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1
June 2019
PODS '19: Proceedings of the 38th ACM SIGMODSIGACTSIGAI Symposium on Principles of Database Systems
Publisher: ACM
Bibliometrics:
Citation Count: 0
Downloads (6 Weeks): 7, Downloads (12 Months): 63, Downloads (Overall): 63
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We study the maximum kcoverage problem in the general edgearrival streaming model: given a collection of m sets F, each subset of a ground set of elements U of size n, the task is to find k sets whose coverage is maximized. The sets are specified as a sequence of ...
Keywords:
heavy hitters, max kcover, sketching/streaming
2
August 2018
SIGCOMM '18: Proceedings of the 2018 Conference of the ACM Special Interest Group on Data Communication
Publisher: ACM
Bibliometrics:
Citation Count: 4
Downloads (6 Weeks): 62, Downloads (12 Months): 759, Downloads (Overall): 980
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There is much interest in integrating millimeter wave radios (mmWave) into wireless LANs and 5G cellular networks to benefit from their multiGHz of available spectrum. Yet, unlike existing technologies, e.g., WiFi, mmWave radios require highly directional antennas. Since the antennas have pencilbeams, the transmitter and receiver need to align their ...
Keywords:
5G, beam alignment, millimeter wave, sparse recovery
3
January 2018
SODA '18: Proceedings of the TwentyNinth Annual ACMSIAM Symposium on Discrete Algorithms
Publisher: Society for Industrial and Applied Mathematics
Bibliometrics:
Citation Count: 0
Downloads (6 Weeks): 5, Downloads (12 Months): 22, Downloads (Overall): 43
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We study the classic set cover problem from the perspective of sublinear algorithms. Given access to a collection of m sets over n elements in the query model, we show that sublinear algorithms derived from existing techniques have almost tight query complexities. On one hand, first we show an adaptation ...
4
December 2017
NIPS'17: Proceedings of the 31st International Conference on Neural Information Processing Systems
Publisher: Curran Associates Inc.
Bibliometrics:
Citation Count: 0
Downloads (6 Weeks): 0, Downloads (12 Months): 0, Downloads (Overall): 0
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Empirical risk minimization (ERM) is ubiquitous in machine learning and underlies most supervised learning methods. While there is a large body of work on algorithms for various ERM problems, the exact computational complexity of ERM is still not understood. We address this issue for multiple popular ERM problems including kernel ...
5
December 2017
NIPS'17: Proceedings of the 31st International Conference on Neural Information Processing Systems
Publisher: Curran Associates Inc.
Bibliometrics:
Citation Count: 0
Downloads (6 Weeks): 0, Downloads (12 Months): 0, Downloads (Overall): 0
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We introduce a new distancepreserving compact representation of multidimensional pointsets. Given n points in a d dimensional space where each coordinate is represented using B bits (i.e., dB bits per point), it produces a representation of size O ( d log( dB /ε) + log n ) bits per point ...
6
July 2017
SPAA '17: Proceedings of the 29th ACM Symposium on Parallelism in Algorithms and Architectures
Publisher: ACM
Bibliometrics:
Citation Count: 1
Downloads (6 Weeks): 2, Downloads (12 Months): 23, Downloads (Overall): 161
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The theory of NPhardness has been very successful in identifying problems that are unlikely to be solvable in polynomial time. However, many other important problems do have polynomial time algorithms, but large exponents in their time bounds can make them run for days, weeks or more. For example, quadratic time ...
Keywords:
edit distance, finegrained complexity, neural networks, regular expression matching, support vector machines
7
March 2017
ACM Transactions on Algorithms (TALG): Volume 13 Issue 3, August 2017
Publisher: ACM
Bibliometrics:
Citation Count: 1
Downloads (6 Weeks): 1, Downloads (12 Months): 33, Downloads (Overall): 148
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For every fixed constant α > 0, we design an algorithm for computing the k sparse WalshHadamard transform (i.e., Discrete Fourier Transform over the Boolean cube) of an N dimensional vector x ∈ R N in time k 1 + α (log N ) O (1) . Specifically, the algorithm ...
Keywords:
Sparse recovery, explicit constructions, pseudorandomness, sketching, sparse Fourier transform, sublinear time algorithms
8
January 2017
SODA '17: Proceedings of the TwentyEighth Annual ACMSIAM Symposium on Discrete Algorithms
Publisher: Society for Industrial and Applied Mathematics
Bibliometrics:
Citation Count: 1
Downloads (6 Weeks): 0, Downloads (12 Months): 2, Downloads (Overall): 39
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The metric sketching problem is defined as follows. Given a metric on n points, and ϵ > 0, we wish to produce a small size data structure (sketch) that, given any pair of point indices, recovers the distance between the points up to a 1 + ϵ distortion. In this ...
9
January 2017
SODA '17: Proceedings of the TwentyEighth Annual ACMSIAM Symposium on Discrete Algorithms
Publisher: Society for Industrial and Applied Mathematics
Bibliometrics:
Citation Count: 4
Downloads (6 Weeks): 0, Downloads (12 Months): 1, Downloads (Overall): 27
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The Tree Sparsity problem is defined as follows: given a nodeweighted tree of size n and an integer k , output a rooted subtree of size k with maximum weight. The best known algorithm solves this problem in time O ( kn ), i.e., quadratic in the size of the ...
10
December 2016
NIPS'16: Proceedings of the 30th International Conference on Neural Information Processing Systems
Publisher: Curran Associates Inc.
