Alex Bie

24 papers A* 5Journal 18Unranked 1
YearRankTypeTitle / Venue / Authors
2026 J jnl
AI-rithmetic.
CoRR
Alex Bie, Travis Dick, Alex Kulesza, Prabhakar Raghavan, Vinod Raman, Sergei Vassilvitskii
2026 J jnl
Learning from Synthetic Data: Limitations of ERM.
CoRR
Kareem Amin, Alex Bie, Weiwei Kong, Umar Syed, Sergei Vassilvitskii
2025 J jnl
Clustering and Median Aggregation Improve Differentially Private Inference.
CoRR
Kareem Amin, Salman Avestimehr, Sara Babakniya, Alex Bie, Weiwei Kong, Natalia Ponomareva, Umar Syed
2025 J jnl
Escaping Collapse: The Strength of Weak Data for Large Language Model Training.
CoRR
Kareem Amin, Sara Babakniya, Alex Bie, Weiwei Kong, Umar Syed, Sergei Vassilvitskii
2025 J jnl
Foundation Models Meet Federated Learning: A One-shot Feature-sharing Method with Privacy and Performance Guarantees.
Trans. Mach. Learn. Res.
Mahdi Beitollahi, Alex Bie, Sobhan Hemati, Leo Maxime Brunswic, Xu Li, Xi Chen, Guojun Zhang
2025 J jnl
How to DP-fy Your Data: A Practical Guide to Generating Synthetic Data With Differential Privacy.
CoRR
Natalia Ponomareva, Zheng Xu, H. Brendan McMahan, Peter Kairouz, Lucas Rosenblatt, Vincent Cohen-Addad, Cristóbal Guzmán, Ryan McKenna, Galen Andrew, Alex Bie, Da Yu, Alex Kurakin, Morteza Zadimoghaddam, Sergei Vassilvitskii, Andreas Terzis
2025 A* conf
On the Learnability of Distribution Classes with Adaptive Adversaries.
ICML
Tosca Lechner, Alex Bie, Gautam Kamath
2025 J jnl
On the Learnability of Distribution Classes with Adaptive Adversaries.
CoRR
Tosca Lechner, Alex Bie, Gautam Kamath
2024 J jnl
Distribution Learnability and Robustness.
CoRR
Shai Ben-David, Alex Bie, Gautam Kamath, Tosca Lechner
2024 J jnl
Parametric Feature Transfer: One-shot Federated Learning with Foundation Models.
CoRR
Mahdi Beitollahi, Alex Bie, Sobhan Hemati, Leo Maxime Brunswic, Xu Li, Xi Chen, Guojun Zhang
2024 — conf
Private prediction for large-scale synthetic text generation.
EMNLP (Findings)
Kareem Amin, Alex Bie, Weiwei Kong, Alexey Kurakin, Natalia Ponomareva, Umar Syed, Andreas Terzis, Sergei Vassilvitskii
2024 J jnl
Private prediction for large-scale synthetic text generation.
CoRR
Kareem Amin, Alex Bie, Weiwei Kong, Alexey Kurakin, Natalia Ponomareva, Umar Syed, Andreas Terzis, Sergei Vassilvitskii
2024 J jnl
Understanding the Role of Layer Normalization in Label-Skewed Federated Learning.
Trans. Mach. Learn. Res.
Guojun Zhang, Mahdi Beitollahi, Alex Bie, Xi Chen
2023 A* conf
Distribution Learnability and Robustness.
NeurIPS
Shai Ben-David, Alex Bie, Gautam Kamath, Tosca Lechner
2023 J jnl
Normalization Is All You Need: Understanding Layer-Normalized Federated Learning under Extreme Label Shift.
CoRR
Guojun Zhang, Mahdi Beitollahi, Alex Bie, Xi Chen
2023 A* conf
Private Distribution Learning with Public Data: The View from Sample Compression.
NeurIPS
Shai Ben-David, Alex Bie, Clément L. Canonne, Gautam Kamath, Vikrant Singhal
2023 J jnl
Private Distribution Learning with Public Data: The View from Sample Compression.
CoRR
Shai Ben-David, Alex Bie, Clément L. Canonne, Gautam Kamath, Vikrant Singhal
2023 J jnl
Private GANs, Revisited.
CoRR
Alex Bie, Gautam Kamath, Guojun Zhang
2023 J jnl
Private GANs, Revisited.
Trans. Mach. Learn. Res.
Alex Bie, Gautam Kamath, Guojun Zhang
2022 A* conf
Private Estimation with Public Data.
NeurIPS
Alex Bie, Gautam Kamath, Vikrant Singhal
2022 J jnl
Private Estimation with Public Data.
CoRR
Alex Bie, Gautam Kamath, Vikrant Singhal
2021 A* conf
Don't Generate Me: Training Differentially Private Generative Models with Sinkhorn Divergence.
NeurIPS
Tianshi Cao, Alex Bie, Arash Vahdat, Sanja Fidler, Karsten Kreis
2021 J jnl
Don't Generate Me: Training Differentially Private Generative Models with Sinkhorn Divergence.
CoRR
Tianshi Cao, Alex Bie, Arash Vahdat, Sanja Fidler, Karsten Kreis
2019 J jnl
Fully Quantizing a Simplified Transformer for End-to-end Speech Recognition.
CoRR
Alex Bie, Bharat Venkitesh, Joao Monteiro, Md. Akmal Haidar, Mehdi Rezagholizadeh