no code implementations • 6 Nov 2022 • Anders Aamand, Justin Y. Chen, Piotr Indyk, Shyam Narayanan, Ronitt Rubinfeld, Nicholas Schiefer, Sandeep Silwal, Tal Wagner
However, those simulations involve neural networks for the 'combine' function of size polynomial or even exponential in the number of graph nodes $n$, as well as feature vectors of length linear in $n$.
no code implementations • 14 Apr 2022 • Ronitt Rubinfeld, Arsen Vasilyan
We propose a model by which to systematically study the design of tester-learner pairs $(\mathcal{A},\mathcal{T})$, such that if the distribution on examples in the data passes the tester $\mathcal{T}$ then one can safely trust the output of the agnostic learner $\mathcal{A}$ on the data.
no code implementations • ICLR 2022 • Justin Y. Chen, Talya Eden, Piotr Indyk, Honghao Lin, Shyam Narayanan, Ronitt Rubinfeld, Sandeep Silwal, Tal Wagner, David P. Woodruff, Michael Zhang
We propose data-driven one-pass streaming algorithms for estimating the number of triangles and four cycles, two fundamental problems in graph analytics that are widely studied in the graph data stream literature.
no code implementations • ICLR 2021 • Talya Eden, Piotr Indyk, Shyam Narayanan, Ronitt Rubinfeld, Sandeep Silwal, Tal Wagner
We consider the problem of estimating the number of distinct elements in a large data set (or, equivalently, the support size of the distribution induced by the data set) from a random sample of its elements.
no code implementations • 15 Feb 2021 • Amartya Shankha Biswas, Edward Pyne, Ronitt Rubinfeld
We then prove that no that algorithm with sub-constant error given probe access to random $d$-regular graphs can have runtime better than $\Omega(\sqrt{n}/\log(n))$ per query in expectation, obtaining a nearly matching lower bound.
Data Structures and Algorithms
no code implementations • 6 Oct 2020 • Maryam Aliakbarpour, Amartya Shankha Biswas, Kavya Ravichandran, Ronitt Rubinfeld
Understanding the shape of a distribution of data is of interest to people in a great variety of fields, as it may affect the types of algorithms used for that data.
no code implementations • 9 Jun 2020 • Piotr Indyk, Frederik Mallmann-Trenn, Slobodan Mitrović, Ronitt Rubinfeld
In contrast, we show that if the algorithm is given a prediction of the input sequence, then it can achieve a competitive ratio that tends to $1$ as the prediction error rate tends to $0$.
1 code implementation • 19 Feb 2020 • Amartya Shankha Biswas, Talya Eden, Quanquan C. Liu, Slobodan Mitrović, Ronitt Rubinfeld
Finally, we prove that a recent result of Bera, Pashanasangi and Seshadhri (ITCS 2020) for exactly counting all subgraphs of size at most $5$ can be implemented in the MPC model in total space.
Data Structures and Algorithms Distributed, Parallel, and Cluster Computing
no code implementations • NeurIPS 2019 • Maryam Aliakbarpour, Ilias Diakonikolas, Daniel Kane, Ronitt Rubinfeld
In this paper, we use the framework of property testing to design algorithms to test the properties of the distribution that the data is drawn from with respect to differential privacy.
no code implementations • 6 Jul 2019 • Maryam Aliakbarpour, Ravi Kumar, Ronitt Rubinfeld
In our model, the noisy distribution is a mixture of the original distribution and noise, where the latter is known to the tester either explicitly or via sample access; the form of the noise is also known a priori.
no code implementations • 6 Jul 2019 • Maryam Aliakbarpour, Themis Gouleakis, John Peebles, Ronitt Rubinfeld, Anak Yodpinyanee
We then build on these lower bounds to give $\Omega(n/\log{n})$ lower bounds for testing monotonicity over a matching poset of size $n$ and significantly improved lower bounds over the hypercube poset.
no code implementations • ICML 2018 • Maryam Aliakbarpour, Ilias Diakonikolas, Ronitt Rubinfeld
Our theoretical results significantly improve over the best known algorithms for identity testing, and are the first results for private equivalence testing.
no code implementations • 18 Jul 2017 • Maryam Aliakbarpour, Ilias Diakonikolas, Ronitt Rubinfeld
We investigate the problems of identity and closeness testing over a discrete population from random samples.
no code implementations • 24 Apr 2015 • Clément Canonne, Themis Gouleakis, Ronitt Rubinfeld
We then focus on the question of whether algorithms for sampling correctors can be more efficient in terms of sample complexity than learning algorithms for the analogous families of distributions.
1 code implementation • 8 Jun 2007 • Sofya Raskhodnikova, Dana Ron, Ronitt Rubinfeld, Adam Smith
We raise the question of approximating the compressibility of a string with respect to a fixed compression scheme, in sublinear time.
Data Structures and Algorithms
