| Fetchsgd: Communication-efficient federated learning with sketching D Rothchild*, A Panda*, E Ullah, N Ivkin, I Stoica, V Braverman, ... ICML 2020, 8253-8265, 2020 | 441 | 2020 |
| Visual adversarial examples jailbreak large language models X Qi, K Huang, A Panda, M Wang, P Mittal AAAI 2024, 2023 | 201* | 2023 |
| Neurotoxin: Durable backdoors in federated learning Z Zhang*, A Panda*, L Song, Y Yang, M Mahoney, P Mittal, R Kannan, ... ICML 2022, 26429-26446, 2022 | 154 | 2022 |
| Sparsefed: Mitigating model poisoning attacks in federated learning with sparsification A Panda, S Mahloujifar, AN Bhagoji, S Chakraborty, P Mittal AISTATS 2022, 7587-7624, 2022 | 111 | 2022 |
| Privacy-preserving in-context learning for large language models T Wu*, A Panda*, JT Wang*, P Mittal ICLR 2024, 2023 | 47* | 2023 |
| Safety Alignment Should Be Made More Than Just a Few Tokens Deep X Qi, A Panda, K Lyu, X Ma, S Roy, A Beirami, P Mittal, P Henderson arXiv preprint arXiv:2406.05946, 2024 | 27 | 2024 |
| Teach LLMs to Phish: Stealing Private Information from Language Models A Panda, CA Choquette-Choo, Z Zhang, Y Yang, P Mittal ICLR 2024, 2024 | 23* | 2024 |
| Differentially private image classification by learning priors from random processes X Tang*, A Panda*, V Sehwag, P Mittal NeurIPS 2023 36, 2024 | 15 | 2024 |
| A New Linear Scaling Rule for Private Adaptive Hyperparameter Optimization A Panda*, X Tang*, V Sehwag, S Mahloujifar, P Mittal ICML 2024, 2023 | 14* | 2023 |
| 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 | 13 | 2024 |
| Lottery ticket adaptation: Mitigating destructive interference in llms A Panda, B Isik, X Qi, S Koyejo, T Weissman, P Mittal ICML 2024 (Workshops), 2024 | 9 | 2024 |
| Privacy auditing of large language models A Panda, X Tang, M Nasr, CA Choquette-Choo, P Mittal ICML 2024 Next Generation of AI Safety Workshop, 2024 | 3 | 2024 |
| StructMoE: Structured Mixture of Experts Using Low Rank Experts Z Sarwar, A Panda, B Thérien, S Rawls, A Das, K Balasubramaniam, ... NeurIPS Efficient Natural Language and Speech Processing Workshop, 182-193, 2024 | | 2024 |
| Dense Backpropagation Improves Routing for Sparsely-Gated Mixture-of-Experts A Panda, V Baherwani, Z Sarwar, B Thérien, S Rawls, S Sahu, ... Workshop on Machine Learning and Compression, NeurIPS 2024, 2024 | | 2024 |
| Refusal Tokens: A Simple Way to Calibrate Refusals in Large Language Models N Jain, A Shrivastava, C Zhu, D Liu, A Samuel, A Panda, A Kumar, ... arXiv preprint arXiv:2412.06748, 2024 | | 2024 |
| Unlocking Trustworthy Machine Learning With Sparsity A Panda Princeton University, 2024 | | 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 |
| Workshop on Sparsity in LLMs (SLLM): Deep Dive into Mixture of Experts, Quantization, Hardware, and Inference T Chen, U Evci, Y Ioannou, B Isik, S Liu, M Adnan, A Nowak, A Panda ICLR 2025 Workshop Proposals, 0 | | |
| StructMoE: Augmenting MoEs with Hierarchically Routed Low Rank Experts Z Sarwar, A Panda, B Thérien, S Rawls, S Sahu, S Chakraborty | | |
Prateek MittalProfessor, Princeton UniversityVerified email at princeton.edu