@conference {14679,
	title = {Dynamic Enforcement of Knowledge-Based Security Policies},
	booktitle = {Computer Security Foundations Symposium (CSF), 2011 IEEE 24th},
	year = {2011},
	month = {2011/06/27/29},
	pages = {114 - 128},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {This paper explores the idea of knowledge-based security policies, which are used to decide whether to answer queries over secret data based on an estimation of the querier{\textquoteright}s (possibly increased) knowledge given the results. Limiting knowledge is the goal of existing information release policies that employ mechanisms such as noising, anonymization, and redaction. Knowledge-based policies are more general: they increase flexibility by not fixing the means to restrict information flow. We enforce a knowledge-based policy by explicitly tracking a model of a querier{\textquoteright}s belief about secret data, represented as a probability distribution, and denying any query that could increase knowledge above a given threshold. We implement query analysis and belief tracking via abstract interpretation using a novel probabilistic polyhedral domain, whose design permits trading off precision with performance while ensuring estimates of a querier{\textquoteright}s knowledge are sound. Experiments with our implementation show that several useful queries can be handled efficiently, and performance scales far better than would more standard implementations of probabilistic computation based on sampling.},
	keywords = {abstract interpretation, belief networks, belief tracking, Data models, dynamic enforcement, Facebook, information flow, knowledge based systems, knowledge-based security, knowledge-based security policy, privacy, probabilistic computation, probabilistic logic, probabilistic polyhedral domain, probabilistic polyhedron, probability, query analysis, Security, security of data, semantics, Waste materials},
	isbn = {978-1-61284-644-6},
	doi = {10.1109/CSF.2011.15},
	author = {Mardziel,P. and Magill,S. and Hicks, Michael W. and Srivatsa,M.}
}
@conference {12436,
	title = {Sparse dictionary-based representation and recognition of action attributes},
	booktitle = {2011 IEEE International Conference on Computer Vision (ICCV)},
	year = {2011},
	month = {2011/11/06/13},
	pages = {707 - 714},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {We present an approach for dictionary learning of action attributes via information maximization. We unify the class distribution and appearance information into an objective function for learning a sparse dictionary of action attributes. The objective function maximizes the mutual information between what has been learned and what remains to be learned in terms of appearance information and class distribution for each dictionary item. We propose a Gaussian Process (GP) model for sparse representation to optimize the dictionary objective function. The sparse coding property allows a kernel with a compact support in GP to realize a very efficient dictionary learning process. Hence we can describe an action video by a set of compact and discriminative action attributes. More importantly, we can recognize modeled action categories in a sparse feature space, which can be generalized to unseen and unmodeled action categories. Experimental results demonstrate the effectiveness of our approach in action recognition applications.},
	keywords = {action attributes, appearance information, class distribution, Dictionaries, dictionary learning process, Encoding, Entropy, Gaussian process model, Gaussian processes, Histograms, HUMANS, Image coding, image representation, information maximization, learning (artificial intelligence), modeled action categories, Mutual information, Object recognition, probabilistic logic, sparse coding property, sparse dictionary-based recognition, sparse dictionary-based representation, sparse feature space, unmodeled action categories},
	isbn = {978-1-4577-1101-5},
	doi = {10.1109/ICCV.2011.6126307},
	author = {Qiang Qiu and Zhuolin Jiang and Chellapa, Rama}
}
@conference {17633,
	title = {New Constructive Aspects of the Lovasz Local Lemma},
	booktitle = {2010 51st Annual IEEE Symposium on Foundations of Computer Science (FOCS)},
	year = {2010},
	month = {2010/10/23/26},
	pages = {397 - 406},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {The Lov{\textquoteright}{a}sz Local Lemma (LLL) is a powerful tool that gives sufficient conditions for avoiding all of a given set of {\textquoteleft}{\textquoteleft}bad{\textquoteright}{\textquoteright} events, with positive probability. A series of results have provided algorithms to efficiently construct structures whose existence is non-constructively guaranteed by the LLL, culminating in the recent breakthrough of Moser \& Tardos. We show that the output distribution of the Moser-Tardos algorithm well-approximates the emph{conditional LLL-distribution} {\textendash} the distribution obtained by conditioning on all bad events being avoided. We show how a known bound on the probabilities of events in this distribution can be used for further probabilistic analysis and give new constructive and non-constructive results. We also show that when an LLL application provides a small amount of slack, the number of resamplings of the Moser-Tardos algorithm is nearly linear in the number of underlying independent variables (not events!), and can thus be used to give efficient constructions in cases where the underlying proof applies the LLL to super-polynomially many events. Even in cases where finding a bad event that holds is computationally hard, we show that applying the algorithm to avoid a polynomial-sized {\textquoteleft}{\textquoteleft}core{\textquoteright}{\textquoteright} subset of bad events leads to a desired outcome with high probability. We demonstrate this idea on several applications. We give the first constant-factor approximation algorithm for the Santa Claus problem by making an LLL-based proof of Feige constructive. We provide Monte Carlo algorithms for acyclic edge coloring, non-repetitive graph colorings, and Ramsey-type graphs. In all these applications the algorithm falls directly out of the non-constructive LLL-based proof. Our algorithms are very simple, often provide better bounds than previous algorithms, and are in several cases the first efficient algorithms known. As a second type of application we consider settings beyond the critical dependency threshold of the LLL: - - avoiding all bad events is impossible in these cases. As the first (even non-constructive) result of this kind, we show that by sampling from the LLL-distribution of a selected smaller core, we can avoid a fraction of bad events that is higher than the expectation. MAX $k$-SAT is an example of this.},
	keywords = {acyclic edge coloring, Algorithm design and analysis, Approximation algorithms, Approximation methods, computational complexity, Computer science, constant factor approximation algorithm, graph colouring, Linearity, Lovasz Local Lemma, MAX k-SAT, Monte Carlo Algorithm, Monte Carlo methods, Moser-Tardos algorithm, nonrepetitive graph coloring, output distribution, polynomial sized core subset, Polynomials, Probabilistc Method, probabilistic analysis, probabilistic logic, probability, Ramsey type graph, Sampling methods, Santa Claus Problem},
	isbn = {978-1-4244-8525-3},
	doi = {10.1109/FOCS.2010.45},
	author = {Haeupler,B. and Saha,B. and Srinivasan, Aravind}
}
@article {15970,
	title = {Uniform accountability for multiple modes of reasoning},
	journal = {International Journal of Approximate Reasoning},
	volume = {2},
	year = {1988},
	month = {1988/07//},
	pages = {233 - 246},
	abstract = {This article discusses various issues surrounding the general debate on knowledge representation methods and argues in favor of the idea that while many methods are necessary, there also must be a kind of unified nature to the enterprise if it is to serve the needs of intelligence. Specific points include the misleading distinction between probabilistic and logical reasoning regarding the notion of truth and also some matters of nonmonotonicity. An effort is made to sample the broad range of approaches in the literature, with an eye toward such a unification.},
	keywords = {Artificial intelligence, commonsense reasoning, probabilistic logic, probability theory, uncertainty representation},
	isbn = {0888-613X},
	doi = {16/0888-613X(88)90119-3},
	url = {http://www.sciencedirect.com/science/article/pii/0888613X88901193},
	author = {Kanal,Laveen and Perlis, Don}
}
