27 citations · 115 across the 17 of their papers we have counts for
4 papers · 1 filter
Distributionally Robust Optimization with Bias and Variance Reduction
Ronak Mehta, Vincent Roulet, Krishna Pillutla +1
We consider the distributionally robust optimization (DRO) problem with spectral risk-based uncertainty set and -divergence penalty. This formulation includes common risk-sensit…
Towards Federated Foundation Models: Scalable Dataset Pipelines for Group-Structured Learning
Zachary Charles, Nicole Mitchell, Krishna Pillutla +2
We introduce Dataset Grouper, a library to create large-scale group-structured (e.g., federated) datasets, enabling federated learning simulation at the scale of foundation models.…
Unleashing the Power of Randomization in Auditing Differentially Private ML
Krishna Pillutla, Galen Andrew, Peter Kairouz +3
We present a rigorous methodology for auditing differentially private machine learning algorithms by adding multiple carefully designed examples called canaries. We take a first pr…
Modified Gauss-Newton Algorithms under Noise
Krishna Pillutla, Vincent Roulet, Sham Kakade +1
Gauss-Newton methods and their stochastic version have been widely used in machine learning and signal processing. Their nonsmooth counterparts, modified Gauss-Newton or prox-linea…