Ghazi, Badih

Advances and Open Problems in Federated Learning

Kairouz, P., McMahan, H. B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A. N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al. (2019) · arXiv

Privacy-Preserving Technologies in DataStochastic Gradient Optimization Techniques

Cited by 7,857

Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

Comanici, G., Bieber, E., Schaekermann, M., Pasupat, I., Sachdeva, N., Dhillon, I., Blistein, M., Ram, O., Zhang, D., Rosen, E., et al. (2025) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 69

Large-Scale Differentially Private BERT

Anil, R., Ghazi, B., Gupta, V., Kumar, R., & Manurangsi, P. (2022) · Findings of the Association for Computational Linguistics: EMNLP 2022

Privacy-Preserving Technologies in DataStochastic Gradient Optimization Techniques

Cited by 68

Scalable and Differentially Private Distributed Aggregation in the Shuffled Model

Ghazi, B., Pagh, R., & Velingker, A. (2019) · arXiv

Privacy-Preserving Technologies in DataCryptography and Data Security

Cited by 62

On the Power of Multiple Anonymous Messages

Ghazi, B., Golowich, N., Kumar, R., Pagh, R., & Velingker, A. (2019) · arXiv

Privacy-Preserving Technologies in DataCryptography and Data Security

Cited by 26

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