Search Results for author: Aleksandar Nikolov

Found 9 papers, 0 papers with code

Gaussian Noise is Nearly Instance Optimal for Private Unbiased Mean Estimation

no code implementations31 Jan 2023 Aleksandar Nikolov, Haohua Tang

We investigate unbiased high-dimensional mean estimators in differential privacy.

Learning versus Refutation in Noninteractive Local Differential Privacy

no code implementations26 Oct 2022 Alexander Edmonds, Aleksandar Nikolov, Toniann Pitassi

We study two basic statistical tasks in non-interactive local differential privacy (LDP): learning and refutation.

PAC learning

Private Query Release via the Johnson-Lindenstrauss Transform

no code implementations15 Aug 2022 Aleksandar Nikolov

We introduce a new method for releasing answers to statistical queries with differential privacy, based on the Johnson-Lindenstrauss lemma.


Private Query Release Assisted by Public Data

no code implementations ICML 2020 Raef Bassily, Albert Cheu, Shay Moran, Aleksandar Nikolov, Jonathan Ullman, Zhiwei Steven Wu

In comparison, with only private samples, this problem cannot be solved even for simple query classes with VC-dimension one, and without any private samples, a larger public sample of size $d/\alpha^2$ is needed.

Maximizing Determinants under Matroid Constraints

no code implementations16 Apr 2020 Vivek Madan, Aleksandar Nikolov, Mohit Singh, Uthaipon Tantipongpipat

Our main result is a new approximation algorithm with an approximation guarantee that depends only on the dimension $d$ of the vectors and not on the size $k$ of the output set.

Experimental Design

Locally Private Hypothesis Selection

no code implementations21 Feb 2020 Sivakanth Gopi, Gautam Kamath, Janardhan Kulkarni, Aleksandar Nikolov, Zhiwei Steven Wu, Huanyu Zhang

Absent privacy constraints, this problem requires $O(\log k)$ samples from $p$, and it was recently shown that the same complexity is achievable under (central) differential privacy.

Two-sample testing

The Power of Factorization Mechanisms in Local and Central Differential Privacy

no code implementations19 Nov 2019 Alexander Edmonds, Aleksandar Nikolov, Jonathan Ullman

We give new characterizations of the sample complexity of answering linear queries (statistical queries) in the local and central models of differential privacy: *In the non-interactive local model, we give the first approximate characterization of the sample complexity.

Proportional Volume Sampling and Approximation Algorithms for A-Optimal Design

no code implementations22 Feb 2018 Aleksandar Nikolov, Mohit Singh, Uthaipon Tao Tantipongpipat

Our main result is to obtain improved approximation algorithms for the $A$-optimal design problem by introducing the proportional volume sampling algorithm.

Clustering feature selection

Approximate Near Neighbors for General Symmetric Norms

no code implementations18 Nov 2016 Alexandr Andoni, Huy L. Nguyen, Aleksandar Nikolov, Ilya Razenshteyn, Erik Waingarten

We show that every symmetric normed space admits an efficient nearest neighbor search data structure with doubly-logarithmic approximation.

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