@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}
}
@conference {13393,
	title = {Online Filtering, Smoothing and Probabilistic Modeling of Streaming data},
	booktitle = {IEEE 24th International Conference on Data Engineering, 2008. ICDE 2008},
	year = {2008},
	month = {2008/04/07/12},
	pages = {1160 - 1169},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {In this paper, we address the problem of extending a relational database system to facilitate efficient real-time application of dynamic probabilistic models to streaming data. We use the recently proposed abstraction of model-based views for this purpose, by allowing users to declaratively specify the model to be applied, and by presenting the output of the models to the user as a probabilistic database view. We support declarative querying over such views using an extended version of SQL that allows for querying probabilistic data. Underneath we use particle filters, a class of sequential Monte Carlo algorithms, to represent the present and historical states of the model as sets of weighted samples (particles) that are kept up-to-date as new data arrives. We develop novel techniques to convert the queries on the model-based view directly into queries over particle tables, enabling highly efficient query processing. Finally, we present experimental evaluation of our prototype implementation over several synthetic and real datasets, that demonstrates the feasibility of online modeling of streaming data using our system and establishes the advantages of tight integration between dynamic probabilistic models and databases.},
	keywords = {Data analysis, data streaming, declarative query, dynamic probabilistic model, Filtering, Global Positioning System, hidden Markov models, Monitoring, Monte Carlo methods, Noise generators, Noise measurement, online filtering, particle filter, particle filtering (numerical methods), probabilistic database view, probability, Real time systems, real-time application, relational database system, Relational databases, sequential Monte Carlo algorithm, Smoothing methods, SQL},
	isbn = {978-1-4244-1836-7},
	doi = {10.1109/ICDE.2008.4497525},
	author = {Kanagal,B. and Deshpande, Amol}
}
@article {19662,
	title = {MCMC Data Association and Sparse Factorization Updating for Real Time Multitarget Tracking with Merged and Multiple Measurements},
	journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
	volume = {28},
	year = {2006},
	month = {2006/12//},
	pages = {1960 - 1972},
	abstract = {In several multitarget tracking applications, a target may return more than one measurement per target and interacting targets may return multiple merged measurements between targets. Existing algorithms for tracking and data association, initially applied to radar tracking, do not adequately address these types of measurements. Here, we introduce a probabilistic model for interacting targets that addresses both types of measurements simultaneously. We provide an algorithm for approximate inference in this model using a Markov chain Monte Carlo (MCMC)-based auxiliary variable particle filter. We Rao-Blackwellize the Markov chain to eliminate sampling over the continuous state space of the targets. A major contribution of this work is the use of sparse least squares updating and downdating techniques, which significantly reduce the computational cost per iteration of the Markov chain. Also, when combined with a simple heuristic, they enable the algorithm to correctly focus computation on interacting targets. We include experimental results on a challenging simulation sequence. We test the accuracy of the algorithm using two sensor modalities, video, and laser range data. We also show the algorithm exhibits real time performance on a conventional PC},
	keywords = {algorithms, approximate inference, Artificial intelligence, auxiliary variable particle filter, Computational efficiency, continuous state space, downdating, Image Enhancement, Image Interpretation, Computer-Assisted, Inference algorithms, Information Storage and Retrieval, laser range scanner, laser range scanner., Least squares approximation, least squares approximations, Least squares methods, linear least squares, Markov chain Monte Carlo, Markov processes, MCMC data association, merged measurements, Monte Carlo methods, Movement, multiple merged measurements, multitarget tracking, particle filter, particle filtering (numerical methods), Particle filters, Pattern Recognition, Automated, probabilistic model, QR factorization, Radar tracking, Rao-Blackwellized, real time multitarget tracking, Reproducibility of results, Sampling methods, Sensitivity and Specificity, sensor fusion, sparse factorization updating, sparse least squares, State-space methods, Subtraction Technique, target tracking, updating},
