Gregory Valiant
Gregory Valiant

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gregory.valiantatgmail.com

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Bibliometrics: publication history
Average citations per article9.15
Citation Count302
Publication count33
Publication years2006-2017
Available for download19
Average downloads per article285.00
Downloads (cumulative)5,415
Downloads (12 Months)1,034
Downloads (6 Weeks)133
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33 results found Export Results: bibtexendnoteacmrefcsv

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1 published by ACM
October 2017 Journal of the ACM (JACM): Volume 64 Issue 6, November 2017
Publisher: ACM
Bibliometrics:
Citation Count: 0
Downloads (6 Weeks): 51,   Downloads (12 Months): 205,   Downloads (Overall): 205

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We show that a class of statistical properties of distributions, which includes such practically relevant properties as entropy, the number of distinct elements, and distance metrics between pairs of distributions, can be estimated given a sublinear sized sample. Specifically, given a sample consisting of independent draws from any distribution over ...
Keywords: unseen species, entropy estimation, Statistical property estimation, distinct elements

2 published by ACM
June 2017 STOC 2017: Proceedings of the 49th Annual ACM SIGACT Symposium on Theory of Computing
Publisher: ACM
Bibliometrics:
Citation Count: 0
Downloads (6 Weeks): 41,   Downloads (12 Months): 278,   Downloads (Overall): 278

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The vast majority of theoretical results in machine learning and statistics assume that the training data is a reliable reflection of the phenomena to be learned. Similarly, most learning techniques used in practice are brittle to the presence of large amounts of biased or malicious data. Motivated by this, we ...
Keywords: high-dimensional statistics, outlier removal, robust learning

3
December 2016 NIPS'16: Proceedings of the 30th International Conference on Neural Information Processing Systems
Publisher: Curran Associates Inc.
Bibliometrics:
Citation Count: 1
Downloads (6 Weeks): 0,   Downloads (12 Months): 0,   Downloads (Overall): 0

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We consider a crowdsourcing model in which n workers are asked to rate the quality of n items previously generated by other workers. An unknown set of α n workers generate reliable ratings, while the remaining workers may behave arbitrarily and possibly adversarially. The manager of the experiment can also ...

4 published by ACM
June 2016 STOC '16: Proceedings of the forty-eighth annual ACM symposium on Theory of Computing
Publisher: ACM
Bibliometrics:
Citation Count: 2
Downloads (6 Weeks): 6,   Downloads (12 Months): 106,   Downloads (Overall): 225

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We consider the following basic learning task: given independent draws from an unknown distribution over a discrete support, output an approximation of the distribution that is as accurate as possible in L1 distance (equivalently, total variation distance, or "statistical distance"). Perhaps surprisingly, it is often possible to "de-noise" the empirical ...
Keywords: Good-Turing frequency estimation, instance optimality, the unseen species problem, Distribution learning, property estimation

5
December 2015 NIPS'15: Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 2
Publisher: MIT Press
Bibliometrics:
Citation Count: 1

We consider the problem of testing whether two unequal-sized samples were drawn from identical distributions, versus distributions that differ significantly. Specifically, given a target error parameter ε > 0, m 1 independent draws from an unknown distribution p with discrete support, and m 2 draws from an unknown distribution q ...

6 published by ACM
May 2015 Journal of the ACM (JACM): Volume 62 Issue 2, May 2015
Publisher: ACM
Bibliometrics:
Citation Count: 6
Downloads (6 Weeks): 9,   Downloads (12 Months): 83,   Downloads (Overall): 294

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Given a set of n d -dimensional Boolean vectors with the promise that the vectors are chosen uniformly at random with the exception of two vectors that have Pearson correlation coefficient ρ (Hamming distance d ċ 1−ρ&frac;2), how quickly can one find the two correlated vectors? We present an algorithm ...
Keywords: Correlations, parity with noise, locality sensitive hashing, metric embedding, approximate closest pair, asymmetric embeddings, learning juntas, nearest neighbor

7
October 2014 FOCS '14: Proceedings of the 2014 IEEE 55th Annual Symposium on Foundations of Computer Science
Publisher: IEEE Computer Society
Bibliometrics:
Citation Count: 7

We consider the problem of verifying the identity of a distribution: Given the description of a distribution over a discrete support p = (p1, p2,…, pn) how many samples (independent draws) must one obtain from an unknown distribution, q, to distinguish, with high probability, the case that p = q ...
Keywords: Property Testing, Identity Testing, Instance Optimal, Automated Theorem Proving, Cauchy-Schwarz inequality

8
October 2014 FOCS '14: Proceedings of the 2014 IEEE 55th Annual Symposium on Foundations of Computer Science
Publisher: IEEE Computer Society
Bibliometrics:
Citation Count: 0

We show that, if truth assignments on n variables reproduce through recombination so that satisfaction of a particular Boolean function confers a small evolutionary advantage, then a polynomially large population over polynomially many generations (polynomial in n and the inverse of the initial satisfaction probability) will end up almost certainly ...
Keywords: evolution, algorithms, Boolean functions

9
June 2014 ICML'14: Proceedings of the 31st International Conference on International Conference on Machine Learning - Volume 32
Publisher: JMLR.org
Bibliometrics:
Citation Count: 1

This work provides simple algorithms for multiclass (and multi-label) prediction in settings where both the number of examples n and the data dimension d are relatively large. These robust and parameter free algorithms are essentially iterative least-squares updates and very versatile both in theory and in practice. On the theoretical ...

