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Learned-Miller, E., B. M. Marlin, and A. Kae, "The Shape-Time Random Field for Semantic Video Labeling", 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 272-279, 2014. Abstractstrf_cvpr2014.pdf

We propose a novel discriminative model for semantic labeling in videos by incorporating a prior to model both the shape and temporal dependencies of an object in video. A typical approach for this task is the conditional random field (CRF), which can model local interactions among adjacent regions in a video frame. Recent work [16, 14] has shown how to incorporate a shape prior into a CRF for improving labeling performance, but it may be difficult to model temporal dependencies present in video by using this prior. The conditional restricted Boltzmann machine (CRBM) can model both shape and temporal dependencies, and has been used to learn walking styles from motion- capture data. In this work, we incorporate a CRBM prior into a CRF framework and present a new state-of-the-art model for the task of semantic labeling in videos. In particular, we explore the task of labeling parts of complex face scenes from videos in the YouTube Faces Database (YFDB). Our combined model outperforms competitive baselines both qualitatively and quantitatively.

Sheldon, D., T. Sun, A. Kumar, and T. G. Dietterich, "Approximate Inference in Collective Graphical Models", In Proceedings of the 30th International Conference on Machine Learning (ICML), 2013., 2013. icml2013-cgm-map.pdf
Natarajan, A., A. Parate, E. Gaiser, G. Angarita, R. Malison, B. Marlin, and D. Ganesan, "Detecting Cocaine Use with Wearable Electrocardiogram Sensors", 2013 ACM international joint conference on Pervasive and ubiquitous computing, Zurich, Switzerland, pp. 123-132, 2013. ubicomp_cocaine_paper.pdf
Marlin, B. M., R. J. Adams, R. Sadasivam, and T. K. Houston, "Towards Collaborative Filtering Recommender Systems for Tailored Health Communications", AMIA 2013 Annual Symposium, Washington D.C., pp. 1600-1607, 2013. cf_thc_paper.pdf
Sheldon, D., and T. G. Dietterich, "Collective Graphical Models", Advances in Neural Information Processing Systems (NIPS 2011), 2012. Abstract

There are many settings in which we wish to fit a model of the behavior of individuals but where our data consist only of aggregate information (counts or low-dimensional contingency tables). This paper introduces Collective Graphical Models–-a framework for modeling and probabilistic inference that operates directly on the sufficient statistics of the individual model. We derive a highly-efficient Gibbs sampling algorithm for sampling from the posterior distribution of the sufficient statistics conditioned on noisy aggregate observations, prove its correctness, and demonstrate its effectiveness experimentally.

Hochachka, W. M., D. Fink, R. A. Hutchinson, D. Sheldon, W. - K. Wong, and S. Kelling, Data Intensive Science Applied to Broad-Scale Citizen Science, , 2012. Abstract

Identifying ecological patterns across broad spatial and temporal extents requires novel approaches and methods for acquiring, integrating and modeling massive quantities of diverse data. For example, a growing number of research projects engage continent-wide networks of volunteers (‘citizen-scientists’) to collect species occurrence data. Although these data are information rich, they present numerous challenges in project design, implementation and analysis, which include: developing data collection tools that maximize data quantity while maintaining high standards of data quality, and applying new analytical and visualization techniques that can accurately reveal patterns in these data. Here, we describe how advances in data-intensive science provide accurate estimates in species distributions at continental scales by identifying complex environmental associations.

Xue, S., A. Fern, and D. Sheldon, "Scheduling Conservation Designs via Network Cascade Optimization", Twenty-Sixth AAAI Conference on Artificial Intelligence, 2012. Abstract

We introduce the problem of scheduling land purchases to conserve an endangered species in a way that achieves maxi- mum population spread but delays purchases as long as possible, so that conservation planners retain maximum flexibility and use available budgets in the most efficient way. We develop the problem formally as a stochastic optimization problem over a network cascade model describing the population spread, and present a solution approach that reduces the stochastic problem to a novel variant of a Steiner tree problem. We give a primal-dual algorithm for the problem that computes both a feasible solution and a bound on the quality of an optimal solution. Our experiments, using actual conservation data and a standard diffusion model, show that the approach produces near optimal results and is much more scalable than more generic off-the-shelf optimizers.

Krafft, P., J. Moore, H. Wallach, and B. Desmarais, "Topic-Partitioned Multinetwork Embeddings", Advances in Neural Information Processing Systems Twenty-Five, Lake Tahoe, NV, 2012. Abstract

We introduce a joint model of network content and context designed for exploratory analysis of email networks via visualization of topic-specific communication patterns. Our model is an admixture model for text and network attributes that uses multinomial distributions over words as admixture components for explaining email text and latent Euclidean positions of actors as admixture components for explaining email recipients. This model allows us to infer topics of communication, a partition of the overall network into topic-specific subnetworks, and two-dimensional visualizations of those subnetworks. We validate the appropriateness of our model by achieving state-of-the-art performance on a prediction task and semantic coherence comparable to that of latent Dirichlet allocation. We demonstrate the capability of our model for descriptive, explanatory, and exploratory analysis by investigating the inferred topic-specific communication patterns of a new email data set, the New Hanover County email corpus.

Marlin, B. M., D. C. Kale, R. G. Khemani, and R. C. Wetzel, "Unsupervised pattern discovery in electronic health care data using probabilistic clustering models", Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium, New York, NY, USA, ACM, pp. 389–398, 2012. Abstract2012-ihi-marlin.pdf

Bedside clinicians routinely identify temporal patterns in physiologic data in the process of choosing and administering treatments intended to alter the course of critical illness for individual patients. Our primary interest is the study of unsupervised learning techniques for automatically uncovering such patterns from the physiologic time series data contained in electronic health care records. This data is sparse, high-dimensional and often both uncertain and incomplete. In this paper, we develop and study a probabilistic clustering model designed to mitigate the effects of temporal sparsity inherent in electronic health care records data. We evaluate the model qualitatively by visualizing the learned cluster parameters and quantitatively in terms of its ability to predict mortality outcomes associated with patient episodes. Our results indicate that the model can discover distinct, recognizable physiologic patterns with prognostic significance.