In this paper, we present CrowdSense, an algorithm for estimating the crowd’s majority opinion by querying only a subset of it. CrowdSense works in an online fashion where examples come one at a time and it dynamically samples subsets of labelers based on an exploration/exploitation criterion. The algorithm produces a weighted combination of a subset of the labelers’ votes that approximates the crowd’s opinion. We also present two probabilistic variants of CrowdSense that are based on different assumptions on the joint probability distribution between the labelers’ votes and the majority vote. Our experiments demonstrate that we can reliably approximate the entire crowd’s vote by collecting opinions from a representative subset of the crowd.

We study the mixtures of factorizing probability distributions represented as visible marginal distributions in stochastic layered networks. We take the perspective of kernel transitions of distributions, which gives a unified picture of distributed representations arising from Deep Belief Networks (DBN) and other networks without lateral connections. We describe combinatorial and geometric properties of the set of kernels and products of kernels realizable by DBNs as the network parameters vary. We describe explicit classes of probability distributions, including exponential families, that can be learned by DBNs. We use these submodels to bound the maximal and the expected Kullback-Leibler approximation errors of DBNs from above depending on the number of hidden layers and units that they contain.

Network analysis is a growing field across many domains, including computer vision, social media marketing, transportation networks, and intelligence analysis. The growing use of digital communication devices and platforms, as well as persistent surveillance sensors, has resulted in explosion of the quantity of data and stretched the abilities of current technologies to process this data and draw meaningful conclusions. Current tools either require significant levels of manual intervention (e.g., to prepare the data, to define patterns, or to draw conclusions from data) or are unable to generalize to new data sources and analysis needs. In this paper, we present automated solutions to two major problems in network analysis: (a) finding patterns in the network data that contains high levels of noise and irrelevant information; and (b) learning repetitive patterns and dependencies between entities and attributes. Our modeling framework represents network data using multi-attributed graphs that can encode various discrete and continuous features and relationships between network entities. The pattern search and learning model is based on probabilistic multi-attributed graph matching, and implemented using distributed message passing algorithms. Our algorithms achieved high accuracy rates in learning and finding patterns in the data, are flexible to new domains and data types, and scale to large datasets using the Map-Reduce framework.

In this paper, we focus on computing the prices of secu- rities represented by logical formulas in combinatorial prediction markets when the price function is represented by a Bayesian network. This problem turns out to be a natural extension of the weighted model counting (WMC) problem (Sang, Bearne, and Kautz 2005), which we call generalized weighted model counting (GWMC) problem. In GWMC, we are given a logical formula F and a polynomial-time computable weight function. We are asked to compute the total weight of the valuations that satisfy F. Based on importance sampling, we propose a Monte-Carlo meta-algorithm that has a good theoretical guarantee for formulas in disjunctive normal form (DNF). The meta-algorithm queries an oracle algorithm that computes marginal probabilities in Bayesian networks, and has the following theoretical guarantee. When the weight function can be approximately represented by a Bayesian network for which the oracle algorithm runs in polynomial time, our meta-algorithm becomes a fully polynomial-time randomized approximation scheme (FPRAS).

We consider the problem of evaluating the performance of human contributors for tasks involving answering a series of questions, each of which has a single correct answer. The answers may not be known a priori. We assert that the measure of a contributor’s judgments is the amount by which having these judgments decreases the entropy of our discovering the answer. This quantity is the pointwise mutual information between the judgments and the answer. The expected value of this metric is the mutual information between the contributor and the answer prior, which can be computed using only the prior and the conditional probabil- ities of the contributor’s judgments given a correct answer, without knowing the answers themselves. We also propose using multivariable information measures, such as conditional mutual information, to measure the inter- actions between contributors’ judgments. These metrics have a variety of applications. They can be used as a basis for contributor performance evaluation and incentives. They can be used to measure the efficiency of the judgment collection process. If the collection process allows assignment of contributors to questions, they can also be used to optimize this scheduling.

We propose to improve the accuracy of prediction market forecasts by using Bayesian networks to constrain probabilities among related questions. Prediction markets are already known to increase forecast accuracy compared to single best estimates. Our own flat prediction market substantially beat a baseline linear opinion pool during the first year. One way to improve performance is by expressing relationships among the questions. Elsewhere we describe work on combinatorial markets. Here we show how to use Bayesian networks within a flat market. The general approach is to decompose a target question (hypothesis) into a set of related variables (causal factors and evidence), when the relationship among the variables is known with some confidence. Then the marginal probabilities for the variables in the Bayes net are updated using the market estimates, with the Bayes net enforcing coherence. This paper describes the overall concept, shows the results for a particular model of the potential Greek exit from the European Union, and describes the team’s future research plan.

We present Global-Local POS tagging, a framework to train generative stochastic Part-of-Speech models on large corpora. Global Taggers offer several advantages over their counter parts trained on small, curated corpus, including the ability to automatically extend and update their models to new text. Global Taggers also avoid a fundamental limitation of current models, whose performance heavily relies on curated text with manually assigned labels. We illustrate our approach by training several Global Taggers, implemented with generative stochastic models, on two large corpora using high performance computing architecture. We further demonstrate that global taggers can be improved by incorporating models trained on curated text, called Local Taggers, for better tagging performance derived from specific topics.

Scarcity of training data for word sense disambiguation argues for the use of knowledge-based disambiguation methods, which rely on information available in terminological resources. Unfortunately, these resources are not generally optimized to perform word sense disambiguation. On the other hand, there are many examples of ambiguous biomedical words with context in MEDLINE. However, these examples of ambiguity are not labeled with their proper sense. We propose the integration of the UMLS and MEDLINE to create concept profiles which are used to perform knowledge-based word sense disambiguation. Our results show an accuracy of 0.8770 on a biomedical word sense disambiguation data set; this represents a statistically significant improvement over other knowledge-based methods based on the UMLS on this data set.

In this paper we discuss the gap that exists between the caregivers of older adults attempting to age-in-place and sophisticated ”smart-home” systems that can sense the environment and provide assistance when needed. We argue that smart-home systems need to be customizable by end-users, and we present a general-purpose model for cognitive assistive technology that can be adapted to suit many different tasks, users and environments. Al- though we can provide mechanisms for engineers and designers to build and adapt smart-home systems based on this general-purpose model, these mechanisms are not easily understood by or sufficiently user-friendly for actual end users such as older adults and their care- givers. Our goal is therefore to study how to bridge the gap between the end-users and this technology. In this paper, we discuss our work on this problem from both sides: developing technology that is customizable and general-purpose, and studying user’s abilities and needs when it comes to building smart-home systems to help with activities of daily living. We show how a large gap still exists, and propose ideas for how to bridge the gap.

Gaussian process (GP) predictors are an important component of many Bayesian approaches to machine learning. However, even a straightforward implementation of Gaussian process regression (GPR) requires O(n^2) space and O(n^3) time for a dataset of n examples. Several approximation methods have been proposed, but there is a lack of understanding of the relative merits of the different approximations, and in what situations they are most useful. We recommend assessing the quality of the predictions obtained as a function of the compute time taken, and comparing to standard baselines (e.g., Subset of Data and FITC). We empirically investigate four different approximation algorithms on four different prediction problems, and make our code available to encourage future comparisons.