10
June 2014 ICML'14: Proceedings of the 31st International Conference on International Conference on Machine Learning - Volume 32
Publisher: JMLR.org
Bibliometrics:
Citation Count: 0

We study the effectiveness of learning low degree polynomials using neural networks by the gradient descent method. While neural networks have been shown to have great expressive power, and gradient descent has been widely used in practice for learning neural networks, few theoretical guarantees are known for such methods. In ...

11
January 2014 SODA '14: Proceedings of the twenty-fifth annual ACM-SIAM symposium on Discrete algorithms
Publisher: Society for Industrial and Applied Mathematics
Bibliometrics:
Citation Count: 6
Downloads (6 Weeks): 1,   Downloads (12 Months): 11,   Downloads (Overall): 82

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We study the question of learning a sparse multivariate polynomial over the real domain. In particular, for some unknown polynomial f ( x ) of degree- d and k monomials, we show how to reconstruct f , within error ε, given only a set of examples x i drawn uniformly ...

12
January 2014 SODA '14: Proceedings of the twenty-fifth annual ACM-SIAM symposium on Discrete algorithms
Publisher: Society for Industrial and Applied Mathematics
Bibliometrics:
Citation Count: 11
Downloads (6 Weeks): 1,   Downloads (12 Months): 29,   Downloads (Overall): 91

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We study the question of closeness testing for two discrete distributions. More precisely, given samples from two distributions p and q over an n -element set, we wish to distinguish whether p = q versus p is at least ε-far from q , in either ℓ 1 or ℓ 2 ...

13
December 2013 NIPS'13: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2
Publisher: Curran Associates Inc.
Bibliometrics:
Citation Count: 0

Recently, Valiant and Valiant [1, 2] showed that a class of distributional properties, which includes such practically relevant properties as entropy, the number of distinct elements, and distance metrics between pairs of distributions, can be estimated given a sublinear sized sample. Specifically, given a sample consisting of independent draws from ...

14
January 2013 SODA '13: Proceedings of the twenty-fourth annual ACM-SIAM symposium on Discrete algorithms
Publisher: Society for Industrial and Applied Mathematics
Bibliometrics:
Citation Count: 7
Downloads (6 Weeks): 0,   Downloads (12 Months): 7,   Downloads (Overall): 35

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We give highly efficient algorithms, and almost matching lower bounds, for a range of basic statistical problems that involve testing and estimating the L 1 (total variation) distance between two k -modal distributions p and q over the discrete domain {1,..., n }. More precisely, we consider the following four ...

15
October 2012 FOCS '12: Proceedings of the 2012 IEEE 53rd Annual Symposium on Foundations of Computer Science
Publisher: IEEE Computer Society
Bibliometrics:
Citation Count: 20

Given a set of $n$ $d$-dimensional Boolean vectors with the promise that the vectors are chosen uniformly at random with the exception of two vectors that have Pearson -- correlation $\rho$ (Hamming distance $d\cdot \frac{1-\rho}{2}$), how quickly can one find the two correlated vectors? We present an algorithm which, for ...
Keywords: Correlation, closest pair, nearest neighbor, locality sensitive hashing, learning parity with noise, learning juntas, metric embedding

16 published by ACM
June 2012 Journal of the ACM (JACM): Volume 59 Issue 3, June 2012
Publisher: ACM
Bibliometrics:
Citation Count: 10
Downloads (6 Weeks): 4,   Downloads (12 Months): 23,   Downloads (Overall): 338

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This article provides new worst-case bounds for the size and treewith of the result Q ( D ) of a conjunctive query Q applied to a database D . We derive bounds for the result size | Q ( D )| in terms of structural properties of Q , both ...
Keywords: Database theory, conjunctive queries, size bounds, treewidth

17 published by ACM
February 2012 Communications of the ACM: Volume 55 Issue 2, February 2012
Publisher: ACM
Bibliometrics:
Citation Count: 6
Downloads (6 Weeks): 2,   Downloads (12 Months): 120,   Downloads (Overall): 1,541

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18
January 2012
Bibliometrics:
Citation Count: 1

In this dissertation, we apply the computational perspective to three basic statistical questions which underlie and abstract several of the challenges encountered in the analysis of today's large datasets. Estimating Statistical Properties Given a sample drawn from an unknown distribution, and a specific statistical property of the distribution that we ...

19
October 2011 FOCS '11: Proceedings of the 2011 IEEE 52nd Annual Symposium on Foundations of Computer Science
Publisher: IEEE Computer Society
Bibliometrics:
Citation Count: 10

For a broad class of practically relevant distribution properties, which includes entropy and support size, nearly all of the proposed estimators have an especially simple form. Given a set of independent samples from a discrete distribution, these estimators tally the vector of summary statistics -- the number of domain elements ...
Keywords: Property Testing, Entropy Estimation, L1 Estimation, Duality

20 published by ACM
June 2011 PODC '11: Proceedings of the 30th annual ACM SIGACT-SIGOPS symposium on Principles of distributed computing
Publisher: ACM
Bibliometrics:
Citation Count: 0
Downloads (6 Weeks): 2,   Downloads (12 Months): 10,   Downloads (Overall): 97

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Under many distributed protocols, the prescribed behavior for participants is to behave greedily, i.e., to repeatedly "best respond" to the others' actions. We present recent work (Proc. ICS'11) where we tackle the following general question: "When is it best for a long-sighted participant to adhere to a distributed greedy protocol?" ...
Keywords: greedy protocols, game theory



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