| SquirRL: Automating attack analysis on blockchain incentive mechanisms with deep reinforcement learning C Hou, M Zhou, Y Ji, P Daian, F Tramer, G Fanti, A Juels arXiv preprint arXiv:1912.01798, 2019 | 103 | 2019 |
| Efficient algorithms for federated saddle point optimization C Hou, KK Thekumparampil, G Fanti, S Oh arXiv preprint arXiv:2102.06333, 2021 | 27 | 2021 |
| Privately customizing prefinetuning to better match user data in federated learning C Hou, H Zhan, A Shrivastava, S Wang, A Livshits, G Fanti, D Lazar arXiv preprint arXiv:2302.09042, 2023 | 16 | 2023 |
| FedChain: Chained algorithms for near-optimal communication cost in federated learning C Hou, KK Thekumparampil, G Fanti, S Oh arXiv preprint arXiv:2108.06869, 2021 | 15 | 2021 |
| PrE-Text: Training Language Models on Private Federated Data in the Age of LLMs C Hou, A Shrivastava, H Zhan, R Conway, T Le, A Sagar, G Fanti, D Lazar ICML 2024, 0 | 9* | |
| Reducing the communication cost of federated learning through multistage optimization C Hou, KK Thekumparampil, G Fanti, S Oh arXiv preprint arXiv:2108.06869, 2021 | 6 | 2021 |
| On the Convergence of Differentially-Private Fine-tuning: To Linearly Probe or to Fully Fine-tune? S Ke, C Hou, G Fanti, S Oh arXiv preprint arXiv:2402.18905, 2024 | 5 | 2024 |
| Multistage stepsize schedule in federated learning: Bridging theory and practice GFC Hou, K Thekumparampil, S Oh ICML Workshop 12, 2021 | 3 | 2021 |
| Pretrained deep models outperform GBDTs in Learning-To-Rank under label scarcity C Hou, KK Thekumparampil, M Shavlovsky, G Fanti, Y Dattatreya, ... arXiv preprint arXiv:2308.00177, 2023 | 2 | 2023 |
| Strategies for Learning From Non-Ideal Sources of Data C Hou Carnegie Mellon University, 2024 | | 2024 |
| FedChain: Chained Algorithms for Near-optimal Communication Cost in Federated Learning C Hou, KK Thekumparampil, G Fanti, S Oh International Conference on Learning Representations, 2021 | | 2021 |
| Private Federated Learning using Preference-Optimized Synthetic Data C Hou, MY Wang, Y Zhu, D Lazar, G Fanti Will Synthetic Data Finally Solve the Data Access Problem?, 0 | | |
| Characterizing the Training Dynamics of Private Fine-tuning with Langevin diffusion S Ke, C Hou, S Oh, G Fanti NeurIPS 2024 Workshop on Fine-Tuning in Modern Machine Learning: Principles …, 0 | | |
Giulia FantiCarnegie Mellon University, ECE DepartmentZweryfikowany adres z andrew.cmu.edu
