12 citations · 12 across the 5 of their papers we have counts for
5 papers
SPriFed-OMP: A Differentially Private Federated Learning Algorithm for Sparse Basis Recovery
Ajinkya Kiran Mulay, Xiaojun Lin
Sparse basis recovery is a classical and important statistical learning problem when the number of model dimensions is much larger than the number of samples . However, ther…
LOCKS: User Differentially Private and Federated Optimal Client Sampling
Ajinkya K Mulay
With changes in privacy laws, there is often a hard requirement for client data to remain on the device rather than being sent to the server. Therefore, most processing happens on…
Private Hypothesis Testing for Social Sciences
Ajinkya K Mulay, Sean Lane, Erin Hennes
While running any experiment, we often have to consider the statistical power to ensure an effective study. Statistical power or power ensures that we can observe an effect with hi…
PowerGraph: Using neural networks and principal components to determine multivariate statistical power trade-offs
Ajinkya K Mulay, Sean Lane, Erin Hennes
Statistical power estimation for studies with multiple model parameters is inherently a multivariate problem. Power for individual parameters of interest cannot be reliably estimat…
Towards Quantifying the Carbon Emissions of Differentially Private Machine Learning
Rakshit Naidu, Harshita Diddee, Ajinkya Mulay +3
In recent years, machine learning techniques utilizing large-scale datasets have achieved remarkable performance. Differential privacy, by means of adding noise, provides strong pr…