Structured Sparsity Inducing Norms Through Submodular Functions

By Editorial Team · October 27, 2026 · 9 min read

Structured Sparsity Inducing Norms Through Submodular Functions Information Guide

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Main Features

Sparsity and the L1 norm (DS4DS 6.03) Guide
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Recent Updates

Details Sparsity and the L1 Norm Guide
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Submodularity: Theory and Applications I
Submodularity: Theory and Applications I
Class 6 - Structured sparsity
Class 6 - Structured sparsity
9.520 - 10/19/2015 - Class 12 - Prof. Lorenzo Rosasco: Structured Sparsity Regularization
9.520 - 10/19/2015 - Class 12 - Prof. Lorenzo Rosasco: Structured Sparsity Regularization
Adversarially Robust Submodular Maximization under Knapsack Constraint
Adversarially Robust Submodular Maximization under Knapsack Constraint
Stefanie Jegelka: An introduction to Submodularity, Part 1
Stefanie Jegelka: An introduction to Submodularity, Part 1
EE596B Lecture 4, Submodular Functions, Optimization, and Applications to Machine Learning
EE596B Lecture 4, Submodular Functions, Optimization, and Applications to Machine Learning
Lecture 8, Submodular Functions, Optimization, & Applications to Machine Learning
Lecture 8, Submodular Functions, Optimization, & Applications to Machine Learning
Lecture 11, Submodular Functions, Optimization, & Applications to Machine Learning
Lecture 11, Submodular Functions, Optimization, & Applications to Machine Learning
Lecture 13, Submodular Functions, Optimization, & Applications to Machine Learning
Lecture 13, Submodular Functions, Optimization, & Applications to Machine Learning
EE596B Lecture 6, Submodular Functions, Optimization, & Applications to Machine Learning
EE596B Lecture 6, Submodular Functions, Optimization, & Applications to Machine Learning
Andre Martins - Softmax: Adaptively Sparse Transformers
Andre Martins - Softmax: Adaptively Sparse Transformers

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Last Updated: October 27, 2026

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Class 13 - Structured Sparsity Regularization Guide
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Francis Bach, INRIA and ENS Paris Succinct Data Representations and Applications ... Hosts: Sebastian Peitz - orcid.org/0000-0002-3389-793X Oliver Wallscheid - linkedin.com/in/wallscheid/ ... Lorenzo Rosasco, MIT, University of Genoa, IIT 9.520/6.860S Statistical Learning Theory and Applications Class website: ... Stefanie Jegelka, MIT simons.berkeley.edu/talks/andreas-krause-stefanie-jegelka-01-23-2017-1 Foundations of Machine ... Lorenzo Rosasco 30 giugno 2016. Authors: Dmitrii Avdiukhin (Indiana University Bloomington);Slobodan Mitrovic (MIT);Grigory Yaroslavtsev (Indiana University ...

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