SquirRL: Automating Attack Discovery on Blockchain Incentive Mechanisms with Deep Reinforcement Learning. - Charlie Hou; Mingxun Zhou; Yan Ji 001; Phil Daian; Florian Tramèr; Giulia Fanti; Ari Juels (CoRR, 2019)

SquirRL: Automating Attack Analysis on Blockchain Incentive Mechanisms with Deep Reinforcement Learning. - Charlie Hou; Mingxun Zhou; Yan Ji 001; Phil Daian; Florian Tramèr; Giulia Fanti; Ari Juels (NDSS, 2021)

Efficient Algorithms for Federated Saddle Point Optimization. - Charlie Hou; Kiran Koshy Thekumparampil; Giulia Fanti; Sewoong Oh (CoRR, 2021)

Reducing the Communication Cost of Federated Learning through Multistage Optimization. - Charlie Hou; Kiran Koshy Thekumparampil; Giulia Fanti; Sewoong Oh (CoRR, 2021)

FedChain: Chained Algorithms for Near-optimal Communication Cost in Federated Learning. - Charlie Hou; Kiran Koshy Thekumparampil; Giulia Fanti; Sewoong Oh (ICLR, 2022)

Privately Customizing Prefinetuning to Better Match User Data in Federated Learning. - Charlie Hou; Hongyuan Zhan; Akshat Shrivastava; Sid Wang; Aleksandr Livshits; Giulia Fanti; Daniel Lazar (CoRR, 2023)

PrE-Text: Training Language Models on Private Federated Data in the Age of LLMs. - Charlie Hou; Akshat Shrivastava; Hongyuan Zhan; Rylan Conway; Trang Le; Adithya Sagar; Giulia Fanti; Daniel Lazar (ICML, 2024)

On the Convergence of Differentially-Private Fine-tuning: To Linearly Probe or to Fully Fine-tune? - Shuqi Ke; Charlie Hou; Giulia Fanti; Sewoong Oh (CoRR, 2024)

Pretrained deep models outperform GBDTs in Learning-To-Rank under label scarcity. - Charlie Hou; Kiran Koshy Thekumparampil; Michael Shavlovsky; Giulia Fanti; Yesh Dattatreya; Sujay Sanghavi (Trans. Mach. Learn. Res., 2024)

Private Federated Learning using Preference-Optimized Synthetic Data. - Charlie Hou; Mei-Yu Wang; Yige Zhu; Daniel Lazar; Giulia Fanti (CoRR, 2025)