Bie, Alex

12 publications

NeurIPS 2025 Escaping Collapse: The Strength of Weak Data for Large Language Model Training Kareem Amin, Sara Babakniya, Alex Bie, Weiwei Kong, Umar Syed, Sergei Vassilvitskii

ICLRW 2025 Escaping Collapse: The Strength of Weak Data for Large Language Model Training Kareem Amin, Sara Babakniya, Alex Bie, Weiwei Kong, Umar Syed, Sergei Vassilvitskii

TMLR 2025 Foundation Models Meet Federated Learning: A One-Shot Feature-Sharing Method with Privacy and Performance Guarantees Mahdi Beitollahi, Alex Bie, Sobhan Hemati, Leo Maxime Brunswic, Xu Li, Xi Chen, Guojun Zhang

ICML 2025 On the Learnability of Distribution Classes with Adaptive Adversaries Tosca Lechner, Alex Bie, Gautam Kamath

NeurIPSW 2024 RenderAttack: Hundreds of Adversarial Attacks Through Differentiable Texture Generation Dron Hazra, Alex Bie, Mantas Mazeika, Xuwang Yin, Andy Zou, Dan Hendrycks, Maximilian Kaufmann

TMLR 2024 Understanding the Role of Layer Normalization in Label-Skewed Federated Learning Guojun Zhang, Mahdi Beitollahi, Alex Bie, Xi Chen

NeurIPS 2023 Distribution Learnability and Robustness Shai Ben-David, Alex Bie, Gautam Kamath, Tosca Lechner

NeurIPS 2023 Private Distribution Learning with Public Data: The View from Sample Compression Shai Ben-David, Alex Bie, Clément L Canonne, Gautam Kamath, Vikrant Singhal

TMLR 2023 Private GANs, Revisited Alex Bie, Gautam Kamath, Guojun Zhang

NeurIPS 2022 Private Estimation with Public Data Alex Bie, Gautam Kamath, Vikrant Singhal

NeurIPSW 2022 Private GANs, Revisited Alex Bie, Gautam Kamath, Guojun Zhang

NeurIPS 2021 Don’t Generate Me: Training Differentially Private Generative Models with Sinkhorn Divergence Tianshi Cao, Alex Bie, Arash Vahdat, Sanja Fidler, Karsten Kreis