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A SOUND TYPE SYSTEM FOR SECURE FLOW ANALYSIS

by Dennis Volpano, Geoffrey Smith, Cynthia Irvine , 1996
"... Ensuring secure information ow within programs in the context of multiple sensitivity levels has been widely studied. Especially noteworthy is Denning's work in secure ow analysis and the lattice model [6][7]. Until now, however, the soundness of Denning's analysis has not been established ..."
Abstract - Cited by 540 (21 self) - Add to MetaCart
Ensuring secure information ow within programs in the context of multiple sensitivity levels has been widely studied. Especially noteworthy is Denning's work in secure ow analysis and the lattice model [6][7]. Until now, however, the soundness of Denning's analysis has not been

Graphical models, exponential families, and variational inference

by Martin J. Wainwright, Michael I. Jordan , 2008
"... The formalism of probabilistic graphical models provides a unifying framework for capturing complex dependencies among random variables, and building large-scale multivariate statistical models. Graphical models have become a focus of research in many statistical, computational and mathematical fiel ..."
Abstract - Cited by 819 (28 self) - Add to MetaCart
likelihoods, marginal probabilities and most probable configurations. We describe how a wide varietyof algorithms — among them sum-product, cluster variational methods, expectation-propagation, mean field methods, max-product and linear programming relaxation, as well as conic programming relaxations — can

The Cache Performance and Optimizations of Blocked Algorithms

by Monica S. Lam, Edward E. Rothberg, Michael E. Wolf - In Proceedings of the Fourth International Conference on Architectural Support for Programming Languages and Operating Systems , 1991
"... Blocking is a well-known optimization technique for improving the effectiveness of memory hierarchies. Instead of operating on entire rows or columns of an array, blocked algorithms operate on submatrices or blocks, so that data loaded into the faster levels of the memory hierarchy are reused. This ..."
Abstract - Cited by 574 (5 self) - Add to MetaCart
. This paper presents cache performance data for blocked programs and evaluates several optimizations to improve this performance. The data is obtained by a theoretical model of data conflicts in the cache, which has been validated by large amounts of simulation. We show that the degree of cache interference

On the (im)possibility of obfuscating programs

by Boaz Barak, Oded Goldreich, Russell Impagliazzo, Steven Rudich, Amit Sahai, Salil Vadhan, Ke Yang - Lecture Notes in Computer Science , 2001
"... Informally, an obfuscator O is an (efficient, probabilistic) “compiler ” that takes as input a program (or circuit) P and produces a new program O(P) that has the same functionality as P yet is “unintelligible ” in some sense. Obfuscators, if they exist, would have a wide variety of cryptographic an ..."
Abstract - Cited by 348 (24 self) - Add to MetaCart
Informally, an obfuscator O is an (efficient, probabilistic) “compiler ” that takes as input a program (or circuit) P and produces a new program O(P) that has the same functionality as P yet is “unintelligible ” in some sense. Obfuscators, if they exist, would have a wide variety of cryptographic

Strictly Proper Scoring Rules, Prediction, and Estimation

by Tilmann GNEITING , Adrian E. RAFTERY , 2007
"... Scoring rules assess the quality of probabilistic forecasts, by assigning a numerical score based on the predictive distribution and on the event or value that materializes. A scoring rule is proper if the forecaster maximizes the expected score for an observation drawn from the distribution F if he ..."
Abstract - Cited by 373 (28 self) - Add to MetaCart
Scoring rules assess the quality of probabilistic forecasts, by assigning a numerical score based on the predictive distribution and on the event or value that materializes. A scoring rule is proper if the forecaster maximizes the expected score for an observation drawn from the distribution F

ESP: Path-Sensitive Program Verification in Polynomial Time

by Manuvir Das, Sorin Lerner, Mark Seigle , 2002
"... In this paper, we present a new algorithm for partial program verification that runs in polynomial time and space. We are interested in checking that a program satisfies a given temporal safety property. Our insight is that by accurately modeling only those branches in a program for which the proper ..."
Abstract - Cited by 299 (4 self) - Add to MetaCart
In this paper, we present a new algorithm for partial program verification that runs in polynomial time and space. We are interested in checking that a program satisfies a given temporal safety property. Our insight is that by accurately modeling only those branches in a program for which

Bucket Elimination: A Unifying Framework for Probabilistic Inference

by Rina Dechter , 1996
"... Probabilistic inference algorithms for belief updating, finding the most probable explanation, the maximum a posteriori hypothesis, and the maximum expected utility are reformulated within the bucket elimination framework. This emphasizes the principles common to many of the algorithms appearing in ..."
Abstract - Cited by 294 (27 self) - Add to MetaCart
Probabilistic inference algorithms for belief updating, finding the most probable explanation, the maximum a posteriori hypothesis, and the maximum expected utility are reformulated within the bucket elimination framework. This emphasizes the principles common to many of the algorithms appearing

Bucket Elimination: A Unifying Framework for Reasoning

by Rina Dechter
"... Bucket elimination is an algorithmic framework that generalizes dynamic programming to accommodate many problem-solving and reasoning tasks. Algorithms such as directional-resolution for propositional satisfiability, adaptive-consistency for constraint satisfaction, Fourier and Gaussian elimination ..."
Abstract - Cited by 298 (58 self) - Add to MetaCart
elimination for solving linear equalities and inequalities, and dynamic programming for combinatorial optimization, can all be accommodated within the bucket elimination framework. Many probabilistic inference tasks can likewise be expressed as bucket-elimination algorithms. These include: belief updating

Scale-sensitive Dimensions, Uniform Convergence, and Learnability

by Noga Alon, Shai Ben-David, Nicolo Cesa-Bianchi, David Haussler , 1997
"... Learnability in Valiant's PAC learning model has been shown to be strongly related to the existence of uniform laws of large numbers. These laws define a distribution-free convergence property of means to expectations uniformly over classes of random variables. Classes of real-valued functions ..."
Abstract - Cited by 242 (2 self) - Add to MetaCart
Learnability in Valiant's PAC learning model has been shown to be strongly related to the existence of uniform laws of large numbers. These laws define a distribution-free convergence property of means to expectations uniformly over classes of random variables. Classes of real-valued functions

The Bayesian Structural EM Algorithm

by Nir Friedman , 1998
"... In recent years there has been a flurry of works on learning Bayesian networks from data. One of the hard problems in this area is how to effectively learn the structure of a belief network from incomplete data---that is, in the presence of missing values or hidden variables. In a recent paper, I in ..."
Abstract - Cited by 260 (13 self) - Add to MetaCart
approximations to the Bayesian score. In this paper, I extend Structural EM to deal directly with Bayesian model selection. I prove the convergence of the resulting algorithm and show how to apply it for learning a large class of probabilistic models, including Bayesian networks and some variants thereof.
Results 1 - 10 of 2,598