Chen Zhu - RankMe

51 papers A* 18Misc 1Journal 28Unranked 3

Year	Rank	Type	Title / Venue / Authors
2024	J	`jnl`	Nemotron-4 15B Technical Report. CoRR Jupinder Parmar, Shrimai Prabhumoye, Joseph Jennings, Mostofa Patwary, Sandeep Subramanian, Dan Su, Chen Zhu, Deepak Narayanan, Aastha Jhunjhunwala, Ayush Dattagupta, Vibhu Jawa, Jiwei Liu, Ameya Mahabaleshwarkar, Osvald Nitski, Annika Brundyn, James Maki, Miguel Martinez, Jiaxuan You, John Kamalu, Patrick LeGresley, Denys Fridman, Jared Casper, Ashwath Aithal, Oleksii Kuchaiev, Mohammad Shoeybi, Jonathan M. Cohen, Bryan Catanzaro
2024	A*	`conf`	ODIN: Disentangled Reward Mitigates Hacking in RLHF. ICML Lichang Chen, Chen Zhu, Jiuhai Chen, Davit Soselia, Tianyi Zhou, Tom Goldstein, Heng Huang, Mohammad Shoeybi, Bryan Catanzaro
2024	J	`jnl`	ODIN: Disentangled Reward Mitigates Hacking in RLHF. CoRR Lichang Chen, Chen Zhu, Davit Soselia, Jiuhai Chen, Tianyi Zhou, Tom Goldstein, Heng Huang, Mohammad Shoeybi, Bryan Catanzaro
2024	J	`jnl`	OPTune: Efficient Online Preference Tuning. CoRR Lichang Chen, Jiuhai Chen, Chenxi Liu, John Kirchenbauer, Davit Soselia, Chen Zhu, Tom Goldstein, Tianyi Zhou, Heng Huang
2024	J	`jnl`	Refusal Tokens: A Simple Way to Calibrate Refusals in Large Language Models. CoRR Neel Jain, Aditya Shrivastava, Chen Zhu, Daben Liu, Alfy Samuel, Ashwinee Panda, Anoop Kumar, Micah Goldblum, Tom Goldstein
2024	A*	`conf`	Retrieval meets Long Context Large Language Models. ICLR Peng Xu, Wei Ping, Xianchao Wu, Lawrence McAfee, Chen Zhu, Zihan Liu, Sandeep Subramanian, Evelina Bakhturina, Mohammad Shoeybi, Bryan Catanzaro
2023	—	`conf`	How Many Demonstrations Do You Need for In-context Learning? EMNLP (Findings) Jiuhai Chen, Lichang Chen, Chen Zhu, Tianyi Zhou
2023	J	`jnl`	It Takes One to Tango but More Make Trouble? The Number of Demonstrations Needed for In-Context Learning. CoRR Jiuhai Chen, Lichang Chen, Chen Zhu, Tianyi Zhou
2023	A*	`conf`	On the Exploitability of Instruction Tuning. NeurIPS Manli Shu, Jiongxiao Wang, Chen Zhu, Jonas Geiping, Chaowei Xiao, Tom Goldstein
2023	J	`jnl`	On the Exploitability of Instruction Tuning. CoRR Manli Shu, Jiongxiao Wang, Chen Zhu, Jonas Geiping, Chaowei Xiao, Tom Goldstein
2023	J	`jnl`	Retrieval meets Long Context Large Language Models. CoRR Peng Xu, Wei Ping, Xianchao Wu, Lawrence McAfee, Chen Zhu, Zihan Liu, Sandeep Subramanian, Evelina Bakhturina, Mohammad Shoeybi, Bryan Catanzaro
2022	A*	`conf`	Diurnal or Nocturnal? Federated Learning of Multi-branch Networks from Periodically Shifting Distributions. ICLR Chen Zhu, Zheng Xu, Mingqing Chen, Jakub Konecný, Andrew Hard, Tom Goldstein
2022	A*	`conf`	Plug-In Inversion: Model-Agnostic Inversion for Vision with Data Augmentations. ICML Amin Ghiasi, Hamid Kazemi, Steven Reich, Chen Zhu, Micah Goldblum, Tom Goldstein
2022	J	`jnl`	Plug-In Inversion: Model-Agnostic Inversion for Vision with Data Augmentations. CoRR Amin Ghiasi, Hamid Kazemi, Steven Reich, Chen Zhu, Micah Goldblum, Tom Goldstein
2022	A*	`conf`	Pre-Train Your Loss: Easy Bayesian Transfer Learning with Informative Priors. NeurIPS Ravid Shwartz-Ziv, Micah Goldblum, Hossein Souri, Sanyam Kapoor, Chen Zhu, Yann LeCun, Andrew Gordon Wilson
2022	J	`jnl`	Pre-Train Your Loss: Easy Bayesian Transfer Learning with Informative Priors. CoRR Ravid Shwartz-Ziv, Micah Goldblum, Hossein Souri, Sanyam Kapoor, Chen Zhu, Yann LeCun, Andrew Gordon Wilson
