database, Yangsibo Huang authored at least 33 papers between 2019 and 2024.

Collaborative distances:

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

2024

The Future of Open Human Feedback.

Shachar Don-Yehiya, Ben Burtenshaw, Ramón Fernandez Astudillo, Cailean Osborne, Mimansa Jaiswal, Tzu-Sheng Kuo, Wenting Zhao, Idan Shenfeld, Andi Peng, Mikhail Yurochkin, Atoosa Kasirzadeh, Yangsibo Huang, Tatsunori Hashimoto, Yacine Jernite, Daniel Vila-Suero, Omri Abend, Jennifer Ding, Sara Hooker, Hannah Rose Kirk, Leshem Choshen

CoRR, 2024

ConceptMix: A Compositional Image Generation Benchmark with Controllable Difficulty.

Xindi Wu, Dingli Yu, Yangsibo Huang, Olga Russakovsky, Sanjeev Arora

CoRR, 2024

MUSE: Machine Unlearning Six-Way Evaluation for Language Models.

Weijia Shi, Jaechan Lee, Yangsibo Huang, Sadhika Malladi, Jieyu Zhao, Ari Holtzman, Daogao Liu, Luke Zettlemoyer, Noah A. Smith, Chiyuan Zhang

CoRR, 2024

Evaluating Copyright Takedown Methods for Language Models.

Boyi Wei, Weijia Shi, Yangsibo Huang, Noah A. Smith, Chiyuan Zhang, Luke Zettlemoyer, Kai Li, Peter Henderson

CoRR, 2024

Crosslingual Capabilities and Knowledge Barriers in Multilingual Large Language Models.

Lynn Chua, Badih Ghazi, Yangsibo Huang, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Amer Sinha, Chulin Xie, Chiyuan Zhang

CoRR, 2024

SORRY-Bench: Systematically Evaluating Large Language Model Safety Refusal Behaviors.

Tinghao Xie, Xiangyu Qi, Yi Zeng, Yangsibo Huang, Udari Madhushani Sehwag, Kaixuan Huang, Luxi He, Boyi Wei, Dacheng Li, Ying Sheng, Ruoxi Jia, Bo Li, Kai Li, Danqi Chen, Peter Henderson, Prateek Mittal

CoRR, 2024

Fantastic Copyrighted Beasts and How (Not) to Generate Them.

Luxi He, Yangsibo Huang, Weijia Shi, Tinghao Xie, Haotian Liu, Yue Wang, Luke Zettlemoyer, Chiyuan Zhang, Danqi Chen, Peter Henderson

CoRR, 2024

Mind the Privacy Unit! User-Level Differential Privacy for Language Model Fine-Tuning.

Lynn Chua, Badih Ghazi, Yangsibo Huang, Pritish Kamath, Daogao Liu, Pasin Manurangsi, Amer Sinha, Chiyuan Zhang

CoRR, 2024

AI Risk Management Should Incorporate Both Safety and Security.

Xiangyu Qi, Yangsibo Huang, Yi Zeng, Edoardo Debenedetti, Jonas Geiping, Luxi He, Kaixuan Huang, Udari Madhushani, Vikash Sehwag, Weijia Shi, Boyi Wei, Tinghao Xie, Danqi Chen, Pin-Yu Chen, Jeffrey Ding, Ruoxi Jia, Jiaqi Ma, Arvind Narayanan, Weijie J. Su, Mengdi Wang, Chaowei Xiao, Bo Li, Dawn Song, Peter Henderson, Prateek Mittal

CoRR, 2024

A Safe Harbor for AI Evaluation and Red Teaming.

Shayne Longpre, Sayash Kapoor, Kevin Klyman, Ashwin Ramaswami, Rishi Bommasani, Borhane Blili-Hamelin, Yangsibo Huang, Aviya Skowron, Zheng Xin Yong, Suhas Kotha, Yi Zeng, Weiyan Shi, Xianjun Yang, Reid Southen, Alexander Robey, Patrick Chao, Diyi Yang, Ruoxi Jia, Daniel Kang, Sandy Pentland, Arvind Narayanan, Percy Liang, Peter Henderson

CoRR, 2024

Assessing the Brittleness of Safety Alignment via Pruning and Low-Rank Modifications.

Boyi Wei, Kaixuan Huang, Yangsibo Huang, Tinghao Xie, Xiangyu Qi, Mengzhou Xia, Prateek Mittal, Mengdi Wang, Peter Henderson

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

Position: A Safe Harbor for AI Evaluation and Red Teaming.

Shayne Longpre, Sayash Kapoor, Kevin Klyman, Ashwin Ramaswami, Rishi Bommasani, Borhane Blili-Hamelin, Yangsibo Huang, Aviya Skowron, Zheng Xin Yong, Suhas Kotha, Yi Zeng, Weiyan Shi, Xianjun Yang, Reid Southen, Alexander Robey, Patrick Chao, Diyi Yang, Ruoxi Jia, Daniel Kang, Sandy Pentland, Arvind Narayanan, Percy Liang, Peter Henderson

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

Detecting Pretraining Data from Large Language Models.

