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183
Bayesian Optimization Algorithm: From Single Level to Hierarchy
, 2002
"... There are four primary goals of this dissertation. First, design a competent optimization algorithm capable of learning and exploiting appropriate problem decomposition by sampling and evaluating candidate solutions. Second, extend the proposed algorithm to enable the use of hierarchical decompositi ..."
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Cited by 90 (18 self)
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There are four primary goals of this dissertation. First, design a competent optimization algorithm capable of learning and exploiting appropriate problem decomposition by sampling and evaluating candidate solutions. Second, extend the proposed algorithm to enable the use of hierarchical decomposition as opposed to decomposition on only a single level. Third, design a class of difficult hierarchical problems that can be used to test the algorithms that attempt to exploit hierarchical decomposition. Fourth, test the developed algorithms on the designed class of problems and several realworld applications. The dissertation proposes the Bayesian optimization algorithm (BOA), which uses Bayesian networks to model the promising solutions found so far and sample new candidate solutions. BOA is theoretically and empirically shown to be capable of both learning a proper decomposition of the problem and exploiting the learned decomposition to ensure robust and scalable search for the optimum across a wide range of problems. The dissertation then identifies important features that must be incorporated into the basic BOA to solve problems that are not decomposable on a single level, but that can still be solved by decomposition over multiple levels of difficulty. Hierarchical
Coordinate: Probabilistic Forecasting of Presence and Availability
 Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI
, 2002
"... We present methods employed in COORDINATE, a prototype service that supports collaboration and communication by learning predictive models that provide forecasts of users ’ presence and availability. We describe how data is collected about user activity and proximity from multiple devices, in additi ..."
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Cited by 80 (21 self)
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We present methods employed in COORDINATE, a prototype service that supports collaboration and communication by learning predictive models that provide forecasts of users ’ presence and availability. We describe how data is collected about user activity and proximity from multiple devices, in addition to analysis of the content of users ’ calendars, the time of day, and day of week. We review applications of presence forecasting embedded in the PRIORITIES application and then present details of the COORDINATE service that was informed by the earlier efforts. 1
Rich Probabilistic Models for Gene Expression
, 2001
"... Clustering is commonly used for analyzing gene expression data. Despite their successes, clustering methods suffer from a number of limitations. First, these methods reveal similarities that exist over all of the measurements, while obscuring relationships that exist over only a subset of the data. ..."
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Cited by 77 (8 self)
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Clustering is commonly used for analyzing gene expression data. Despite their successes, clustering methods suffer from a number of limitations. First, these methods reveal similarities that exist over all of the measurements, while obscuring relationships that exist over only a subset of the data. Second, clustering methods cannot readily incorporate additional types of information, such as clinical data or known attributes of genes. To circumvent these shortcomings, we propose the use of a single coherent probabilistic model, that encompasses much of the rich structure in the genomic expression data, while incorporating additional information such as experiment type, putative binding sites, or functional information. We show how this model can be learned from the data, allowing us to discover patterns in the data and dependencies between the gene expression patterns and additional attributes. The learned model reveals contextspecific relationships, that exist only over a subset of the experiments in the dataset. We demonstrate the power of our approach on synthetic data and on two realworld gene expression data sets for yeast. For example, we demonstrate a novel functionality that falls naturally out of our framework: predicting the “cluster” of the array resulting from a gene mutation based only on the gene’s expression pattern in the context of other mutations.
A Bayesian Approach to Tackling Hard Computational Problems
 IN UAI
, 2001
"... We describe research and results centering on the construction and use of Bayesian models that can predict the run time of problem solvers. Our efforts are motivated by observations of high variance in the time required to solve instances for several challenging problems. The methods ..."