2022	A*	`conf`	Robust Optimization as Data Augmentation for Large-scale Graphs. CVPR Kezhi Kong, Guohao Li, Mucong Ding, Zuxuan Wu, Chen Zhu, Bernard Ghanem, Gavin Taylor, Tom Goldstein
2022	—	`—`	Towards Reliable and Efficient Representation Learning. Chen Zhu
2021	J	`jnl`	A Field Guide to Federated Optimization. CoRR Jianyu Wang, Zachary Charles, Zheng Xu, Gauri Joshi, H. Brendan McMahan, Blaise Agüera y Arcas, Maruan Al-Shedivat, Galen Andrew, Salman Avestimehr, Katharine Daly, Deepesh Data, Suhas N. Diggavi, Hubert Eichner, Advait Gadhikar, Zachary Garrett, Antonious M. Girgis, Filip Hanzely, Andrew Hard, Chaoyang He, Samuel Horváth, Zhouyuan Huo, Alex Ingerman, Martin Jaggi, Tara Javidi, Peter Kairouz, Satyen Kale, Sai Praneeth Karimireddy, Jakub Konecný, Sanmi Koyejo, Tian Li, Luyang Liu, Mehryar Mohri, Hang Qi, Sashank J. Reddi, Peter Richtárik, Karan Singhal, Virginia Smith, Mahdi Soltanolkotabi, Weikang Song, Ananda Theertha Suresh, Sebastian U. Stich, Ameet Talwalkar, Hongyi Wang, Blake E. Woodworth, Shanshan Wu, Felix X. Yu, Honglin Yuan, Manzil Zaheer, Mi Zhang, Tong Zhang, Chunxiang Zheng, Chen Zhu, Wennan Zhu
2021	A*	`conf`	Are Adversarial Examples Created Equal? A Learnable Weighted Minimax Risk for Robustness under Non-uniform Attacks. AAAI Huimin Zeng, Chen Zhu, Tom Goldstein, Furong Huang
2021	A*	`conf`	GradInit: Learning to Initialize Neural Networks for Stable and Efficient Training. NeurIPS Chen Zhu, Renkun Ni, Zheng Xu, Kezhi Kong, W. Ronny Huang, Tom Goldstein
2021	J	`jnl`	GradInit: Learning to Initialize Neural Networks for Stable and Efficient Training. CoRR Chen Zhu, Renkun Ni, Zheng Xu, Kezhi Kong, W. Ronny Huang, Tom Goldstein
2021	A*	`conf`	Long-Short Transformer: Efficient Transformers for Language and Vision. NeurIPS Chen Zhu, Wei Ping, Chaowei Xiao, Mohammad Shoeybi, Tom Goldstein, Anima Anandkumar, Bryan Catanzaro
2021	J	`jnl`	Long-Short Transformer: Efficient Transformers for Language and Vision. CoRR Chen Zhu, Wei Ping, Chaowei Xiao, Mohammad Shoeybi, Tom Goldstein, Anima Anandkumar, Bryan Catanzaro
2021	—	`conf`	MaxVA: Fast Adaptation of Step Sizes by Maximizing Observed Variance of Gradients. ECML/PKDD (3) Chen Zhu, Yu Cheng, Zhe Gan, Furong Huang, Jingjing Liu, Tom Goldstein
2021	A*	`conf`	The Intrinsic Dimension of Images and Its Impact on Learning. ICLR Phillip Pope, Chen Zhu, Ahmed Abdelkader, Micah Goldblum, Tom Goldstein
2021	J	`jnl`	The Intrinsic Dimension of Images and Its Impact on Learning. CoRR Phillip Pope, Chen Zhu, Ahmed Abdelkader, Micah Goldblum, Tom Goldstein
2021	A*	`conf`	VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector Quantization. NeurIPS Mucong Ding, Kezhi Kong, Jingling Li, Chen Zhu, John Dickerson, Furong Huang, Tom Goldstein
2021	J	`jnl`	VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector Quantization. CoRR Mucong Ding, Kezhi Kong, Jingling Li, Chen Zhu, John P. Dickerson, Furong Huang, Tom Goldstein
2020	J	`jnl`	Adaptive Learning Rates with Maximum Variation Averaging. CoRR Chen Zhu, Yu Cheng, Zhe Gan, Furong Huang, Jingjing Liu, Tom Goldstein
2020	A*	`conf`	Adversarially robust transfer learning. ICLR Ali Shafahi, Parsa Saadatpanah, Chen Zhu, Amin Ghiasi, Christoph Studer, David W. Jacobs, Tom Goldstein
2020	J	`jnl`	Are Adversarial Examples Created Equal? A Learnable Weighted Minimax Risk for Robustness under Non-uniform Attacks. CoRR Huimin Zeng, Chen Zhu, Tom Goldstein, Furong Huang
