| Fetchsgd: Communication-efficient federated learning with sketching D Rothchild*, A Panda*, E Ullah, N Ivkin, I Stoica, V Braverman, ... International Conference on Machine Learning, 8253-8265, 2020 | 335 | 2020 |
| Neurotoxin: Durable backdoors in federated learning Z Zhang*, A Panda*, L Song, Y Yang, M Mahoney, P Mittal, R Kannan, ... International Conference on Machine Learning, 26429-26446, 2022 | 86 | 2022 |
| Visual adversarial examples jailbreak large language models X Qi, K Huang, A Panda, M Wang, P Mittal AAAI 2024, 2023 | 67* | 2023 |
| Sparsefed: Mitigating model poisoning attacks in federated learning with sparsification A Panda, S Mahloujifar, AN Bhagoji, S Chakraborty, P Mittal International Conference on Artificial Intelligence and Statistics, 7587-7624, 2022 | 65 | 2022 |
| Privacy-preserving in-context learning for large language models T Wu*, A Panda*, JT Wang*, P Mittal The Twelfth International Conference on Learning Representations, 2023 | 20* | 2023 |
| A New Linear Scaling Rule for Differentially Private Hyperparameter Optimization A Panda*, X Tang*, V Sehwag, S Mahloujifar, P Mittal | 13* | 2023 |
| Differentially private image classification by learning priors from random processes X Tang*, A Panda*, V Sehwag, P Mittal Advances in Neural Information Processing Systems 36, 2024 | 5 | 2024 |
| Teach LLMs to Phish: Stealing Private Information from Language Models A Panda, CA Choquette-Choo, Z Zhang, Y Yang, P Mittal The Twelfth International Conference on Learning Representations, 2024 | 4* | 2024 |
| Private Fine-tuning of Large Language Models with Zeroth-order Optimization X Tang*, A Panda*, M Nasr, S Mahloujifar, P Mittal arXiv preprint arXiv:2401.04343, 2024 | 2 | 2024 |
| Differentially Private Generation of High Fidelity Samples From Diffusion Models V Sehwag, A Panda, A Pokle, X Tang, S Mahloujifar, M Chiang, JZ Kolter, ... ICML 2023 DeployableGenerativeAI Workshop, 2023 | | 2023 |
Prateek MittalProfessor, Princeton UniversityVerified email at princeton.edu
