15 citations · 21 across the 6 of their papers we have counts for
6 papers
Collaborative Heterogeneous Causal Inference Beyond Meta-analysis
Tianyu Guo, Sai Praneeth Karimireddy, Michael I. Jordan
Collaboration between different data centers is often challenged by heterogeneity across sites. To account for the heterogeneity, the state-of-the-art method is to re-weight the co…
Scaff-PD: Communication Efficient Fair and Robust Federated Learning
Yaodong Yu, Sai Praneeth Karimireddy, Yi Ma +1
We present Scaff-PD, a fast and communication-efficient algorithm for distributionally robust federated learning. Our approach improves fairness by optimizing a family of distribut…
Evaluating and Incentivizing Diverse Data Contributions in Collaborative Learning
Baihe Huang, Sai Praneeth Karimireddy, Michael I. Jordan
For a federated learning model to perform well, it is crucial to have a diverse and representative dataset. However, the data contributors may only be concerned with the performanc…
Federated Conformal Predictors for Distributed Uncertainty Quantification
Charles Lu, Yaodong Yu, Sai Praneeth Karimireddy +2
Conformal prediction is emerging as a popular paradigm for providing rigorous uncertainty quantification in machine learning since it can be easily applied as a post-processing ste…
Mechanisms that Incentivize Data Sharing in Federated Learning
Sai Praneeth Karimireddy, Wenshuo Guo, Michael I. Jordan
Federated learning is typically considered a beneficial technology which allows multiple agents to collaborate with each other, improve the accuracy of their models, and solve prob…
TCT: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernels
Yaodong Yu, Alexander Wei, Sai Praneeth Karimireddy +2
State-of-the-art federated learning methods can perform far worse than their centralized counterparts when clients have dissimilar data distributions. For neural networks, even whe…