Transferable Clean-Label Poisoning Attacks on Deep Neural Nets. - Chen Zhu 001; W. Ronny Huang; Hengduo Li; Gavin Taylor; Christoph Studer; Tom Goldstein (ICML, 2019)

Transferable Clean-Label Poisoning Attacks on Deep Neural Nets. - Chen Zhu 001; W. Ronny Huang; Ali Shafahi; Hengduo Li; Gavin Taylor; Christoph Studer; Tom Goldstein (CoRR, 2019)

FreeLB: Enhanced Adversarial Training for Language Understanding. - Chen Zhu 001; Yu Cheng 001; Zhe Gan; Siqi Sun; Tom Goldstein; Jingjing Liu 001 (CoRR, 2019)

Strong Baseline Defenses Against Clean-Label Poisoning Attacks. - Neal Gupta; W. Ronny Huang; Liam Fowl; Chen Zhu 001; Soheil Feizi; Tom Goldstein; John P. Dickerson (CoRR, 2019)

Learning From Noisy Anchors for One-Stage Object Detection. - Hengduo Li; Zuxuan Wu; Chen Zhu 001; Caiming Xiong; Richard Socher; Larry S. Davis (CVPR, 2020)

Deep k-NN Defense Against Clean-Label Data Poisoning Attacks. - Neehar Peri; Neal Gupta; W. Ronny Huang; Liam Fowl; Chen Zhu 001; Soheil Feizi; Tom Goldstein; John P. Dickerson (ECCV Workshops (1), 2020)

Headless Horseman: Adversarial Attacks on Transfer Learning Models. - Ahmed Abdelkader; Michael J. Curry; Liam Fowl; Tom Goldstein; Avi Schwarzschild; Manli Shu; Christoph Studer; Chen Zhu 001 (ICASSP, 2020)

Certified Defenses for Adversarial Patches. - Ping-yeh Chiang; Renkun Ni; Ahmed Abdelkader; Chen Zhu 001; Christoph Studer; Tom Goldstein (ICLR, 2020)

Adversarially robust transfer learning. - Ali Shafahi; Parsa Saadatpanah; Chen Zhu 001; Amin Ghiasi; Christoph Studer; David W. Jacobs; Tom Goldstein (ICLR, 2020)

FreeLB: Enhanced Adversarial Training for Natural Language Understanding. - Chen Zhu 001; Yu Cheng 001; Zhe Gan; Siqi Sun; Tom Goldstein; Jingjing Liu 001 (ICLR, 2020)

Large-Scale Adversarial Training for Vision-and-Language Representation Learning. - Zhe Gan; Yen-Chun Chen 001; Linjie Li; Chen Zhu 001; Yu Cheng 001; Jingjing Liu 001 (NeurIPS, 2020)

Improving the Tightness of Convex Relaxation Bounds for Training Certifiably Robust Classifiers. - Chen Zhu 001; Renkun Ni; Ping-Yeh Chiang; Hengduo Li; Furong Huang; Tom Goldstein (CoRR, 2020)

Adaptive Learning Rates with Maximum Variation Averaging. - Chen Zhu 001; Yu Cheng 001; Zhe Gan; Furong Huang; Jingjing Liu 001; Tom Goldstein (CoRR, 2020)

Towards Accurate Quantization and Pruning via Data-free Knowledge Transfer. - Chen Zhu 001; Zheng Xu 002; Ali Shafahi; Manli Shu; Amin Ghiasi; Tom Goldstein (CoRR, 2020)

FLAG: Adversarial Data Augmentation for Graph Neural Networks. - Kezhi Kong; Guohao Li 001; Mucong Ding; Zuxuan Wu; Chen Zhu 001; Bernard Ghanem; Gavin Taylor; Tom Goldstein (CoRR, 2020)

Modifying Memories in Transformer Models. - Chen Zhu 001; Ankit Singh Rawat; Manzil Zaheer; Srinadh Bhojanapalli; Daliang Li; Felix X. Yu; Sanjiv Kumar (CoRR, 2020)

Are Adversarial Examples Created Equal? A Learnable Weighted Minimax Risk for Robustness under Non-uniform Attacks. - Huimin Zeng 001; Chen Zhu 001; Tom Goldstein; Furong Huang (AAAI, 2021)

The Intrinsic Dimension of Images and Its Impact on Learning. - Phillip Pope; Chen Zhu 001; Ahmed Abdelkader; Micah Goldblum; Tom Goldstein (ICLR, 2021)

VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector Quantization. - Mucong Ding; Kezhi Kong; Jingling Li; Chen Zhu 001; John Dickerson 001; Furong Huang; Tom Goldstein (NeurIPS, 2021)

GradInit: Learning to Initialize Neural Networks for Stable and Efficient Training. - Chen Zhu 001; Renkun Ni; Zheng Xu 002; Kezhi Kong; W. Ronny Huang; Tom Goldstein (NeurIPS, 2021)

