142 citations · 842 across the 49 of their papers we have counts for
9 papers · 1 filter
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…
Efficient Mean Estimation with Pure Differential Privacy via a Sum-of-Squares Exponential Mechanism
Samuel B. Hopkins, Gautam Kamath, Mahbod Majid
We give the first polynomial-time algorithm to estimate the mean of a -variate probability distribution with bounded covariance from independent samples subject t…
Robust Estimation for Random Graphs
Jayadev Acharya, Ayush Jain, Gautam Kamath +2
We study the problem of robustly estimating the parameter of an Erdős-Rényi random graph on nodes, where a fraction of nodes may be adversarially corrupted. After showi…
A Private and Computationally-Efficient Estimator for Unbounded Gaussians
Gautam Kamath, Argyris Mouzakis, Vikrant Singhal +2
We give the first polynomial-time, polynomial-sample, differentially private estimator for the mean and covariance of an arbitrary Gaussian distribution in $\mat…
Unbiased Statistical Estimation and Valid Confidence Intervals Under Differential Privacy
Christian Covington, Xi He, James Honaker +1
We present a method for producing unbiased parameter estimates and valid confidence intervals under the constraints of differential privacy, a formal framework for limiting individ…
Differentially Private Fine-tuning of Language Models
Da Yu, Saurabh Naik, Arturs Backurs +9
We give simpler, sparser, and faster algorithms for differentially private fine-tuning of large-scale pre-trained language models, which achieve the state-of-the-art privacy versus…
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…
Improved Rates for Differentially Private Stochastic Convex Optimization with Heavy-Tailed Data
Gautam Kamath, Xingtu Liu, Huanyu Zhang
We study stochastic convex optimization with heavy-tailed data under the constraint of differential privacy (DP). Most prior work on this problem is restricted to the case where th…
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…