Goldstein, Tom

Adversarial training for free

Shafahi, A., Najibi, M., Ghiasi, A., Xu, Z., Dickerson, J., Studer, C., Davis, L. S., Taylor, G., & Goldstein, T. (2019) · ETH Zurich

Adversarial Robustness in Machine LearningSecurity and Verification in Computing

Cited by 580

Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defenses

Goldblum, M., Tsipras, D., Xie, C., Chen, X., Schwarzschild, A., Song, D., Madry, A., Li, B., & Goldstein, T. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence

Adversarial Robustness in Machine LearningPrivacy-Preserving Technologies in Data

Cited by 362

A Watermark for Large Language Models

Kirchenbauer, J., Geiping, J., Wen, Y., Katz, J., Miers, I., & Goldstein, T. (2023) · arXiv

Advanced Steganography and Watermarking TechniquesAdversarial Robustness in Machine Learning

Cited by 194

Certified Data Removal from Machine Learning Models

Guo, C., Goldstein, T., Hannun, A., & van der Maaten, L. (2019) · arXiv

Privacy-Preserving Technologies in DataCryptography and Data Security

Cited by 160

Adversarial attacks on Copyright Detection Systems

Saadatpanah, P., Shafahi, A., & Goldstein, T. (2019) · arXiv

Adversarial Robustness in Machine LearningDigital Media Forensic Detection

Cited by 36

Spotting LLMs With Binoculars: Zero-Shot Detection of Machine-Generated Text

Hans, A., Schwarzschild, A., Cherepanova, V., Kazemi, H., Saha, A., Goldblum, M., Geiping, J., & Goldstein, T. (2024) · arXiv

Authorship Attribution and ProfilingDigital Media Forensic Detection

Cited by 24

LiveBench: A Challenging, Contamination-Limited LLM Benchmark

White, C., Dooley, S., Roberts, M., Pal, A., Feuer, B., Jain, S., Shwartz-Ziv, R., Jain, N., Saifullah, K., Dey, S., et al. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 20

ODIN: Disentangled Reward Mitigates Hacking in RLHF

Chen, L., Zhu, C., Soselia, D., Chen, J., Zhou, T., Goldstein, T., Huang, H., Shoeybi, M., & Catanzaro, B. (2024) · arXiv

Reinforcement Learning in RoboticsTopic Modeling

Cited by 13

Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach

Geiping, J., McLeish, S., Jain, N., Kirchenbauer, J., Singh, S., Bartoldson, B., Kailkhura, B., Bhatele, A., & Goldstein, T. (2025) · Advances in Neural Information Processing Systems 38

Topic ModelingExplainable Artificial Intelligence (XAI)

Cited by 10

WrapNet: Neural Net Inference with Ultra-Low-Resolution Arithmetic

Ni, R., Chu, H.-M., Castañeda, O., Chiang, P.-Y., Studer, C., & Goldstein, T. (2020) · arXiv

Advanced Neural Network ApplicationsLow-power high-performance VLSI design

Cited by 8

Certifying Model Accuracy under Distribution Shifts

Kumar, A., Levine, A., Goldstein, T., & Feizi, S. (2022) · arXiv

Adversarial Robustness in Machine LearningExplainable Artificial Intelligence (XAI)

Cited by 3

Commercial LLM Agents Are Already Vulnerable to Simple Yet Dangerous Attacks

Li, A., Zhou, Y., Raghuram, V. C., Goldstein, T., & Goldblum, M. (2025) · arXiv

Adversarial Robustness in Machine LearningSecurity and Verification in Computing

Cited by 2

Quantifying Cross-Modality Memorization in Vision-Language Models

Wen, Y., Huang, Y., Goldstein, T., Kumar, R., Ghazi, B., & Zhang, C. (2025) · arXiv

Multimodal Machine Learning ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 0