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no code implementations • 7 Dec 2021 • Pravesh K. Kothari, Pasin Manurangsi, Ameya Velingker

Prior works obtained private robust algorithms for mean estimation of subgaussian distributions with bounded covariance.

no code implementations • NeurIPS 2021 • Badih Ghazi, Ravi Kumar, Pasin Manurangsi

Most works in learning with differential privacy (DP) have focused on the setting where each user has a single sample.

no code implementations • 21 Oct 2021 • Badih Ghazi, Ravi Kumar, Pasin Manurangsi

Most works in learning with differential privacy (DP) have focused on the setting where each user has a single sample.

no code implementations • 3 Aug 2021 • Rohan Anil, Badih Ghazi, Vineet Gupta, Ravi Kumar, Pasin Manurangsi

In this work, we study the large-scale pretraining of BERT-Large with differentially private SGD (DP-SGD).

no code implementations • NeurIPS 2021 • Sreenivas Gollapudi, Guru Guruganesh, Kostas Kollias, Pasin Manurangsi, Renato Paes Leme, Jon Schneider

We design algorithms for this problem which achieve regret $O(d\log T)$ and $\exp(O(d \log d))$.

no code implementations • 20 Apr 2021 • Alisa Chang, Badih Ghazi, Ravi Kumar, Pasin Manurangsi

We provide an approximation algorithm for k-means clustering in the one-round (aka non-interactive) local model of differential privacy (DP).

no code implementations • NeurIPS 2021 • Badih Ghazi, Noah Golowich, Ravi Kumar, Pasin Manurangsi, Chiyuan Zhang

The Randomized Response (RR) algorithm is a classical technique to improve robustness in survey aggregation, and has been widely adopted in applications with differential privacy guarantees.

no code implementations • 16 Dec 2020 • Badih Ghazi, Ravi Kumar, Pasin Manurangsi

On the other hand, the algorithm of Dagan and Kur has a remarkable advantage that the $\ell_{\infty}$ error bound of $O(\frac{1}{\epsilon}\sqrt{k \log \frac{1}{\delta}})$ holds not only in expectation but always (i. e., with probability one) while we can only get a high probability (or expected) guarantee on the error.

no code implementations • 7 Dec 2020 • Badih Ghazi, Noah Golowich, Ravi Kumar, Pasin Manurangsi

In this paper we prove that the sample complexity of properly learning a class of Littlestone dimension $d$ with approximate differential privacy is $\tilde O(d^6)$, ignoring privacy and accuracy parameters.

no code implementations • 30 Nov 2020 • Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Thao Nguyen

In this work, we study the trade-off between differential privacy and adversarial robustness under L2-perturbations in the context of learning halfspaces.

no code implementations • 27 Nov 2020 • Surbhi Goel, Adam Klivans, Pasin Manurangsi, Daniel Reichman

We are also able to obtain lower bounds on the running time in terms of the desired additive error $\epsilon$.

no code implementations • 21 Sep 2020 • Lijie Chen, Badih Ghazi, Ravi Kumar, Pasin Manurangsi

We study the setup where each of $n$ users holds an element from a discrete set, and the goal is to count the number of distinct elements across all users, under the constraint of $(\epsilon, \delta)$-differentially privacy: - In the non-interactive local setting, we prove that the additive error of any protocol is $\Omega(n)$ for any constant $\epsilon$ and for any $\delta$ inverse polynomial in $n$.

no code implementations • NeurIPS 2020 • Badih Ghazi, Ravi Kumar, Pasin Manurangsi

For several basic clustering problems, including Euclidean DensestBall, 1-Cluster, k-means, and k-median, we give efficient differentially private algorithms that achieve essentially the same approximation ratios as those that can be obtained by any non-private algorithm, while incurring only small additive errors.

no code implementations • NeurIPS 2020 • Ilias Diakonikolas, Daniel M. Kane, Pasin Manurangsi

We study the computational complexity of adversarially robust proper learning of halfspaces in the distribution-independent agnostic PAC model, with a focus on $L_p$ perturbations.

no code implementations • 7 Jul 2020 • Badih Ghazi, Noah Golowich, Ravi Kumar, Pasin Manurangsi

We study closure properties for the Littlestone and threshold dimensions of binary hypothesis classes.

no code implementations • 24 Sep 2019 • Badih Ghazi, Pasin Manurangsi, Rasmus Pagh, Ameya Velingker

Using a reduction of Balle et al. (2019), our improved analysis of the protocol of Ishai et al. yields, in the same model, an $\left(\varepsilon, \delta\right)$-differentially private protocol for aggregation that, for any constant $\varepsilon > 0$ and any $\delta = \frac{1}{\mathrm{poly}(n)}$, incurs only a constant error and requires only a constant number of messages per party.

Cryptography and Security Data Structures and Algorithms

no code implementations • NeurIPS 2019 • Ilias Diakonikolas, Daniel M. Kane, Pasin Manurangsi

We study the problem of {\em properly} learning large margin halfspaces in the agnostic PAC model.

no code implementations • 20 Jul 2017 • Rajesh Chitnis, Andreas Emil Feldmann, Pasin Manurangsi

We give a tight inapproximability result by showing that for $k$ no parameterized $(2-\varepsilon)$-approximation algorithm exists under Gap-ETH.

Data Structures and Algorithms Computational Complexity

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