20 citations · 28 across the 6 of their papers we have counts for
22 papers
Calibration with Privacy in Peer Review
Wenxin Ding, Gautam Kamath, Weina Wang +1
Reviewers in peer review are often miscalibrated: they may be strict, lenient, extreme, moderate, etc. A number of algorithms have previously been proposed to calibrate reviews. Su…
The Role of Adaptive Optimizers for Honest Private Hyperparameter Selection
Shubhankar Mohapatra, Sajin Sasy, Xi He +2
Hyperparameter optimization is a ubiquitous challenge in machine learning, and the performance of a trained model depends crucially upon their effective selection. While a rich set…
The Price of Tolerance in Distribution Testing
Clément L. Canonne, Ayush Jain, Gautam Kamath +1
We revisit the problem of tolerant distribution testing. That is, given samples from an unknown distribution over , is it -close to or $\varepsi…
Remember What You Want to Forget: Algorithms for Machine Unlearning
Ayush Sekhari, Jayadev Acharya, Gautam Kamath +1
We study the problem of unlearning datapoints from a learnt model. The learner first receives a dataset drawn i.i.d. from an unknown distribution, and outputs a model $\widehat…
On the Sample Complexity of Privately Learning Unbounded High-Dimensional Gaussians
Ishaq Aden-Ali, Hassan Ashtiani, Gautam Kamath
We provide sample complexity upper bounds for agnostically learning multivariate Gaussians under the constraint of approximate differential privacy. These are the first finite samp…
Enabling Fast Differentially Private SGD via Just-in-Time Compilation and Vectorization
Pranav Subramani, Nicholas Vadivelu, Gautam Kamath
A common pain point in differentially private machine learning is the significant runtime overhead incurred when executing Differentially Private Stochastic Gradient Descent (DPSGD…