	isbn = {0162-8828},
	author = {Zia Khan and Balch, T. and Dellaert, F.}
}
@article {19663,
	title = {MCMC-based particle filtering for tracking a variable number of interacting targets},
	journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
	volume = {27},
	year = {2005},
	month = {2005/11//},
	pages = {1805 - 1819},
	abstract = {We describe a particle filter that effectively deals with interacting targets, targets that are influenced by the proximity and/or behavior of other targets. The particle filter includes a Markov random field (MRF) motion prior that helps maintain the identity of targets throughout an interaction, significantly reducing tracker failures. We show that this MRF prior can be easily implemented by including an additional interaction factor in the importance weights of the particle filter. However, the computational requirements of the resulting multitarget filter render it unusable for large numbers of targets. Consequently, we replace the traditional importance sampling step in the particle filter with a novel Markov chain Monte Carlo (MCMC) sampling step to obtain a more efficient MCMC-based multitarget filter. We also show how to extend this MCMC-based filter to address a variable number of interacting targets. Finally, we present both qualitative and quantitative experimental results, demonstrating that the resulting particle filters deal efficiently and effectively with complicated target interactions.},
	keywords = {algorithms, Animals, Artificial intelligence, Computer simulation, Computer vision, Filtering, filtering theory, HUMANS, Image Enhancement, Image Interpretation, Computer-Assisted, Index Terms- Particle filters, Information Storage and Retrieval, Insects, interacting targets, Markov chain Monte Carlo sampling step, Markov chain Monte Carlo., Markov chains, Markov processes, Markov random field motion, Markov random fields, Models, Biological, Models, Statistical, Monte Carlo Method, Monte Carlo methods, MOTION, Movement, multitarget filter, multitarget tracking, particle filtering, Particle filters, Particle tracking, Pattern Recognition, Automated, Sampling methods, Subtraction Technique, target tracking, Video Recording},
	isbn = {0162-8828},
	author = {Zia Khan and Balch, T. and Dellaert, F.}
}
@conference {19665,
	title = {Multitarget tracking with split and merged measurements},
	booktitle = {IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005. CVPR 2005},
	volume = {1},
	year = {2005},
	month = {2005/06//},
	pages = {605 - 610 vol. 1},
	abstract = {In many multitarget tracking applications in computer vision, a detection algorithm provides locations of potential targets. Subsequently, the measurements are associated with previously estimated target trajectories in a data association step. The output of the detector is often imperfect and the detection data may include multiple, split measurements from a single target or a single merged measurement from several targets. To address this problem, we introduce a multiple hypothesis tracker for interacting targets that generate split and merged measurements. The tracker is based on an efficient Markov chain Monte Carlo (MCMC) based auxiliary variable particle filter. The particle filter is Rao-Blackwellized such that the continuous target state parameters are estimated analytically, and an MCMC sampler generates samples from the large discrete space of data associations. In addition, we include experimental results in a scenario where we track several interacting targets that generate these split and merged measurements.},
	keywords = {Application software, Computer vision, Detection algorithms, Detectors, filtering theory, Markov chain Monte Carlo based auxiliary variable particle filter, Markov processes, merged measurements, Monte Carlo methods, multiple hypothesis tracker, multitarget tracking, parameter estimation, Particle filters, Particle tracking, Rao-Blackwellized filter, split measurements, target tracking, Trajectory},
	author = {Zia Khan and Balch, T. and Dellaert, F.}
}
@conference {17314,
	title = {Ordered treemap layouts},
	booktitle = {IEEE Symposium on Information Visualization, 2001. INFOVIS 2001},
	year = {2001},
	month = {2001///},
	pages = {73 - 78},
	publisher = {IEEE},
	organization = {IEEE},
	keywords = {Clustering algorithms, Computer science, Data visualization, Displays, Electronic switching systems, Filling, Monte Carlo methods, Read only memory, Testing},
	isbn = {0-7695-7342-5},
	doi = {10.1109/INFVIS.2001.963283},
	author = {Shneiderman, Ben and Wattenberg,M.}
}
