no code implementations • 15 Dec 2023 • Naman Agarwal, Satyen Kale, Karan Singh, Abhradeep Guha Thakurta
We study the task of $(\epsilon, \delta)$-differentially private online convex optimization (OCO).
no code implementations • 7 Oct 2022 • Soumyabrata Pal, Prateek Varshney, Prateek Jain, Abhradeep Guha Thakurta, Gagan Madan, Gaurav Aggarwal, Pradeep Shenoy, Gaurav Srivastava
We then study the framework in the linear setting, where the problem reduces to that of estimating the sum of a rank-$r$ and a $k$-column sparse matrix using a small number of linear measurements.
no code implementations • 6 Jul 2022 • Oren Mangoubi, Yikai Wu, Satyen Kale, Abhradeep Guha Thakurta, Nisheeth K. Vishnoi
Consider the following optimization problem: Given $n \times n$ matrices $A$ and $\Lambda$, maximize $\langle A, U\Lambda U^*\rangle$ where $U$ varies over the unitary group $\mathrm{U}(n)$.
1 code implementation • 1 Jul 2022 • Xuechen Li, Daogao Liu, Tatsunori Hashimoto, Huseyin A. Inan, Janardhan Kulkarni, Yin Tat Lee, Abhradeep Guha Thakurta
Large pretrained models can be privately fine-tuned to achieve performance approaching that of non-private models.
1 code implementation • 16 Feb 2022 • Sergey Denisov, Brendan Mcmahan, Keith Rush, Adam Smith, Abhradeep Guha Thakurta
Motivated by recent applications requiring differential privacy over adaptive streams, we investigate the question of optimal instantiations of the matrix mechanism in this setting.
no code implementations • NeurIPS 2021 • Prateek Jain, John Rush, Adam Smith, Shuang Song, Abhradeep Guha Thakurta
We study personalization of supervised learning with user-level differential privacy.
1 code implementation • 23 Nov 2021 • Ameya Daigavane, Gagan Madan, Aditya Sinha, Abhradeep Guha Thakurta, Gaurav Aggarwal, Prateek Jain
Graph Neural Networks (GNNs) are a popular technique for modelling graph-structured data and computing node-level representations via aggregation of information from the neighborhood of each node.
no code implementations • NeurIPS 2018 • Raef Bassily, Abhradeep Guha Thakurta, Om Dipakbhai Thakkar
In the PAC model, we analyze our construction and prove upper bounds on the sample complexity for both the realizable and the non-realizable cases.
no code implementations • NeurIPS 2015 • Kunal Talwar, Abhradeep Guha Thakurta, Li Zhang
In addition, we show that this error bound is nearly optimal amongst all differentially private algorithms.
no code implementations • NeurIPS 2013 • Abhradeep Guha Thakurta, Adam Smith
The technique leads to the first nonprivate algorithms for private online learning in the bandit setting.