5.1k citations · 7.5k across the 42 of their papers we have counts for
42 papers
Variance-Reduced Gradient Estimation via Noise-Reuse in Online Evolution Strategies
Oscar Li, James Harrison, Jascha Sohl-Dickstein +2
Unrolled computation graphs are prevalent throughout machine learning but present challenges to automatic differentiation (AD) gradient estimation methods when their loss functions…
Federated Learning as a Network Effects Game
Shengyuan Hu, Dung Daniel Ngo, Shuran Zheng +2
Federated Learning (FL) aims to foster collaboration among a population of clients to improve the accuracy of machine learning without directly sharing local data. Although there h…
Bitrate-Constrained DRO: Beyond Worst Case Robustness To Unknown Group Shifts
Amrith Setlur, Don Dennis, Benjamin Eysenbach +4
Training machine learning models robust to distribution shifts is critical for real-world applications. Some robust training algorithms (e.g., Group DRO) specialize to group shifts…
On Noisy Evaluation in Federated Hyperparameter Tuning
Kevin Kuo, Pratiksha Thaker, Mikhail Khodak +4
Hyperparameter tuning is critical to the success of federated learning applications. Unfortunately, appropriately selecting hyperparameters is challenging in federated networks. Is…
Differentially Private Adaptive Optimization with Delayed Preconditioners
Tian Li, Manzil Zaheer, Ken Ziyu Liu +3
Privacy noise may negate the benefits of using adaptive optimizers in differentially private model training. Prior works typically address this issue by using auxiliary information…
Adversarial Unlearning: Reducing Confidence Along Adversarial Directions
Amrith Setlur, Benjamin Eysenbach, Virginia Smith +1
Supervised learning methods trained with maximum likelihood objectives often overfit on training data. Most regularizers that prevent overfitting look to increase confidence on add…