2020	A*	`conf`	Certified Defenses for Adversarial Patches. ICLR Ping-Yeh Chiang, Renkun Ni, Ahmed Abdelkader, Chen Zhu, Christoph Studer, Tom Goldstein
2020	J	`jnl`	Certified Defenses for Adversarial Patches. CoRR Ping-Yeh Chiang, Renkun Ni, Ahmed Abdelkader, Chen Zhu, Christoph Studer, Tom Goldstein
2020	—	`conf`	Deep k-NN Defense Against Clean-Label Data Poisoning Attacks. ECCV Workshops (1) Neehar Peri, Neal Gupta, W. Ronny Huang, Liam Fowl, Chen Zhu, Soheil Feizi, Tom Goldstein, John P. Dickerson
2020	J	`jnl`	FLAG: Adversarial Data Augmentation for Graph Neural Networks. CoRR Kezhi Kong, Guohao Li, Mucong Ding, Zuxuan Wu, Chen Zhu, Bernard Ghanem, Gavin Taylor, Tom Goldstein
2020	A*	`conf`	FreeLB: Enhanced Adversarial Training for Natural Language Understanding. ICLR Chen Zhu, Yu Cheng, Zhe Gan, Siqi Sun, Tom Goldstein, Jingjing Liu
2020	Misc	`conf`	Headless Horseman: Adversarial Attacks on Transfer Learning Models. ICASSP Ahmed Abdelkader, Michael J. Curry, Liam Fowl, Tom Goldstein, Avi Schwarzschild, Manli Shu, Christoph Studer, Chen Zhu
2020	J	`jnl`	Headless Horseman: Adversarial Attacks on Transfer Learning Models. CoRR Ahmed Abdelkader, Michael J. Curry, Liam Fowl, Tom Goldstein, Avi Schwarzschild, Manli Shu, Christoph Studer, Chen Zhu
2020	J	`jnl`	Improving the Tightness of Convex Relaxation Bounds for Training Certifiably Robust Classifiers. CoRR Chen Zhu, Renkun Ni, Ping-Yeh Chiang, Hengduo Li, Furong Huang, Tom Goldstein
2020	A*	`conf`	Large-Scale Adversarial Training for Vision-and-Language Representation Learning. NeurIPS Zhe Gan, Yen-Chun Chen, Linjie Li, Chen Zhu, Yu Cheng, Jingjing Liu
2020	J	`jnl`	Large-Scale Adversarial Training for Vision-and-Language Representation Learning. CoRR Zhe Gan, Yen-Chun Chen, Linjie Li, Chen Zhu, Yu Cheng, Jingjing Liu
2020	A*	`conf`	Learning From Noisy Anchors for One-Stage Object Detection. CVPR Hengduo Li, Zuxuan Wu, Chen Zhu, Caiming Xiong, Richard Socher, Larry S. Davis
2020	J	`jnl`	Modifying Memories in Transformer Models. CoRR Chen Zhu, Ankit Singh Rawat, Manzil Zaheer, Srinadh Bhojanapalli, Daliang Li, Felix X. Yu, Sanjiv Kumar
2020	J	`jnl`	Towards Accurate Quantization and Pruning via Data-free Knowledge Transfer. CoRR Chen Zhu, Zheng Xu, Ali Shafahi, Manli Shu, Amin Ghiasi, Tom Goldstein
2019	J	`jnl`	Adversarially robust transfer learning. CoRR Ali Shafahi, Parsa Saadatpanah, Chen Zhu, Amin Ghiasi, Christoph Studer, David W. Jacobs, Tom Goldstein
2019	J	`jnl`	FreeLB: Enhanced Adversarial Training for Language Understanding. CoRR Chen Zhu, Yu Cheng, Zhe Gan, Siqi Sun, Tom Goldstein, Jingjing Liu
2019	J	`jnl`	Learning from Noisy Anchors for One-stage Object Detection. CoRR Hengduo Li, Zuxuan Wu, Chen Zhu, Caiming Xiong, Richard Socher, Larry S. Davis
2019	J	`jnl`	Strong Baseline Defenses Against Clean-Label Poisoning Attacks. CoRR Neal Gupta, W. Ronny Huang, Liam Fowl, Chen Zhu, Soheil Feizi, Tom Goldstein, John P. Dickerson
2019	A*	`conf`	Transferable Clean-Label Poisoning Attacks on Deep Neural Nets. ICML Chen Zhu, W. Ronny Huang, Hengduo Li, Gavin Taylor, Christoph Studer, Tom Goldstein
2019	J	`jnl`	Transferable Clean-Label Poisoning Attacks on Deep Neural Nets. CoRR Chen Zhu, W. Ronny Huang, Ali Shafahi, Hengduo Li, Gavin Taylor, Christoph Studer, Tom Goldstein