31 citations · 68 across the 7 of their papers we have counts for
7 papers
Near-Optimal Disjoint-Path Facility Location Through Set Cover by Pairs
David S. Johnson, Lee Breslau, Ilias Diakonikolas +6
In this paper we consider two special cases of the "cover-by-pairs" optimization problem that arise when we need to place facilities so that each customer is served by two faciliti…
Online Sparse Linear Regression
Dean Foster, Satyen Kale, Howard Karloff
We consider the online sparse linear regression problem, which is the problem of sequentially making predictions observing only a limited number of features in each round, to minim…
Variable Selection is Hard
Dean Foster, Howard Karloff, Justin Thaler
Variable selection for sparse linear regression is the problem of finding, given an m x p matrix B and a target vector y, a sparse vector x such that Bx approximately equals y. Ass…
Fast Algorithms for Constructing Maximum Entropy Summary Trees
Richard Cole, Howard Karloff
Karloff? and Shirley recently proposed summary trees as a new way to visualize large rooted trees (Eurovis 2013) and gave algorithms for generating a maximum-entropy k-node summary…
Distributed Data Placement via Graph Partitioning
Lukasz Golab, Marios Hadjieleftheriou, Howard Karloff +1
With the widespread use of shared-nothing clusters of servers, there has been a proliferation of distributed object stores that offer high availability, reliability and enhanced pe…
On Parsimonious Explanations for 2-D Tree- and Linearly-Ordered Data
Howard Karloff, Flip Korn, Konstantin Makarychev +1
This paper studies the "explanation problem" for tree- and linearly-ordered array data, a problem motivated by database applications and recently solved for the one-dimensional tre…
Combining geometry and combinatorics: A unified approach to sparse signal recovery
R. Berinde, A. C. Gilbert, P. Indyk +2
There are two main algorithmic approaches to sparse signal recovery: geometric and combinatorial. The geometric approach starts with a geometric constraint on the measurement matri…