no code implementations • 16 Apr 2024 • Hilal Asi, Vitaly Feldman, Jelani Nelson, Huy L. Nguyen, Kunal Talwar, Samson Zhou
We study the problem of private vector mean estimation in the shuffle model of privacy where $n$ users each have a unit vector $v^{(i)} \in\mathbb{R}^d$.
no code implementations • 16 Feb 2024 • Chunkai Fu, Jung Hoon Seo, Samson Zhou
We study the integration of machine learning advice into the design of skip lists to improve upon traditional data structure design.
no code implementations • 2 Jun 2023 • Ameya Velingker, Maximilian Vötsch, David P. Woodruff, Samson Zhou
We introduce efficient $(1+\varepsilon)$-approximation algorithms for the binary matrix factorization (BMF) problem, where the inputs are a matrix $\mathbf{A}\in\{0, 1\}^{n\times d}$, a rank parameter $k>0$, as well as an accuracy parameter $\varepsilon>0$, and the goal is to approximate $\mathbf{A}$ as a product of low-rank factors $\mathbf{U}\in\{0, 1\}^{n\times k}$ and $\mathbf{V}\in\{0, 1\}^{k\times d}$.
1 code implementation • 9 Mar 2023 • Murad Tukan, Samson Zhou, Alaa Maalouf, Daniela Rus, Vladimir Braverman, Dan Feldman
In this paper, we introduce the first algorithm to construct coresets for \emph{RBFNNs}, i. e., small weighted subsets that approximate the loss of the input data on any radial basis function network and thus approximate any function defined by an \emph{RBFNN} on the larger input data.
no code implementations • 3 Mar 2023 • David P. Woodruff, Fred Zhang, Samson Zhou
In the online learning with experts problem, an algorithm must make a prediction about an outcome on each of $T$ days (or times), given a set of $n$ experts who make predictions on each day (or time).
no code implementations • 22 Feb 2023 • Guangyao Zheng, Samson Zhou, Vladimir Braverman, Michael A. Jacobs, Vishwa S. Parekh
Selective experience replay aims to recount selected experiences from previous tasks to avoid catastrophic forgetting.
no code implementations • 11 Feb 2023 • Itai Dinur, Uri Stemmer, David P. Woodruff, Samson Zhou
We study the space complexity of the two related fields of differential privacy and adaptive data analysis.
no code implementations • 1 Dec 2022 • Ainesh Bakshi, Piotr Indyk, Praneeth Kacham, Sandeep Silwal, Samson Zhou
We build on the recent Kernel Density Estimation framework, which (after preprocessing in time subquadratic in $n$) can return estimates of row/column sums of the kernel matrix.
no code implementations • 21 Sep 2022 • Elena Grigorescu, Young-San Lin, Sandeep Silwal, Maoyuan Song, Samson Zhou
We show that if the predictor is accurate, we can efficiently bypass these impossibility results and achieve a constant-factor approximation to the optimal solution, i. e., consistency.
no code implementations • 16 Jul 2022 • Sepideh Mahabadi, David P. Woodruff, Samson Zhou
In this paper, we introduce an algorithm that approximately samples $T$ gradients of dimension $d$ from nearly the optimal importance sampling distribution for a robust regression problem over $n$ rows.
no code implementations • 29 Jun 2022 • Eric Price, Sandeep Silwal, Samson Zhou
We further show fine-grained hardness of robust regression through a reduction from the minimum-weight $k$-clique conjecture.
no code implementations • 21 Apr 2022 • Vaidehi Srinivas, David P. Woodruff, Ziyu Xu, Samson Zhou
We initiate the study of the learning with expert advice problem in the streaming setting, and show lower and upper bounds.
1 code implementation • 8 Mar 2022 • Murad Tukan, Xuan Wu, Samson Zhou, Vladimir Braverman, Dan Feldman
$(j, k)$-projective clustering is the natural generalization of the family of $k$-clustering and $j$-subspace clustering problems.
no code implementations • ICLR 2022 • Jon C. Ergun, Zhili Feng, Sandeep Silwal, David P. Woodruff, Samson Zhou
$k$-means clustering is a well-studied problem due to its wide applicability.
no code implementations • NeurIPS 2021 • Zachary Izzo, Sandeep Silwal, Samson Zhou
In order to cope with this "curse of dimensionality," we study dimensionality reduction techniques for the Wasserstein barycenter problem.
no code implementations • NeurIPS 2021 • Vladimir Braverman, Avinatan Hassidim, Yossi Matias, Mariano Schain, Sandeep Silwal, Samson Zhou
In this paper, we introduce adversarially robust streaming algorithms for central machine learning and algorithmic tasks, such as regression and clustering, as well as their more general counterparts, subspace embedding, low-rank approximation, and coreset construction.
no code implementations • 17 May 2021 • Ainesh Bakshi, Chiranjib Bhattacharyya, Ravi Kannan, David P. Woodruff, Samson Zhou
We consider the problem of learning a latent $k$-vertex simplex $K\subset\mathbb{R}^d$, given access to $A\in\mathbb{R}^{d\times n}$, which can be viewed as a data matrix with $n$ points that are obtained by randomly perturbing latent points in the simplex $K$ (potentially beyond $K$).
no code implementations • 1 Jan 2021 • Sepideh Mahabadi, David Woodruff, Samson Zhou
Moreover, we show that our algorithm can be generalized to approximately sample Hessians and thus provides variance reduction for second-order methods as well.
no code implementations • ICLR 2021 • Ainesh Bakshi, Chiranjib Bhattacharyya, Ravi Kannan, David Woodruff, Samson Zhou
Bhattacharyya and Kannan (SODA 2020) give an algorithm for learning such a $k$-vertex latent simplex in time roughly $O(k\cdot\text{nnz}(\mathbf{A}))$, where $\text{nnz}(\mathbf{A})$ is the number of non-zeros in $\mathbf{A}$.
no code implementations • 19 Aug 2020 • Ben Mussay, Daniel Feldman, Samson Zhou, Vladimir Braverman, Margarita Osadchy
Our method is based on the coreset framework and it approximates the output of a layer of neurons/filters by a coreset of neurons/filters in the previous layer and discards the rest.
no code implementations • 23 Jun 2020 • Agniva Chowdhury, Petros Drineas, David P. Woodruff, Samson Zhou
To improve the interpretability of PCA, various approaches to obtain sparse principal direction loadings have been proposed, which are termed Sparse Principal Component Analysis (SPCA).
no code implementations • 23 Apr 2020 • Sepideh Mahabadi, Ilya Razenshteyn, David P. Woodruff, Samson Zhou
Adaptive sampling is a useful algorithmic tool for data summarization problems in the classical centralized setting, where the entire dataset is available to the single processor performing the computation.
1 code implementation • 12 Oct 2019 • Dmitrii Avdiukhin, Grigory Yaroslavtsev, Samson Zhou
Our analysis is based on a new set of first-order linear differential inequalities and their robust approximate versions.
no code implementations • ICLR 2020 • Ben Mussay, Margarita Osadchy, Vladimir Braverman, Samson Zhou, Dan Feldman
We propose the first efficient, data-independent neural pruning algorithm with a provable trade-off between its compression rate and the approximation error for any future test sample.
no code implementations • 7 May 2019 • Dmitrii Avdiukhin, Slobodan Mitrović, Grigory Yaroslavtsev, Samson Zhou
We propose the first adversarially robust algorithm for monotone submodular maximization under single and multiple knapsack constraints with scalable implementations in distributed and streaming settings.