Sparse signal recovery and acquisition with graphical models by Cevher, Volkan, Indyk, Piotr, Carin, Lawrence, Baraniuk, Richard G.

“…A great deal of theoretic and algorithmic research has revolved around sparsity view of signals over the last decade to characterize new, sub-Nyquist sampling limits as well as tractable algorithms for signal recovery from dimensionality reduced measurements. …”
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Reconstruction algorithms for MRI by Bilgic̦, Berkin

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An Interpretable Stroke Prediction Model using Rules and Bayesian Analysis by Letham, Benjamin, Rudin, Cynthia, McCormick, Tyler H., Madigan, David

“…It employs a novel prior structure to encourage sparsity. Our experiments show that the Bayesian List Machine has predictive accuracy on par with the current top algorithms for prediction in machine learning. …”
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Learning sparse tag patterns for social image classification by Lin, Jie, Duan, Ling-Yu, Yuan, Junsong, Li, Qingyong, Luo, Siwei

“…To alleviate the problem, we introduce a Sparse Tag Patterns (STP) model to discover sparsity constrained co-occurrence tag patterns from large scale user contributed tags among social data. …”
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Compressed sensing for image processing by Yashwant, Mandavilli

“…This thesis work focuses on the sparsity of real-world signals. Sparse representation of images is a new measure and its applications are promising. …”
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Copula Gaussian graphical models with hidden variables by Yu, Hang, Dauwels, Justin, Wang, Xueou

“…In this paper, (1) copula Gaussian hidden variable graphical models are introduced, which extend Gaussian hidden variable graphical models to non-Gaussian data; (2) the sparsity pattern of the hidden variable graphical model is learned via stability selection, which leads to more stable results than cross-validation and other methods to select the regularization parameters. …”
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Full-depth eddy kinetic energy in the global ocean estimated from altimeter and Argo observations by Ni, Q, Zhai, X, LaCasce, JH, Chen, D, Marshall, DP

“…<p>Although the surface eddy kinetic energy (EKE) has been well studied using satellite altimeter and surface drifter observations, our knowledge of EKE in the ocean interior is much more limited due to the sparsity of subsurface current measurements. Here we develop a new approach for estimating EKE over the full depth of the global ocean by combining 20&nbsp;years of satellite altimeter and Argo float data to infer the vertical profile of eddies. …”

A new and improved quantitative recovery analysis for iterative hard thresholding algorithms in compressed sensing by Cartis, C, Thompson, A

“…For both stepsize schemes, we obtain asymptotic phase transitions in a proportional-dimensional framework, quantifying the sparsity/undersampling trade-off for which recovery is guaranteed. …”

Learning to learn graph topologies by Pu, X, Cao, T, Zhang, X, Dong, X, Chen, S

“…Classic methods require an explicit convex function to reflect generic topological priors, e.g. the ℓ1 penalty for enforcing sparsity, which limits the flexibility and expressiveness in learning rich topological structures. …”

Evaluating temporal observation-based causal discovery techniques applied to road driver behaviour by Howard, R, Kunze, L

“…However, as it stands observational causal discovery techniques struggle to adequately cope with conditions such as causal sparsity and non-stationarity typically seen during online usage in autonomous agent domains. …”

Simulation-based counterfactual causal discovery on real world driver behaviour by Howard, R, Kunze, L

“…Observational approaches struggle because of the non-stationarity of causal links in dynamic environments, and the sparsity of causal interactions while requiring the approaches to work in an online fashion. …”

Prediction of welfare outcomes for broiler chickens using Bayesian regression on continuous optical flow data. by Roberts, S, Cain, R, Dawkins, MS

“…Our model combines optical flow descriptors of bird motion with robust multivariate forecasting and provides a sparse, efficient model with sparsity-inducing priors to achieve maximum predictive power with the minimum number of key variables.…”

Chordal and factor-width decompositions for scalable semidefinite and polynomial optimization by Zheng, Y, Fantuzzi, G, Papachristodoulou, A

“…Chordal decomposition exploits the sparsity of semidefinite matrices in a semidefinite program (SDP), in order to formulate an equivalent SDP with smaller semidefinite constraints that can be solved more efficiently. …”

A continuous-time mirror descent approach to sparse phase retrieval by Wu, F, Rebeschini, P

“…This yields a simple algorithm which, unlike most existing approaches to sparse phase retrieval, adapts to the sparsity level, without including thresholding steps or adding regularization terms. …”

Analysing factorizations of action-value networks for cooperative multi-agent reinforcement learning by Castellini, J, Oliehoek, FA, Savani, R, Whiteson, S

“…(Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems, AAMAS’19.International Foundation for Autonomous Agents and Multiagent Systems, pp 1862–1864, 2019) and quantify how well various approaches can represent the requisite value functions, and help us identify the reasons that can impede good performance, like sparsity of the values or too tight coordination requirements.…”

Exponential language modeling using morphological features and multi-task learning by Fang, H, Ostendorf, M, Baumann, P, Pierrehumbert, J

“…For languages with fast vocabulary growth and limited resources, data sparsity leads to challenges in training a language model. …”

Sign-constrained least squares estimation for high-dimensional regression by Meinshausen, N

“…Without using any further regularization, the regression vector can be estimated consistently as long as \log(p) s/n -&gt; 0 for n -&gt; \infty, where s is the sparsity of the optimal regression vector, p the number of variables and n sample size. …”

Multilingual distributed representations without word alignment by Hermann, K, Blunsom, P

“…Distributed representations of meaning are a natural way to encode covariance relationships between words and phrases in NLP. By overcoming data sparsity problems, as well as providing information about semantic relatedness which is not available in discrete representations, distributed representations have proven useful in many NLP tasks. …”

Asymptotic analysis of statistical estimators related to MultiGraphex processes under misspecification by Naulet, Z, Rousseau, J, Caron, F

“…We show that one can relate the limit of the estimator of one of the parameters to the sparsity constant of the true graph generating process. …”

Functionally guided alignment of protein interaction networks for module detection. by Ali, W, Deane, C

“… MOTIVATION: Functional module detection within protein interaction networks is a challenging problem due to the sparsity of data and presence of errors. Computational techniques for this task range from purely graph theoretical approaches involving single networks to alignment of multiple networks from several species. …”