Nonparametric Sparsity and Regularization by Mosci, Sofia, Rosasco, Lorenzo, Santoro, Matteo, Verri, Alessandro, Villa, Silvia

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Nonparametric Sparsity and Regularization by Rosasco, Lorenzo Andrea, Villa, Silvia, Mosci, Sofia, Santoro, Matteo, Verri, Alessandro

“…Based on this intuition we propose a new notion of nonparametric sparsity and a corresponding least squares regularization scheme. …”
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Edge-exchangeable graphs and sparsity by Campbell, Trevor David, Broderick, Tamara A

“…We demonstrate that edge-exchangeable models, unlike models that are traditionally vertex exchangeable, can exhibit sparsity. To do so, we outline a general framework for graph generative models; by contrast to the pioneering work of Caron and Fox [12], models within our framework are stationary across steps of the graph sequence. …”
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Compositional Sparsity of Learnable Functions by Poggio, Tomaso, Fraser, Maia

“…This perspective argues that compositional sparsity, or the property that a compositional function have "few" constituent functions, each depending on only a small subset of inputs, is a key principle underlying successful learning architectures. …”
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Leveraging Diversity and Sparsity in Blind Deconvolution by Ahmed, Ali, Demanet, Laurent

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Sparsity and robustness in modern statistical estimation by Copenhaver, Martin Steven

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Sparsity in Machine Learning: Theory and Applications by Lahlou Kitane, Driss

“…Sparsity plays a key role in machine learning for several reasons including interpretability. …”
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Compositional Sparsity: a framework for ML by Poggio, Tomaso

“…The main claim of this perspective is that compositional sparsity of the target function, which corre- sponds to the task to be learned, is the key principle underlying machine learning. …”
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Rank-Sparsity Incoherence for Matrix Decomposition by Chandrasekaran, Venkat, Sanghavi, Sujay, Parrilo, Pablo A., Willsky, Alan S.

“…We develop a notion of rank-sparsity incoherence, expressed as an uncertainty principle between the sparsity pattern of a matrix and its row and column spaces, and we use it to characterize both fundamental identifiability as well as (deterministic) sufficient conditions for exact recovery. …”
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Iterative Projection Methods for Structured Sparsity Regularization by Rosasco, Lorenzo, Verri, Alessandro, Santoro, Matteo, Mosci, Sofia, Villa, Silvia

“…In this paper we propose a general framework to characterize and solve the optimization problems underlying a large class of sparsity based regularization algorithms. More precisely, we study the minimization of learning functionals that are sums of a differentiable data term and a convex non differentiable penalty. …”
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Necessary and Sufficient Conditions for Sparsity Pattern Recovery by Fletcher, Alyson K., Goyal, Vivek K., Rangan, Sundeep

“…he paper considers the problem of detecting the sparsity pattern of a k -sparse vector in BBR n from m random noisy measurements. …”
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Better approximations for Tree Sparsity in Nearly-Linear Time by Backurs, Arturs, Indyk, Piotr, Schmidt, Ludwig

“…The Tree Sparsity problem is defined as follows: given a node-weighted tree of size n and an integer k, output a rooted subtree of size k with maximum weight. …”
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Lower Bounds on the Column Sparsity of Compressed Sensing Matrices by Nachin, Mergen

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Nonparametric High-dimensional Models: Sparsity, Efficiency, Interpretability by Ibrahim, Shibal

“…The focus of this thesis is on considering various sparsity and structural constraints within these methods and develop optimization based approaches to enhance training efficiency, inference, and/or interpretability. …”
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Efficient Deep Learning with Sparsity: Algorithms, Systems, and Applications by Liu, Zhijian

“…In this dissertation, we present our solutions across the algorithm, system, and application stacks to address the demand-supply gap through the lens of sparsity. In Part I, we first develop algorithms, SparseViT and SparseRefine, which identify sparsity within dense input data. …”
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Transform-domain sparsity regularization for inverse problems in geosciences by Jarapour, Behnam, Goyal, Vivek K., McLaughlin, Dennis, Freeman, William T.

“…Where we have tested our sparsity regulariza-tion approach, it has performed better than traditional alter-natives.…”
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Notes on PCA, Regularization, Sparsity and Support Vector Machines by Poggio, Tomaso, Girosi, Federico

“…In addition to extending the close relations between regularization, Support Vector Machines and sparsity, our work also illuminates and formalizes the LFA concept of Penev and Atick (1996). …”
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Sparsity and computation reduction for high-rate visual-inertial odometry by Frey, Kristoffer M. (Kristoffer Martin)

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A fast algorithm for separated sparsity via perturbed lagrangians by Madry, A, Mitrović, S, Schmidt, L

“…There is now a rich class of structured sparsity approaches that expand the modeling power of the sparsity paradigm and incorporate constraints such as group sparsity, graph sparsity, or hierarchical sparsity. …”
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Estimation of channelized features in geological media using sparsity constraint by Jafarpour, Behnam

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