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2020 – today
- 2024
[c10]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, Tom Goldstein:
NEFTune: Noisy Embeddings Improve Instruction Finetuning. ICLR 2024- 2023
- [b1]Ping-Yeh Chiang:
Reliability of Machine Learning Models in the Real World. University of Maryland, College Park, MD, USA, 2023 - [c9]Ping-yeh Chiang, Renkun Ni, David Yu Miller, Arpit Bansal, Jonas Geiping, Micah Goldblum, Tom Goldstein:
Loss Landscapes are All You Need: Neural Network Generalization Can Be Explained Without the Implicit Bias of Gradient Descent. ICLR 2023 - [i17]Neel Jain, Avi Schwarzschild, Yuxin Wen, Gowthami Somepalli, John Kirchenbauer, Ping-yeh Chiang, Micah Goldblum, Aniruddha Saha, Jonas Geiping, Tom Goldstein:
Baseline Defenses for Adversarial Attacks Against Aligned Language Models. CoRR abs/2309.00614 (2023) - [i16]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, Tom Goldstein:
NEFTune: Noisy Embeddings Improve Instruction Finetuning. CoRR abs/2310.05914 (2023) - [i15]Ping-yeh Chiang, Yipin Zhou, Omid Poursaeed, Satya Narayan Shukla, Ashish Shah, Tom Goldstein, Ser-Nam Lim:
Universal Pyramid Adversarial Training for Improved ViT Performance. CoRR abs/2312.16339 (2023) - 2022
- [c8]Gowthami Somepalli, Liam Fowl, Arpit Bansal, Ping-Yeh Chiang, Yehuda Dar, Richard G. Baraniuk, Micah Goldblum, Tom Goldstein:
Can Neural Nets Learn the Same Model Twice? Investigating Reproducibility and Double Descent from the Decision Boundary Perspective. CVPR 2022: 13689-13698 - [c7]Arpit Bansal, Ping-Yeh Chiang, Michael J. Curry, Rajiv Jain, Curtis Wigington, Varun Manjunatha, John P. Dickerson, Tom Goldstein:
Certified Neural Network Watermarks with Randomized Smoothing. ICML 2022: 1450-1465 - [i14]Gowthami Somepalli, Liam Fowl, Arpit Bansal, Ping-Yeh Chiang, Yehuda Dar, Richard G. Baraniuk, Micah Goldblum, Tom Goldstein:
Can Neural Nets Learn the Same Model Twice? Investigating Reproducibility and Double Descent from the Decision Boundary Perspective. CoRR abs/2203.08124 (2022) - [i13]Arpit Bansal, Ping-yeh Chiang, Michael J. Curry, Rajiv Jain, Curtis Wigington, Varun Manjunatha, John P. Dickerson, Tom Goldstein:
Certified Neural Network Watermarks with Randomized Smoothing. CoRR abs/2207.07972 (2022) - [i12]Renkun Ni, Ping-yeh Chiang, Jonas Geiping, Micah Goldblum, Andrew Gordon Wilson, Tom Goldstein:
K-SAM: Sharpness-Aware Minimization at the Speed of SGD. CoRR abs/2210.12864 (2022) - 2021
- [c6]Renkun Ni, Hong-Min Chu, Oscar Castañeda, Ping-yeh Chiang, Christoph Studer, Tom Goldstein:
WrapNet: Neural Net Inference with Ultra-Low-Precision Arithmetic. ICLR 2021 - [c5]Liam Fowl, Micah Goldblum, Ping-yeh Chiang, Jonas Geiping, Wojciech Czaja, Tom Goldstein:
Adversarial Examples Make Strong Poisons. NeurIPS 2021: 30339-30351 - [i11]Liam Fowl, Ping-yeh Chiang, Micah Goldblum, Jonas Geiping, Arpit Bansal, Wojtek Czaja, Tom Goldstein:
Preventing Unauthorized Use of Proprietary Data: Poisoning for Secure Dataset Release. CoRR abs/2103.02683 (2021) - [i10]Liam Fowl, Micah Goldblum, Ping-yeh Chiang, Jonas Geiping, Wojtek Czaja, Tom Goldstein:
Adversarial Examples Make Strong Poisons. CoRR abs/2106.10807 (2021) - [i9]Zeyad Ali Sami Emam, Hong-Min Chu, Ping-Yeh Chiang, Wojciech Czaja, Richard Leapman, Micah Goldblum, Tom Goldstein:
Active Learning at the ImageNet Scale. CoRR abs/2111.12880 (2021) - 2020
- [c4]Ping-Yeh Chiang, Jonas Geiping, Micah Goldblum, Tom Goldstein, Renkun Ni, Steven Reich, Ali Shafahi:
Witchcraft: Efficient PGD Attacks with Random Step Size. ICASSP 2020: 3747-3751 - [c3]Ping-yeh Chiang, Renkun Ni, Ahmed Abdelkader, Chen Zhu, Christoph Studer, Tom Goldstein:
Certified Defenses for Adversarial Patches. ICLR 2020 - [c2]Ping-yeh Chiang, Michael J. Curry, Ahmed Abdelkader, Aounon Kumar, John Dickerson, Tom Goldstein:
Detection as Regression: Certified Object Detection with Median Smoothing. NeurIPS 2020 - [c1]Michael J. Curry, Ping-Yeh Chiang, Tom Goldstein, John Dickerson:
Certifying Strategyproof Auction Networks. NeurIPS 2020 - [i8]Chen Zhu, Renkun Ni, Ping-Yeh Chiang, Hengduo Li, Furong Huang, Tom Goldstein:
Improving the Tightness of Convex Relaxation Bounds for Training Certifiably Robust Classifiers. CoRR abs/2002.09766 (2020) - [i7]Ping-yeh Chiang, Renkun Ni, Ahmed Abdelkader, Chen Zhu, Christoph Studer, Tom Goldstein:
Certified Defenses for Adversarial Patches. CoRR abs/2003.06693 (2020) - [i6]Michael J. Curry, Ping-Yeh Chiang, Tom Goldstein, John P. Dickerson:
Certifying Strategyproof Auction Networks. CoRR abs/2006.08742 (2020) - [i5]Ping-yeh Chiang, Michael J. Curry, Ahmed Abdelkader, Aounon Kumar, John P. Dickerson, Tom Goldstein:
Detection as Regression: Certified Object Detection by Median Smoothing. CoRR abs/2007.03730 (2020) - [i4]Renkun Ni, Hong-Min Chu, Oscar Castañeda, Ping-yeh Chiang, Christoph Studer, Tom Goldstein:
WrapNet: Neural Net Inference with Ultra-Low-Resolution Arithmetic. CoRR abs/2007.13242 (2020) - [i3]Kevin Kuo, Anthony Ostuni, Elizabeth Horishny, Michael J. Curry, Samuel Dooley, Ping-yeh Chiang, Tom Goldstein, John P. Dickerson:
ProportionNet: Balancing Fairness and Revenue for Auction Design with Deep Learning. CoRR abs/2010.06398 (2020)
2010 – 2019
- 2019
- [i2]Angeline Aguinaldo, Ping-Yeh Chiang, Alexander Gain, Ameya Patil, Kolten Pearson, Soheil Feizi:
Compressing GANs using Knowledge Distillation. CoRR abs/1902.00159 (2019) - [i1]Ping-Yeh Chiang, Jonas Geiping, Micah Goldblum, Tom Goldstein, Renkun Ni, Steven Reich, Ali Shafahi:
WITCHcraft: Efficient PGD attacks with random step size. CoRR abs/1911.07989 (2019)
Coauthor Index
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