Zobrazeno 1 - 10
of 58
pro vyhledávání: '"Mahabadi, Sepideh"'
Many methods in differentially private model training rely on computing the similarity between a query point (such as public or synthetic data) and private data. We abstract out this common subroutine and study the following fundamental algorithmic p
Externí odkaz:
http://arxiv.org/abs/2403.08917
We study the $k$-connectivity augmentation problem ($k$-CAP) in the single-pass streaming model. Given a $(k-1)$-edge connected graph $G=(V,E)$ that is stored in memory, and a stream of weighted edges $L$ with weights in $\{0,1,\dots,W\}$, the goal i
Externí odkaz:
http://arxiv.org/abs/2402.10806
Autor:
Mahabadi, Sepideh, Trajanovski, Stojan
We study core-set construction algorithms for the task of Diversity Maximization under fairness/partition constraint. Given a set of points $P$ in a metric space partitioned into $m$ groups, and given $k_1,\ldots,k_m$, the goal of this problem is to
Externí odkaz:
http://arxiv.org/abs/2310.08122
Given a set of points of interest, a volumetric spanner is a subset of the points using which all the points can be expressed using "small" coefficients (measured in an appropriate norm). Formally, given a set of vectors $X = \{v_1, v_2, \dots, v_n\}
Externí odkaz:
http://arxiv.org/abs/2310.00175
Given a set of $n$ vectors in $\mathbb{R}^d$, the goal of the \emph{determinant maximization} problem is to pick $k$ vectors with the maximum volume. Determinant maximization is the MAP-inference task for determinantal point processes (DPP) and has r
Externí odkaz:
http://arxiv.org/abs/2309.15286
Autor:
Mahabadi, Sepideh, Narayanan, Shyam
In this work we consider the diversity maximization problem, where given a data set $X$ of $n$ elements, and a parameter $k$, the goal is to pick a subset of $X$ of size $k$ maximizing a certain diversity measure. [CH01] defined a variety of diversit
Externí odkaz:
http://arxiv.org/abs/2307.04329
This paper studies the fair range clustering problem in which the data points are from different demographic groups and the goal is to pick $k$ centers with the minimum clustering cost such that each group is at least minimally represented in the cen
Externí odkaz:
http://arxiv.org/abs/2306.06778
Autor:
Mahabadi, Sepideh, Vuong, Thuy-Duong
We study the task of determinant maximization under partition constraint, in the context of large data sets. Given a point set $V\subset \mathbb{R}^d$ that is partitioned into $s$ groups $V_1,..., V_s$, and integers $k_1,...,k_s$ where $k=\sum_i k_i$
Externí odkaz:
http://arxiv.org/abs/2211.00289
We introduce data structures for solving robust regression through stochastic gradient descent (SGD) by sampling gradients with probability proportional to their norm, i.e., importance sampling. Although SGD is widely used for large scale machine lea
Externí odkaz:
http://arxiv.org/abs/2207.07822
Similarity search is a fundamental algorithmic primitive, widely used in many computer science disciplines. Given a set of points $S$ and a radius parameter $r>0$, the $r$-near neighbor ($r$-NN) problem asks for a data structure that, given any query
Externí odkaz:
http://arxiv.org/abs/2101.10905