Bibliometrics:
Citation Count: 0
Downloads (6 Weeks): 0, Downloads (12 Months): 0, Downloads (Overall): 0
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We address the problem of recovering a highdimensional but structured vector from linear observations in a general setting where the vector can come from an arbitrary union of subspaces. This setup includes wellstudied problems such as compressive sensing and lowrank matrix recovery. We show how to design more efficient algorithms ...
11
July 2016
IJCAI'16: Proceedings of the TwentyFifth International Joint Conference on Artificial Intelligence
Publisher: AAAI Press
We introduce a framework for sparsity structures defined via graphs. Our approach is flexible and generalizes several previously studied sparsity models. Moreover, we provide efficient projection algorithms for our sparsity model that run in nearlylinear time. In the context of sparse recovery, our framework achieves an informationtheoretically optimal sample complexity ...
12
June 2016
PODS '16: Proceedings of the 35th ACM SIGMODSIGACTSIGAI Symposium on Principles of Database Systems
Publisher: ACM
Bibliometrics:
Citation Count: 5
Downloads (6 Weeks): 1, Downloads (12 Months): 11, Downloads (Overall): 132
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We consider the classic Set Cover problem in the data stream model. For n elements and m sets (m ≥ n) we give a O(1/δ)pass algorithm with a strongly sublinear ~O(mn δ ) space and logarithmic approximation factor. This yields a significant improvement over the earlier algorithm of Demaine et ...
Keywords:
geometric set cover, lower bound, multiple pass, sampling, set cover, streaming algorithms
13
January 2016
SODA '16: Proceedings of the twentyseventh annual ACMSIAM symposium on Discrete algorithms
Publisher: Society for Industrial and Applied Mathematics
Bibliometrics:
Citation Count: 0
Downloads (6 Weeks): 1, Downloads (12 Months): 40, Downloads (Overall): 44
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We initiate the study of tradeoffs between sparsity and the number of measurements in sparse recovery schemes for generic norms. Specifically for a norm ·, sparsity parameter k , approximation factor K > 0, and probability of failure P > 0, we ask: what is the minimal value of m ...
14
January 2016
SODA '16: Proceedings of the twentyseventh annual ACMSIAM symposium on Discrete algorithms
Publisher: Society for Industrial and Applied Mathematics
Bibliometrics:
Citation Count: 1
Downloads (6 Weeks): 3, Downloads (12 Months): 26, Downloads (Overall): 39
Full text available:
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For every fixed constant α > 0, we design an algorithm for computing the k sparse WalshHadamard transform (i.e., Discrete Fourier Transform over the Boolean cube) of an N dimensional vector x ∈ R N in time k 1+α (log N ) O (1) . Specifically, the algorithm is given ...
15
December 2015
NIPS'15: Proceedings of the 28th International Conference on Neural Information Processing Systems  Volume 1
Publisher: MIT Press
We show the existence of a LocalitySensitive Hashing (LSH) family for the angular distance that yields an approximate Near Neighbor Search algorithm with the asymptotically optimal running time exponent. Unlike earlier algorithms with this property (e.g., Spherical LSH [1, 2]), our algorithm is also practical, improving upon the wellstudied hyperplane ...
16
July 2015
ICML'15: Proceedings of the 32nd International Conference on International Conference on Machine Learning  Volume 37
Publisher: JMLR.org
We introduce a framework for sparsity structures defined via graphs. Our approach is flexible and generalizes several previously studied sparsity models. Moreover, we provide efficient projection algorithms for our sparsity model that run in nearlylinear time. In the context of sparse recovery, we show that our framework achieves an informationtheoretically ...
17
June 2015
STOC '15: Proceedings of the fortyseventh annual ACM symposium on Theory of Computing
Publisher: ACM
Bibliometrics:
Citation Count: 45
Downloads (6 Weeks): 6, Downloads (12 Months): 82, Downloads (Overall): 540
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The edit distance (a.k.a. the Levenshtein distance) between two strings is defined as the minimum number of insertions, deletions or substitutions of symbols needed to transform one string into another. The problem of computing the edit distance between two strings is a classical computational task, with a wellknown algorithm based ...
Keywords:
SETH, conditional lower bounds, edit distance, pattern matching
18
May 2015
PODS '15: Proceedings of the 34th ACM SIGMODSIGACTSIGAI Symposium on Principles of Database Systems
Publisher: ACM
Bibliometrics:
Citation Count: 0
Downloads (6 Weeks): 1, Downloads (12 Months): 8, Downloads (Overall): 153
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Keywords:
property testing, distribution, histogram
19
January 2015
Proceedings of the VLDB Endowment: Volume 8 Issue 5, January 2015
Publisher: VLDB Endowment
Bibliometrics:
Citation Count: 7
Downloads (6 Weeks): 8, Downloads (12 Months): 62, Downloads (Overall): 134
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Visualizations are frequently used as a means to understand trends and gather insights from datasets, but often take a long time to generate. In this paper, we focus on the problem of rapidly generating approximate visualizations while preserving crucial visual properties of interest to analysts. Our primary focus will be ...
20
October 2014
FOCS '14: Proceedings of the 2014 IEEE 55th Annual Symposium on Foundations of Computer Science
Publisher: IEEE Computer Society
We give an algorithm for l2/l2 sparse recovery from Fourier measurements using O(klog N) samples, matching the lower bound of Do BaIndykPriceWoodruff'10 for nonadaptive algorithms up to constant factors for any k≤ N1δ. The algorithm runs in tilde O(N) time. Our algorithm extends to higher dimensions, leading to sample complexity ...
Keywords:
sparse Fourier Transform, sample complexity, compressed sensing, sparse recovery

