Compressing GANs using Knowledge Distillation. - Angeline Aguinaldo; Ping-Yeh Chiang; Alexander Gain; Ameya Patil; Kolten Pearson; Soheil Feizi (CoRR, 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)

Detection as Regression: Certified Object Detection with Median Smoothing. - Ping-yeh Chiang; Michael J. Curry; Ahmed Abdelkader; Aounon Kumar; John Dickerson 001; Tom Goldstein (NeurIPS, 2020)

Certifying Strategyproof Auction Networks. - Michael J. Curry; Ping-Yeh Chiang; Tom Goldstein; John Dickerson 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)

Detection as Regression: Certified Object Detection by Median Smoothing. - Ping-yeh Chiang; Michael J. Curry; Ahmed Abdelkader; Aounon Kumar; John P. Dickerson; 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)

ProportionNet: Balancing Fairness and Revenue for Auction Design with Deep Learning. - Kevin Kuo; Anthony Ostuni; Elizabeth Horishny; Michael J. Curry; Samuel Dooley; Ping-yeh Chiang; Tom Goldstein; John P. Dickerson (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)

Adversarial Examples Make Strong Poisons. - Liam Fowl; Micah Goldblum; Ping-yeh Chiang; Jonas Geiping; Wojciech Czaja; Tom Goldstein (NeurIPS, 2021)

Preventing Unauthorized Use of Proprietary Data: Poisoning for Secure Dataset Release. - Liam Fowl; Ping-yeh Chiang; Micah Goldblum; Jonas Geiping; Arpit Bansal; Wojtek Czaja; Tom Goldstein (CoRR, 2021)

Active Learning at the ImageNet Scale. - Zeyad Ali Sami Emam; Hong-Min Chu; Ping-Yeh Chiang; Wojciech Czaja; Richard Leapman; Micah Goldblum; Tom Goldstein (CoRR, 2021)

Can Neural Nets Learn the Same Model Twice? Investigating Reproducibility and Double Descent from the Decision Boundary Perspective. - Gowthami Somepalli; Liam Fowl; Arpit Bansal; Ping-Yeh Chiang; Yehuda Dar; Richard G. Baraniuk; Micah Goldblum; Tom Goldstein (CVPR, 2022)

Certified Neural Network Watermarks with Randomized Smoothing. - Arpit Bansal; Ping-Yeh Chiang; Michael J. Curry; Rajiv Jain; Curtis Wigington; Varun Manjunatha; John P. Dickerson; Tom Goldstein (ICML, 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)

Baseline Defenses for Adversarial Attacks Against Aligned Language Models. - Neel Jain; Avi Schwarzschild; Yuxin Wen; Gowthami Somepalli; John Kirchenbauer; Ping-yeh Chiang; Micah Goldblum; Aniruddha Saha; Jonas Geiping; Tom Goldstein (CoRR, 2023)

Universal Pyramid Adversarial Training for Improved ViT Performance. - Ping-yeh Chiang; Yipin Zhou; Omid Poursaeed; Satya Narayan Shukla; Ashish Shah; Tom Goldstein; Ser-Nam Lim (CoRR, 2023)

Reliability of Machine Learning Models in the Real World. - Ping-Yeh Chiang (2023)

NEFTune: Noisy Embeddings Improve Instruction Finetuning. - Neel Jain; Ping-yeh Chiang; Yuxin Wen; John Kirchenbauer; Hong-Min Chu; Gowthami Somepalli; Brian R. Bartoldson; Bhavya Kailkhura; Avi Schwarzschild; Aniruddha Saha; Micah Goldblum; Jonas Geiping; et al. (ICLR, 2024)