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Found 11 papers, 1 papers with code
(Individual) Fairness for k-Clustering
Intuitively, if a set of $k$ random points are chosen from $P$ as centers, every point $x\in P$ expects to have a center within radius $r(x)$.
Adaptive Sketches for Robust Regression with Importance Sampling
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.
Sampling a Near Neighbor in High Dimensions -- Who is the Fairest of Them All?
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 point $q$, returns a point $p$ within distance at most $r$ from $q$.
Adaptive Single-Pass Stochastic Gradient Descent in Input Sparsity Time
Moreover, we show that our algorithm can be generalized to approximately sample Hessians and thus provides variance reduction for second-order methods as well.
Streaming Complexity of SVMs
We show that, for both problems, for dimensions $d=1, 2$, one can obtain streaming algorithms with space polynomially smaller than $\frac{1}{\lambda\epsilon}$, which is the complexity of SGD for strongly convex functions like the bias-regularized SVM, and which is known to be tight in general, even for $d=1$.
Non-Adaptive Adaptive Sampling on Turnstile Streams
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.
Individual Fairness for $k$-Clustering
Intuitively, if a set of $k$ random points are chosen from $P$ as centers, every point $x\in P$ expects to have a center within radius $r(x)$.
Composable Core-sets for Determinant Maximization: A Simple Near-Optimal Algorithm
In this work, first we provide a theoretical approximation guarantee of $O(C^{k^2})$ for the Greedy algorithm in the context of composable core-sets; Further, we propose to use a Local Search based algorithm that while being still practical, achieves a nearly optimal approximation bound of $O(k)^{2k}$; Finally, we implement all three algorithms and show the effectiveness of our proposed algorithm on standard data sets.
Near Neighbor: Who is the Fairest of Them All?
Namely, given a set of $n$ points $P$ and a parameter $r$, the goal is to preprocess the points, such that given a query point $q$, any point in the $r$-neighborhood of the query, i. e., $\ball(q, r)$, have the same probability of being reported as the near neighbor.
Nonlinear Dimension Reduction via Outer Bi-Lipschitz Extensions
We introduce and study the notion of an outer bi-Lipschitz extension of a map between Euclidean spaces.
Composable Core-sets for Determinant Maximization Problems via Spectral Spanners
We show that for many objective functions one can use a spectral spanner, independent of the underlying functions, as a core-set and obtain almost optimal composable core-sets.
