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 001; Ke Sheng (AIRT@MICCAI, 2019)

Deep Q Learning Driven CT Pancreas Segmentation With Geometry-Aware U-Net. - Yunze Man; Yangsibo Huang; Junyi Feng; Xi Li 001; Fei Wu 001 (IEEE Trans. Medical Imaging, 2019)

IFGAN: Missing Value Imputation using Feature-specific Generative Adversarial Networks. - Wei Qiu; Yangsibo Huang; Quanzheng Li (IEEE BigData, 2020)

TextHide: Tackling Data Privacy for Language Understanding Tasks. - Yangsibo Huang; Zhao Song 002; Danqi Chen 001; Kai Li 001; Sanjeev Arora (EMNLP (Findings), 2020)

InstaHide: Instance-hiding Schemes for Private Distributed Learning. - Yangsibo Huang; Zhao Song 002; Kai Li 001; Sanjeev Arora (ICML, 2020)

Privacy-preserving Learning via Deep Net Pruning. - Yangsibo Huang; Yushan Su; Sachin Ravi; Zhao Song 002; Sanjeev Arora; Kai Li 001 (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)

MixCon: Adjusting the Separability of Data Representations for Harder Data Recovery. - Xiaoxiao Li; Yangsibo Huang; Binghui Peng; Zhao Song 002; Kai Li 001 (CoRR, 2020)

EMA: Auditing Data Removal from Trained Models. - Yangsibo Huang; Xiaoxiao Li; Kai Li 001 (MICCAI (5), 2021)

Evaluating Gradient Inversion Attacks and Defenses in Federated Learning. - Yangsibo Huang; Samyak Gupta; Zhao Song 002; Kai Li 001; Sanjeev Arora (NeurIPS, 2021)

Recovering Private Text in Federated Learning of Language Models. - Samyak Gupta; Yangsibo Huang; Zexuan Zhong; Tianyu Gao 001; Kai Li 001; Danqi Chen 001 (NeurIPS, 2022)

Privacy Implications of Retrieval-Based Language Models. - Yangsibo Huang; Samyak Gupta; Zexuan Zhong; Kai Li 001; Danqi Chen 001 (EMNLP, 2023)

Sparsity-Preserving Differentially Private Training of Large Embedding Models. - Badih Ghazi; Yangsibo Huang; Pritish Kamath; Ravi Kumar 001; Pasin Manurangsi; Amer Sinha; Chiyuan Zhang (NeurIPS, 2023)

kNN-Adapter: Efficient Domain Adaptation for Black-Box Language Models. - Yangsibo Huang; Daogao Liu; Zexuan Zhong; Weijia Shi; Yin Tat Lee (CoRR, 2023)

Challenges towards the Next Frontier in Privacy. - Rachel Cummings; Damien Desfontaines; David Evans 001; Roxana Geambasu; Matthew Jagielski; Yangsibo Huang; Peter Kairouz; Gautam Kamath 001; Sewoong Oh; Olga Ohrimenko; Nicolas Papernot; Ryan Rogers 002; et al. (CoRR, 2023)

Matching-based Data Valuation for Generative Model. - Jiaxi Yang; Wenglong Deng; Benlin Liu; Yangsibo Huang; Xiaoxiao Li (CoRR, 2023)

Learning across Data Owners with Joint Differential Privacy. - Yangsibo Huang; Haotian Jiang; Daogao Liu; Mohammad Mahdian; Jieming Mao; Vahab Mirrokni (CoRR, 2023)

A Dataset Auditing Method for Collaboratively Trained Machine Learning Models. - Yangsibo Huang; Chun-Yin Huang; Xiaoxiao Li; Kai Li 001 (IEEE Trans. Medical Imaging, 2023)

LabelDP-Pro: Learning with Label Differential Privacy via Projections. - Badih Ghazi; Yangsibo Huang; Pritish Kamath; Ravi Kumar 001; Pasin Manurangsi; Chiyuan Zhang (ICLR, 2024)

Catastrophic Jailbreak of Open-source LLMs via Exploiting Generation. - Yangsibo Huang; Samyak Gupta; Mengzhou Xia; Kai Li 001; Danqi Chen 001 (ICLR, 2024)

Detecting Pretraining Data from Large Language Models. - Weijia Shi; Anirudh Ajith; Mengzhou Xia; Yangsibo Huang; Daogao Liu; Terra Blevins; Danqi Chen 001; Luke Zettlemoyer (ICLR, 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 005; Weiyan Shi; et al. (ICML, 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 002 (ICML, 2024)

Evaluating Copyright Takedown Methods for Language Models. - Boyi Wei; Weijia Shi; Yangsibo Huang; Noah A. Smith; Chiyuan Zhang; Luke Zettlemoyer; Kai Li 001; Peter Henderson 002 (NeurIPS, 2024)

ConceptMix: A Compositional Image Generation Benchmark with Controllable Difficulty. - Xindi Wu; Dingli Yu; Yangsibo Huang; Olga Russakovsky; Sanjeev Arora (NeurIPS, 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 005; Weiyan Shi; et al. (CoRR, 2024)

AI Risk Management Should Incorporate Both Safety and Security. - Xiangyu Qi; Yangsibo Huang; Yi Zeng 005; Edoardo Debenedetti; Jonas Geiping; Luxi He; Kaixuan Huang; Udari Madhushani; Vikash Sehwag; Weijia Shi; Boyi Wei; Tinghao Xie; et al. (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)