Weijia Shi, Anirudh Ajith, Mengzhou Xia, Yangsibo Huang, Daogao Liu, Terra Blevins, Danqi Chen, Luke Zettlemoyer

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Catastrophic Jailbreak of Open-source LLMs via Exploiting Generation.

Yangsibo Huang, Samyak Gupta, Mengzhou Xia, Kai Li, Danqi Chen

Proceedings of the Twelfth International Conference on Learning Representations, 2024

LabelDP-Pro: Learning with Label Differential Privacy via Projections.

Badih Ghazi, Yangsibo Huang, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Chiyuan Zhang

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

A Dataset Auditing Method for Collaboratively Trained Machine Learning Models.

Yangsibo Huang, Chun-Yin Huang, Xiaoxiao Li, Kai Li

IEEE Trans. Medical Imaging, 2023

Learning across Data Owners with Joint Differential Privacy.

Yangsibo Huang, Haotian Jiang, Daogao Liu, Mohammad Mahdian, Jieming Mao, Vahab Mirrokni

CoRR, 2023

Matching-based Data Valuation for Generative Model.

Jiaxi Yang, Wenglong Deng, Benlin Liu, Yangsibo Huang, Xiaoxiao Li

CoRR, 2023

Challenges towards the Next Frontier in Privacy.

Rachel Cummings, Damien Desfontaines, David Evans, Roxana Geambasu, Matthew Jagielski, Yangsibo Huang, Peter Kairouz, Gautam Kamath, Sewoong Oh, Olga Ohrimenko, Nicolas Papernot, Ryan Rogers, Milan Shen, Shuang Song, Weijie J. Su, Andreas Terzis, Abhradeep Thakurta, Sergei Vassilvitskii, Yu-Xiang Wang, Li Xiong, Sergey Yekhanin, Da Yu, Huanyu Zhang, Wanrong Zhang

CoRR, 2023

kNN-Adapter: Efficient Domain Adaptation for Black-Box Language Models.

Yangsibo Huang, Daogao Liu, Zexuan Zhong, Weijia Shi, Yin Tat Lee

CoRR, 2023

Sparsity-Preserving Differentially Private Training of Large Embedding Models.

Badih Ghazi, Yangsibo Huang, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Amer Sinha, Chiyuan Zhang

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Privacy Implications of Retrieval-Based Language Models.

Yangsibo Huang, Samyak Gupta, Zexuan Zhong, Kai Li, Danqi Chen

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023

2022

Recovering Private Text in Federated Learning of Language Models.

Samyak Gupta, Yangsibo Huang, Zexuan Zhong, Tianyu Gao, Kai Li, Danqi Chen

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

2021

Evaluating Gradient Inversion Attacks and Defenses in Federated Learning.

Yangsibo Huang, Samyak Gupta, Zhao Song, Kai Li, Sanjeev Arora

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

EMA: Auditing Data Removal from Trained Models.

Yangsibo Huang, Xiaoxiao Li, Kai Li

Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2021 - 24th International Conference, Strasbourg, France, September 27, 2021

2020

MixCon: Adjusting the Separability of Data Representations for Harder Data Recovery.

Xiaoxiao Li, Yangsibo Huang, Binghui Peng, Zhao Song, Kai Li

CoRR, 2020

Deep Learning Based Detection and Localization of Cerebal Aneurysms in Computed Tomography Angiography.

Ziheng Duan, Daniel Montes, Yangsibo Huang, Dufan Wu, Javier M. Romero, Ramon Gilberto Gonzalez, Quanzheng Li

CoRR, 2020

Privacy-preserving Learning via Deep Net Pruning.

Yangsibo Huang, Yushan Su, Sachin Ravi, Zhao Song, Sanjeev Arora, Kai Li

CoRR, 2020

InstaHide: Instance-hiding Schemes for Private Distributed Learning.

Yangsibo Huang, Zhao Song, Kai Li, Sanjeev Arora

Proceedings of the 37th International Conference on Machine Learning, 2020

TextHide: Tackling Data Privacy for Language Understanding Tasks.

Yangsibo Huang, Zhao Song, Danqi Chen, Kai Li, Sanjeev Arora

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2020, 2020

IFGAN: Missing Value Imputation using Feature-specific Generative Adversarial Networks.

Wei Qiu, Yangsibo Huang, Quanzheng Li

Proceedings of the 2020 IEEE International Conference on Big Data (IEEE BigData 2020), 2020

2019

Deep Q Learning Driven CT Pancreas Segmentation With Geometry-Aware U-Net.

Yunze Man, Yangsibo Huang, Junyi Feng, Xi Li, Fei Wu

IEEE Trans. Medical Imaging, 2019

DeepMCDose: A Deep Learning Method for Efficient Monte Carlo Beamlet Dose Calculation by Predictive Denoising in MR-Guided Radiotherapy.

Ryan Neph, Yangsibo Huang, Youming Yang, Ke Sheng

Proceedings of the Artificial Intelligence in Radiation Therapy, 2019