Optimal Statistical and Computational Rates for One Bit Matrix Completion. - Renkun Ni; Quanquan Gu (AISTATS, 2016)

Learning Accurate Low-Bit Deep Neural Networks with Stochastic Quantization. - Yinpeng Dong; Jianguo Li; Renkun Ni (BMVC, 2017)

Learning Accurate Low-Bit Deep Neural Networks with Stochastic Quantization. - Yinpeng Dong; Renkun Ni; Jianguo Li; Yurong Chen 001; Jun Zhu 001; Hang Su 006 (CoRR, 2017)

Stochastic Quantization for Learning Accurate Low-Bit Deep Neural Networks. - Yinpeng Dong; Renkun Ni; Jianguo Li; Yurong Chen 001; Hang Su 006; Jun Zhu 001 (Int. J. Comput. Vis., 2019)

Witchcraft: Efficient PGD Attacks with Random Step Size. - Ping-Yeh Chiang; Jonas Geiping; Micah Goldblum; Tom Goldstein; Renkun Ni; Steven Reich; Ali Shafahi (ICASSP, 2020)

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

Unraveling Meta-Learning: Understanding Feature Representations for Few-Shot Tasks. - Micah Goldblum; Steven Reich; Liam Fowl; Renkun Ni; Valeriia Cherepanova; Tom Goldstein (ICML, 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)

WrapNet: Neural Net Inference with Ultra-Low-Resolution Arithmetic. - Renkun Ni; Hong-Min Chu; Oscar Castañeda; Ping-yeh Chiang; Christoph Studer; Tom Goldstein (CoRR, 2020)

WrapNet: Neural Net Inference with Ultra-Low-Precision Arithmetic. - Renkun Ni; Hong-Min Chu; Oscar Castañeda; Ping-yeh Chiang; Christoph Studer; Tom Goldstein (ICLR, 2021)

Data Augmentation for Meta-Learning. - Renkun Ni; Micah Goldblum; Amr Sharaf; Kezhi Kong; Tom Goldstein (ICML, 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)

Lung Nodule Malignancy Prediction in Sequential CT Scans: Summary of ISBI 2018 Challenge. - Yoganand Balagurunathan; Andrew Beers; Michael F. McNitt-Gray; Lubomir M. Hadjiiski; Sandy Napel; Dmitry B. Goldgof; Gustavo Pérez; Pablo Arbeláez; Alireza Mehrtash; Tina Kapur; Ehwa Yang; Jung Won Moon; et al. (IEEE Trans. Medical Imaging, 2021)

Promoting Fairness in Learned Models by Learning to Active Learn under Parity Constraints. - Amr Sharaf; Hal Daumé III; Renkun Ni (FAccT, 2022)

The Close Relationship Between Contrastive Learning and Meta-Learning. - Renkun Ni; Manli Shu; Hossein Souri; Micah Goldblum; Tom Goldstein (ICLR, 2022)

K-SAM: Sharpness-Aware Minimization at the Speed of SGD. - Renkun Ni; Ping-yeh Chiang; Jonas Geiping; Micah Goldblum; Andrew Gordon Wilson; Tom Goldstein (CoRR, 2022)

Loss Landscapes are All You Need: Neural Network Generalization Can Be Explained Without the Implicit Bias of Gradient Descent. - Ping-yeh Chiang; Renkun Ni; David Yu Miller; Arpit Bansal; Jonas Geiping; Micah Goldblum; Tom Goldstein (ICLR, 2023)

GOAT: A Global Transformer on Large-scale Graphs. - Kezhi Kong; Jiuhai Chen; John Kirchenbauer; Renkun Ni; C. Bayan Bruss; Tom Goldstein (ICML, 2023)

Battle of the Backbones: A Large-Scale Comparison of Pretrained Models across Computer Vision Tasks. - Micah Goldblum; Hossein Souri; Renkun Ni; Manli Shu; Viraj Prabhu; Gowthami Somepalli; Prithvijit Chattopadhyay; Mark Ibrahim; Adrien Bardes; Judy Hoffman; Rama Chellappa; Andrew Gordon Wilson; et al. (NeurIPS, 2023)

Improving Model and Data Efficiency for Deep Learning. - Renkun Ni (2023)

FedAQT: Accurate Quantized Training with Federated Learning. - Renkun Ni; Yonghui Xiao; Phoenix Meadowlark; Oleg Rybakov; Tom Goldstein; Ananda Theertha Suresh; Ignacio López-Moreno; Mingqing Chen; Rajiv Mathews (ICASSP, 2024)