Information and Decision Theoretic Approaches to Problems in Active Diagnosis.

In applications such as active learning or disease/fault diagnosis, one often encounters the problem of identifying an unknown object while minimizing the number of ``yes" or ``no" questions (queries) posed about that object. This problem has been commonly referred to as object/entity identification or active diagnosis in the literature. In this thesis, we consider several extensions of this fundamental problem that are motivated by practical considerations in real-world, time-critical identification tasks such as emergency response. First, we consider the problem where the objects are partitioned into groups, and the goal is to identify only the group to which the object belongs. We then consider the case where the cost of identifying an object grows exponentially in the number of queries. To address these problems we show that a standard algorithm for object identification, known as the splitting algorithm or generalized binary search (GBS), may be viewed as a generalization of Shannon-Fano coding. We then extend this result to the group-based and the exponential cost settings, leading to new, improved algorithms. We then study the problem of active diagnosis under persistent query noise. Previous work in this area either assumed that the noise is independent or that the underlying query noise distribution is completely known. We make no such assumptions, and introduce an algorithm that returns a ranked list of objects, such that the expected rank of the true object is optimized. Finally, we study the problem of active diagnosis where multiple objects are present, such as in disease/fault diagnosis. Current algorithms in this area have an exponential time complexity making them slow and intractable. We address this issue by proposing an extension of our rank-based approach to the multiple object scenario, where we optimize the area under the ROC curve of the rank-based output. The AUC criterion allows us to make a simplifying assumption that significantly reduces the complexity of active diagnosis (from exponential to near quadratic), with little or no compromise on the performance quality. Further, we demonstrate the performance of the proposed algorithms through extensive experiments on both synthetic and real world datasets.Ph.D.Electrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/91606/1/gowtham_1.pd

Query Learning with Exponential Query Costs

In query learning, the goal is to identify an unknown object while minimizing the number of "yes" or "no" questions (queries) posed about that object. A well-studied algorithm for query learning is known as generalized binary search (GBS). We show that GBS is a greedy algorithm to optimize the expected number of queries needed to identify the unknown object. We also generalize GBS in two ways. First, we consider the case where the cost of querying grows exponentially in the number of queries and the goal is to minimize the expected exponential cost. Then, we consider the case where the objects are partitioned into groups, and the objective is to identify only the group to which the object belongs. We derive algorithms to address these issues in a common, information-theoretic framework. In particular, we present an exact formula for the objective function in each case involving Shannon or Renyi entropy, and develop a greedy algorithm for minimizing it. Our algorithms are demonstrated on two applications of query learning, active learning and emergency response.Comment: 15 page

Discovering hidden relationships between renal diseases and regulated genes through 3D network visualizations

Abstract Background In a recent study, two-dimensional (2D) network layouts were used to visualize and quantitatively analyze the relationship between chronic renal diseases and regulated genes. The results revealed complex relationships between disease type, gene specificity, and gene regulation type, which led to important insights about the underlying biological pathways. Here we describe an attempt to extend our understanding of these complex relationships by reanalyzing the data using three-dimensional (3D) network layouts, displayed through 2D and 3D viewing methods. Findings The 3D network layout (displayed through the 3D viewing method) revealed that genes implicated in many diseases (non-specific genes) tended to be predominantly down-regulated, whereas genes regulated in a few diseases (disease-specific genes) tended to be up-regulated. This new global relationship was quantitatively validated through comparison to 1000 random permutations of networks of the same size and distribution. Our new finding appeared to be the result of using specific features of the 3D viewing method to analyze the 3D renal network. Conclusions The global relationship between gene regulation and gene specificity is the first clue from human studies that there exist common mechanisms across several renal diseases, which suggest hypotheses for the underlying mechanisms. Furthermore, the study suggests hypotheses for why the 3D visualization helped to make salient a new regularity that was difficult to detect in 2D. Future research that tests these hypotheses should enable a more systematic understanding of when and how to use 3D network visualizations to reveal complex regularities in biological networks.http://deepblue.lib.umich.edu/bitstream/2027.42/112972/1/13104_2010_Article_700.pd

Securing private data sharing in multi-party analytics

A general class of problems arises when datasets containing private information belong to multiple parties or owners and they collectively want to perform analytic studies on the entire set while respecting the privacy and security concerns of each individual party. We describe a solution to this problem in the form of a secure procedure for data mapping and/or linkage, which allows to identify the correspondence between entities in a distributed dataset. In contrast to existing methods this solution does not require either a trusted or semi-trusted third party, while being simple, efficient and scalable for both large datasets and number of parties