scispace - formally typeset
Search or ask a question
Institution

AT&T Labs

Company
About: AT&T Labs is a based out in . It is known for research contribution in the topics: Network packet & The Internet. The organization has 1879 authors who have published 5595 publications receiving 483151 citations.


Papers
More filters
Proceedings ArticleDOI
David McAllester1
24 Jul 1998
TL;DR: The PAC-Bayesian theorems given here apply to an arbitrary prior measure on an arbitrary concept space and provide an alternative to the use of VC dimension in proving PAC bounds for parameterized concepts.
Abstract: This paper gives PAC guarantees for “Bayesian” algorithms—algorithms that optimize risk minimization expressions involving a prior probability and a likelihood for the training data. PAC-Bayesian algorithms are motivated by a desire to provide an informative prior encoding information about the expected experimental setting but still having PAC performance guarantees over all IID settings. The PAC-Bayesian theorems given here apply to an arbitrary prior measure on an arbitrary concept space. These theorems provide an alternative to the use of VC dimension in proving PAC bounds for parameterized concepts.

549 citations

Proceedings ArticleDOI
01 Nov 2000
TL;DR: This paper presents the design and implementation of a distributed rewall using the KeyNote trust management system to specify, distribute, and resolve policy, and OpenBSD, an open source UNIX operating system.
Abstract: Conventional rewalls rely on topology restrictions and controlled network entry points to enforce traAEc ltering. Furthermore, a rewall cannot lter traAEc it does not see, so, e ectively, everyone on the protected side is trusted. While this model has worked well for small to medium size networks, networking trends such as increased connectivity, higher line speeds, extranets, and telecommuting threaten to make it obsolete. To address the shortcomings of traditional rewalls, the concept of a \distributed rewall" has been proposed. In this scheme, security policy is still centrally de ned, but enforcement is left up to the individual endpoints. IPsec may be used to distribute credentials that express parts of the overall network policy. Alternately, these credentials may be obtained through out-of-band means. In this paper, we present the design and implementation of a distributed rewall using the KeyNote trust management system to specify, distribute, and resolve policy, and OpenBSD, an open source UNIX operating system.

548 citations

Proceedings ArticleDOI
Robert M. Bell1, Yehuda Koren1
28 Oct 2007
TL;DR: This work enhances the neighborhood-based approach leading to substantial improvement of prediction accuracy, without a meaningful increase in running time, and suggests a novel scheme for low dimensional embedding of the users.
Abstract: Recommender systems based on collaborative filtering predict user preferences for products or services by learning past user-item relationships. A predominant approach to collaborative filtering is neighborhood based ("k-nearest neighbors"), where a user-item preference rating is interpolated from ratings of similar items and/or users. We enhance the neighborhood-based approach leading to substantial improvement of prediction accuracy, without a meaningful increase in running time. First, we remove certain so-called "global effects" from the data to make the ratings more comparable, thereby improving interpolation accuracy. Second, we show how to simultaneously derive interpolation weights for all nearest neighbors, unlike previous approaches where each weight is computed separately. By globally solving a suitable optimization problem, this simultaneous interpolation accounts for the many interactions between neighbors leading to improved accuracy. Our method is very fast in practice, generating a prediction in about 0.2 milliseconds. Importantly, it does not require training many parameters or a lengthy preprocessing, making it very practical for large scale applications. Finally, we show how to apply these methods to the perceivably much slower user-oriented approach. To this end, we suggest a novel scheme for low dimensional embedding of the users. We evaluate these methods on the netflix dataset, where they deliver significantly better results than the commercial netflix cinematch recommender system.

547 citations

Proceedings ArticleDOI
02 Nov 2011
TL;DR: A survey is deployed to 200 Facebook users recruited via Amazon Mechanical Turk, finding that 36% of content remains shared with the default privacy settings, and overall, privacy settings match users' expectations only 37% of the time, and when incorrect, almost always expose content to more users than expected.
Abstract: The sharing of personal data has emerged as a popular activity over online social networking sites like Facebook. As a result, the issue of online social network privacy has received significant attention in both the research literature and the mainstream media. Our overarching goal is to improve defaults and provide better tools for managing privacy, but we are limited by the fact that the full extent of the privacy problem remains unknown; there is little quantification of the incidence of incorrect privacy settings or the difficulty users face when managing their privacy.In this paper, we focus on measuring the disparity between the desired and actual privacy settings, quantifying the magnitude of the problem of managing privacy. We deploy a survey, implemented as a Facebook application, to 200 Facebook users recruited via Amazon Mechanical Turk. We find that 36% of content remains shared with the default privacy settings. We also find that, overall, privacy settings match users' expectations only 37% of the time, and when incorrect, almost always expose content to more users than expected. Finally, we explore how our results have potential to assist users in selecting appropriate privacy settings by examining the user-created friend lists. We find that these have significant correlation with the social network, suggesting that information from the social network may be helpful in implementing new tools for managing privacy.

545 citations

Journal ArticleDOI
16 May 2000
TL;DR: A tool for compressing XML data, with applications in data exchange and archiving, which usually achieves about twice the compression ratio of gzip at roughly the same speed.
Abstract: We describe a tool for compressing XML data, with applications in data exchange and archiving, which usually achieves about twice the compression ratio of gzip at roughly the same speed. The compressor, called XMill, incorporates and combines existing compressors in order to apply them to heterogeneous XML data: it uses zlib, the library function for gzip, a collection of datatype specific compressors for simple data types, and, possibly, user defined compressors for application specific data types.

545 citations


Authors

Showing all 1881 results

NameH-indexPapersCitations
Yoshua Bengio2021033420313
Scott Shenker150454118017
Paul Shala Henry13731835971
Peter Stone130122979713
Yann LeCun121369171211
Louis E. Brus11334763052
Jennifer Rexford10239445277
Andreas F. Molisch9677747530
Vern Paxson9326748382
Lorrie Faith Cranor9232628728
Ward Whitt8942429938
Lawrence R. Rabiner8837870445
Thomas E. Graedel8634827860
William W. Cohen8538431495
Michael K. Reiter8438030267
Network Information
Related Institutions (5)
Microsoft
86.9K papers, 4.1M citations

94% related

Google
39.8K papers, 2.1M citations

91% related

Hewlett-Packard
59.8K papers, 1.4M citations

89% related

Bell Labs
59.8K papers, 3.1M citations

88% related

Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20225
202133
202069
201971
2018100
201791