Media Summary: B now we can compare this in terms of the inequality we need for Many problems in machine learning that involve discrete structures or subset selection may be phrased in the language of ... ... positive values so we index from one to r this is going to be equal to the indices from

5 1 Submodularity - Detailed Analysis & Overview

B now we can compare this in terms of the inequality we need for Many problems in machine learning that involve discrete structures or subset selection may be phrased in the language of ... ... positive values so we index from one to r this is going to be equal to the indices from Speaker: Fabien Mathieu (Swapcard). Webpage: Jeff Bilmes, University of Washington Interactive Learning.

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5-1 Submodularity
Stefanie Jegelka 1: Submodularity
Submodularity - Stefanie Jegelka - MLSS 2017
Submodularity: Theory and Applications I
Submodular Optimization and Machine Learning - Part 1
MIT 6.854 Spring 2016 Lecture 13: Submodular Functions
5-2 Submodular Maximization
Submodularity and Optimization -- Jeff Bilmes (Part 1)
EE596B Lecture 5, Submodular Functions, Optimization, and Applications to Machine Learning
Introduction to Submodular Functions
5B 1  Revisiting Modified Greedy Algorithm for Monotone Submodular Maximization with a Knapsack Cons
EE596B Lecture 4, Submodular Functions, Optimization, and Applications to Machine Learning
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5-1 Submodularity

5-1 Submodularity

B now we can compare this in terms of the inequality we need for

Stefanie Jegelka 1: Submodularity

Stefanie Jegelka 1: Submodularity

Stefanie Jegelka 1: Submodularity

Submodularity - Stefanie Jegelka - MLSS 2017

Submodularity - Stefanie Jegelka - MLSS 2017

This is Stefanie Jegelka's lecture on

Submodularity: Theory and Applications I

Submodularity: Theory and Applications I

Stefanie Jegelka, MIT https://simons.berkeley.edu/talks/andreas-krause-stefanie-jegelka-01-23-2017-

Submodular Optimization and Machine Learning - Part 1

Submodular Optimization and Machine Learning - Part 1

Many problems in machine learning that involve discrete structures or subset selection may be phrased in the language of ...

MIT 6.854 Spring 2016 Lecture 13: Submodular Functions

MIT 6.854 Spring 2016 Lecture 13: Submodular Functions

Recorded by Andrew Xia 2016.

5-2 Submodular Maximization

5-2 Submodular Maximization

... positive values so we index from one to r this is going to be equal to the indices from

Submodularity and Optimization -- Jeff Bilmes (Part 1)

Submodularity and Optimization -- Jeff Bilmes (Part 1)

Intro ...

EE596B Lecture 5, Submodular Functions, Optimization, and Applications to Machine Learning

EE596B Lecture 5, Submodular Functions, Optimization, and Applications to Machine Learning

Submodular

Introduction to Submodular Functions

Introduction to Submodular Functions

Speaker: Fabien Mathieu (Swapcard). Webpage: https://www.lincs.fr/events/introduction-to-

5B 1  Revisiting Modified Greedy Algorithm for Monotone Submodular Maximization with a Knapsack Cons

5B 1 Revisiting Modified Greedy Algorithm for Monotone Submodular Maximization with a Knapsack Cons

Introduction ...

EE596B Lecture 4, Submodular Functions, Optimization, and Applications to Machine Learning

EE596B Lecture 4, Submodular Functions, Optimization, and Applications to Machine Learning

Submodular

Interactive Learning of Mixtures of Submodular Functions

Interactive Learning of Mixtures of Submodular Functions

Jeff Bilmes, University of Washington https://simons.berkeley.edu/talks/jeff-bilmes-02-17-2017 Interactive Learning.