Long-Short Transformer: Efficient Transformers for Language and Vision. - Chen Zhu 001; Wei Ping; Chaowei Xiao; Mohammad Shoeybi; Tom Goldstein; Anima Anandkumar; Bryan Catanzaro (NeurIPS, 2021)

MaxVA: Fast Adaptation of Step Sizes by Maximizing Observed Variance of Gradients. - Chen Zhu 001; Yu Cheng 001; Zhe Gan; Furong Huang; Jingjing Liu 001; Tom Goldstein (ECML/PKDD (3), 2021)

A Field Guide to Federated Optimization. - Jianyu Wang; Zachary Charles; Zheng Xu 002; Gauri Joshi; H. Brendan McMahan; Blaise Agüera y Arcas; Maruan Al-Shedivat; Galen Andrew; Salman Avestimehr; Katharine Daly; Deepesh Data; Suhas N. Diggavi; et al. (CoRR, 2021)

Robust Optimization as Data Augmentation for Large-scale Graphs. - Kezhi Kong; Guohao Li 001; Mucong Ding; Zuxuan Wu; Chen Zhu 001; Bernard Ghanem; Gavin Taylor; Tom Goldstein (CVPR, 2022)

Diurnal or Nocturnal? Federated Learning of Multi-branch Networks from Periodically Shifting Distributions. - Chen Zhu 001; Zheng Xu 002; Mingqing Chen; Jakub Konecný; Andrew Hard; Tom Goldstein (ICLR, 2022)

Plug-In Inversion: Model-Agnostic Inversion for Vision with Data Augmentations. - Amin Ghiasi; Hamid Kazemi; Steven Reich; Chen Zhu 001; Micah Goldblum; Tom Goldstein (ICML, 2022)

Pre-Train Your Loss: Easy Bayesian Transfer Learning with Informative Priors. - Ravid Shwartz-Ziv; Micah Goldblum; Hossein Souri; Sanyam Kapoor; Chen Zhu 001; Yann LeCun; Andrew Gordon Wilson (NeurIPS, 2022)

Towards Reliable and Efficient Representation Learning. - Chen Zhu 001 (2022)

How Many Demonstrations Do You Need for In-context Learning? - Jiuhai Chen; Lichang Chen; Chen Zhu 001; Tianyi Zhou 001 (EMNLP (Findings), 2023)

On the Exploitability of Instruction Tuning. - Manli Shu; Jiongxiao Wang; Chen Zhu 001; Jonas Geiping; Chaowei Xiao; Tom Goldstein (NeurIPS, 2023)

It Takes One to Tango but More Make Trouble? The Number of Demonstrations Needed for In-Context Learning. - Jiuhai Chen; Lichang Chen; Chen Zhu 001; Tianyi Zhou 001 (CoRR, 2023)

Retrieval meets Long Context Large Language Models. - Peng Xu 008; Wei Ping; Xianchao Wu; Lawrence McAfee; Chen Zhu 001; Zihan Liu 001; Sandeep Subramanian; Evelina Bakhturina; Mohammad Shoeybi; Bryan Catanzaro (ICLR, 2024)

ODIN: Disentangled Reward Mitigates Hacking in RLHF. - Lichang Chen; Chen Zhu 001; Jiuhai Chen; Davit Soselia; Tianyi Zhou 001; Tom Goldstein; Heng Huang; Mohammad Shoeybi; Bryan Catanzaro (ICML, 2024)

Nemotron-4 15B Technical Report. - Jupinder Parmar; Shrimai Prabhumoye; Joseph Jennings; Mostofa Patwary; Sandeep Subramanian; Dan Su 003; Chen Zhu 001; Deepak Narayanan; Aastha Jhunjhunwala; Ayush Dattagupta; Vibhu Jawa; Jiwei Liu; et al. (CoRR, 2024)

OPTune: Efficient Online Preference Tuning. - Lichang Chen; Jiuhai Chen; Chenxi Liu; John Kirchenbauer; Davit Soselia; Chen Zhu 001; Tom Goldstein; Tianyi Zhou 001; Heng Huang (CoRR, 2024)

Nemotron-4 340B Technical Report. - Bo Adler; Niket Agarwal; Ashwath Aithal; Dong H. Anh; Pallab Bhattacharya; Annika Brundyn; Jared Casper; Bryan Catanzaro; Sharon Clay; Jonathan M. Cohen; Sirshak Das; Ayush Dattagupta; et al. (CoRR, 2024)

Refusal Tokens: A Simple Way to Calibrate Refusals in Large Language Models. - Neel Jain; Aditya Shrivastava; Chen Zhu 001; Daben Liu; Alfy Samuel; Ashwinee Panda; Anoop Kumar; Micah Goldblum; Tom Goldstein (CoRR, 2024)