SORRY-Bench: Systematically Evaluating Large Language Model Safety Refusal Behaviors. - Tinghao Xie; Xiangyu Qi; Yi Zeng 005; Yangsibo Huang; Udari Madhushani Sehwag; Kaixuan Huang; Luxi He; Boyi Wei; Dacheng Li; Ying Sheng 007; Ruoxi Jia 001; Bo Li 026; et al. (CoRR, 2024)

Crosslingual Capabilities and Knowledge Barriers in Multilingual Large Language Models. - Lynn Chua; Badih Ghazi; Yangsibo Huang; Pritish Kamath; Ravi Kumar 001; Pasin Manurangsi; Amer Sinha; Chulin Xie; Chiyuan Zhang (CoRR, 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; et al. (CoRR, 2024)

On Memorization of Large Language Models in Logical Reasoning. - Chulin Xie; Yangsibo Huang; Chiyuan Zhang; Da Yu; Xinyun Chen; Bill Yuchen Lin; Bo Li 026; Badih Ghazi; Ravi Kumar 001 (CoRR, 2024)

Machine Unlearning Doesn't Do What You Think: Lessons for Generative AI Policy, Research, and Practice. - A. Feder Cooper; Christopher A. Choquette-Choo; Miranda Bogen; Matthew Jagielski; Katja Filippova; Ken Ziyu Liu; Alexandra Chouldechova; Jamie Hayes; Yangsibo Huang; Niloofar Mireshghallah; Ilia Shumailov; Eleni Triantafillou; et al. (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 001; Peter Henderson 002 (ICLR, 2025)

Unlearn and Burn: Adversarial Machine Unlearning Requests Destroy Model Accuracy. - Yangsibo Huang; Daogao Liu; Lynn Chua; Badih Ghazi; Pritish Kamath; Ravi Kumar 001; Pasin Manurangsi; Milad Nasr; Amer Sinha; Chiyuan Zhang (ICLR, 2025)

On Evaluating the Durability of Safeguards for Open-Weight LLMs. - Xiangyu Qi; Boyi Wei; Nicholas Carlini; Yangsibo Huang; Tinghao Xie; Luxi He; Matthew Jagielski; Milad Nasr; Prateek Mittal; Peter Henderson 002 (ICLR, 2025)

MUSE: Machine Unlearning Six-Way Evaluation for Language Models. - Weijia Shi; Jaechan Lee; Yangsibo Huang; Sadhika Malladi; Jieyu Zhao 001; Ari Holtzman; Daogao Liu; Luke Zettlemoyer; Noah A. Smith; Chiyuan Zhang (ICLR, 2025)

SORRY-Bench: Systematically Evaluating Large Language Model Safety Refusal. - Tinghao Xie; Xiangyu Qi; Yi Zeng 005; Yangsibo Huang; Udari Madhushani Sehwag; Kaixuan Huang; Luxi He; Boyi Wei; Dacheng Li; Ying Sheng 007; Ruoxi Jia 001; Bo Li 026; et al. (ICLR, 2025)

GMValuator: Similarity-based Data Valuation for Generative Models. - Jiaxi Yang; Wenlong Deng; Benlin Liu; Yangsibo Huang; James Zou; Xiaoxiao Li (ICLR, 2025)

An Adversarial Perspective on Machine Unlearning for AI Safety. - Jakub Lucki; Boyi Wei; Yangsibo Huang; Peter Henderson 002; Florian Tramèr; Javier Rando (Trans. Mach. Learn. Res., 2025)

Exploring and Mitigating Adversarial Manipulation of Voting-Based Leaderboards. - Yangsibo Huang; Milad Nasr; Anastasios Angelopoulos; Nicholas Carlini; Wei-Lin Chiang; Christopher A. Choquette-Choo; Daphne Ippolito; Matthew Jagielski; Katherine Lee; Ken Ziyu Liu; Ion Stoica; Florian Tramèr; et al. (CoRR, 2025)

Scaling Laws for Differentially Private Language Models. - Ryan McKenna; Yangsibo Huang; Amer Sinha; Borja Balle; Zachary Charles; Christopher A. Choquette-Choo; Badih Ghazi; Georgios Kaissis; Ravi Kumar 001; Ruibo Liu; Da Yu; Chiyuan Zhang (CoRR, 2025)

Scaling Embedding Layers in Language Models. - Da Yu; Edith Cohen; Badih Ghazi; Yangsibo Huang; Pritish Kamath; Ravi Kumar 001; Daogao Liu; Chiyuan Zhang (CoRR, 2025)

MATH-Perturb: Benchmarking LLMs' Math Reasoning Abilities against Hard Perturbations. - Kaixuan Huang; Jiacheng Guo; Zihao Li; Xiang Ji; Jiawei Ge 003; Wenzhe Li; Yingqing Guo; Tianle Cai; Hui Yuan 002; Runzhe Wang; Yue Wu; Ming Yin; et al. (CoRR, 2025)

Quantifying Cross-Modality Memorization in Vision-Language Models. - Yuxin Wen; Yangsibo Huang; Tom Goldstein; Ravi Kumar 001; Badih Ghazi; Chiyuan Zhang (CoRR, 2025)