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Cited by 66 (9 self)
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We describe research and results centering on the construction and use of Bayesian models that can predict the run time of problem solvers. Our efforts are motivated by observations of high variance in the time required to solve instances for several challenging problems. The methods
Busybody: creating and fielding personalized models of the cost of interruption
 In Proc of CSCW 2004, ACM Press
, 2004
"... Interest has been growing in opportunities to build and deploy statistical models that can infer a computer user’s current interruptability from computer activity and relevant contextual information. We describe a system that intermittently asks users to assess their perceived interruptability durin ..."
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Cited by 64 (9 self)
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Interest has been growing in opportunities to build and deploy statistical models that can infer a computer user’s current interruptability from computer activity and relevant contextual information. We describe a system that intermittently asks users to assess their perceived interruptability during a training phase and that builds decisiontheoretic models with the ability to predict the cost of interrupting the user. The models are used at runtime to compute the expected cost of interruptions, providing a mediator for incoming notifications, based on a consideration of a user’s current and recent history of computer activity, meeting status, location, time of day, and whether a conversation is detected.
Exact Bayesian structure discovery in Bayesian networks
 J. of Machine Learning Research
, 2004
"... We consider a Bayesian method for learning the Bayesian network structure from complete data. Recently, Koivisto and Sood (2004) presented an algorithm that for any single edge computes its marginal posterior probability in O(n2 n) time, where n is the number of attributes; the number of parents per ..."
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Cited by 61 (8 self)
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We consider a Bayesian method for learning the Bayesian network structure from complete data. Recently, Koivisto and Sood (2004) presented an algorithm that for any single edge computes its marginal posterior probability in O(n2 n) time, where n is the number of attributes; the number of parents per attribute is bounded by a constant. In this paper we show that the posterior probabilities for all the n(n−1) potential edges can be computed in O(n2 n) total time. This result is achieved by a forward–backward technique and fast Möbius transform algorithms, which are of independent interest. The resulting speedup by a factor of about n 2 allows us to experimentally study the statistical power of learning moderatesize networks. We report results from a simulation study that covers data sets with 20 to 10,000 records over 5 to 25 discrete attributes. 1
From Promoter Sequence to Expression: A Probabilistic Framework
, 2002
"... We present a probabilistic framework that models the process by which transcriptional binding explains the mRNA expression of different genes. Our joint probabilistic model unifies the two key components of this process: the prediction of gene regulation events from sequence motifs in the gene' ..."
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Cited by 60 (6 self)
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We present a probabilistic framework that models the process by which transcriptional binding explains the mRNA expression of different genes. Our joint probabilistic model unifies the two key components of this process: the prediction of gene regulation events from sequence motifs in the gene's promoter region, and the prediction of mRNA expression from combinations of gene regulation events in different settings. Our approach has several advantages. By learning promoter sequence motifs that are directly predictive of expression data, it can improve the identification of binding site patterns. It is also able to identify combinatorial regulation via interactions of different transcription factors. Finally, the general framework allows us to integrate additional data sources, including data from the recent binding localization assays. We demonstrate our approach on the cell cycle data of Spellman et al., combined with the binding localization information of Simon et al. We show that the learned model predicts expression from sequence, and that it identifies coherent coregulated groups with significant transcription factor motifs. It also provides valuable biological insight into the domain via these coregulated "modules" and the combinatorial regulation effects that govern their behavior.
Dynamic Restart Policies
, 2002
"... We describe theoretical results and empirical study of contextsensitive restart policies for randomized search procedures. ..."
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Cited by 60 (5 self)
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We describe theoretical results and empirical study of contextsensitive restart policies for randomized search procedures.
Stratified exponential families: Graphical models and model selection
 ANNALS OF STATISTICS
, 2001
"... ..."
ContextSpecific Bayesian Clustering for Gene Expression Data
, 2002
"... The recent growth in genomic data and measurements of genomewide expression patterns allows us to apply computational tools to examine gene regulation by transcription factors. ..."
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Cited by 56 (5 self)
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The recent growth in genomic data and measurements of genomewide expression patterns allows us to apply computational tools to examine gene regulation by transcription factors.