8 citations · 8 across the 4 of their papers we have counts for
7 papers
Differentially Private n-gram Extraction
Kunho Kim, Sivakanth Gopi, Janardhan Kulkarni +1
We revisit the problem of -gram extraction in the differential privacy setting. In this problem, given a corpus of private text data, the goal is to release as many -grams as…
Numerical Composition of Differential Privacy
Sivakanth Gopi, Yin Tat Lee, Lukas Wutschitz
We give a fast algorithm to optimally compose privacy guarantees of differentially private (DP) algorithms to arbitrary accuracy. Our method is based on the notion of privacy loss…
Fast and Memory Efficient Differentially Private-SGD via JL Projections
Zhiqi Bu, Sivakanth Gopi, Janardhan Kulkarni +3
Differentially Private-SGD (DP-SGD) of Abadi et al. (2016) and its variations are the only known algorithms for private training of large scale neural networks. This algorithm requ…
Locally Private Hypothesis Selection
Sivakanth Gopi, Gautam Kamath, Janardhan Kulkarni +3
We initiate the study of hypothesis selection under local differential privacy. Given samples from an unknown probability distribution and a set of probability distribution…
CSPs with Global Modular Constraints: Algorithms and Hardness via Polynomial Representations
Joshua Brakensiek, Sivakanth Gopi, Venkatesan Guruswami
We study the complexity of Boolean constraint satisfaction problems (CSPs) when the assignment must have Hamming weight in some congruence class modulo M, for various choices of th…
Spanoids - an abstraction of spanning structures, and a barrier for LCCs
Zeev Dvir, Sivakanth Gopi, Yuzhou Gu +1
We introduce a simple logical inference structure we call a (generalizing the notion of a matroid), which captures well-studied problems in several areas. These…