97 citations · 127 across the 21 of their papers we have counts for
15 papers · 1 filter
Rigid-profile input scheduling under constrained dynamics with a water network application
Adair Lang, Michael Cantoni, Farhad Farokhi +1
The motivation for this work stems from the problem of scheduling requests for flow at supply points along an automated network of open-water channels. The off-take flows are rigid…
Gradient Sparsification Can Improve Performance of Differentially-Private Convex Machine Learning
Farhad Farokhi
We use gradient sparsification to reduce the adverse effect of differential privacy noise on performance of private machine learning models. To this aim, we employ compressed sensi…
When Machine Learning Meets Privacy: A Survey and Outlook
Bo Liu, Ming Ding, Sina Shaham +3
The newly emerged machine learning (e.g. deep learning) methods have become a strong driving force to revolutionize a wide range of industries, such as smart healthcare, financial…
Non-Stochastic Private Function Evaluation
Farhad Farokhi, Girish Nair
We consider private function evaluation to provide query responses based on private data of multiple untrusted entities in such a way that each cannot learn something substantially…
Structured preconditioning of conjugate gradients for path-graph network optimal control problems
Armaghan Zafar, Michael Cantoni, Farhad Farokhi
A structured preconditioned conjugate gradient (PCG) solver is developed for the Newton steps in second-order methods for a class of constrained network optimal control problems. O…
Deconvoluting Kernel Density Estimation and Regression for Locally Differentially Private Data
Farhad Farokhi
Local differential privacy has become the gold-standard of privacy literature for gathering or releasing sensitive individual data points in a privacy-preserving manner. However, l…