Goldblum, Micah

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 Cookbook of Self-Supervised Learning

Balestriero, R., Ibrahim, M., Sobal, V., Morcos, A., Shekhar, S., Goldstein, T., Bordes, F., Bardes, A., Mialon, G., Tian, Y., et al. (2023) · arXiv

Domain Adaptation and Few-Shot LearningMachine Learning and Algorithms

Cited by 182

Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery

Wen, Y., Jain, N., Kirchenbauer, J., Goldblum, M., Geiping, J., & Goldstein, T. (2023) · arXiv

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 77

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

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

Style Outweighs Substance: Failure Modes of LLM Judges in Alignment Benchmarking

Feuer, B., Goldblum, M., Datta, T., Nambiar, S., Besaleli, R., Dooley, S., Cembalest, M., & Dickerson, J. P. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 2