Media Summary: This is the first lecture in the series on In this lecture we give the basic greedy algorithm, and give the proof by Wolsey, Nemhauser and Fisher stating that if \mathcal{I} is ... In this lecture we consider the problem of maximizing a monotone

10 1 Submodular Functions Part - Detailed Analysis & Overview

This is the first lecture in the series on In this lecture we give the basic greedy algorithm, and give the proof by Wolsey, Nemhauser and Fisher stating that if \mathcal{I} is ... In this lecture we consider the problem of maximizing a monotone Many problems in machine learning that involve discrete structures or subset selection may be phrased in the language of ... Speaker: Fabien Mathieu (Swapcard). Webpage: This videos from ICSI660 class in 12/03/2018. The professor is Feng Chen. He comes from University at Albany, State University ...

Models, Inference and Algorithms Broad Institute of MIT and Harvard September 26, 2018 MIA Meeting: ...

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10.1 Submodular Functions, Part I
10.2 Submodular Functions, Part II
Submodularity: Theory and Applications I
Lecture 10, Submodular Functions, Optimization, & Applications to Machine Learning
5-1 Submodularity
Jan Vondrak - Submodular Functions and Their Applications
10.3 Submodular Functions, Part III
Submodular Optimization and Machine Learning - Part 1
Introduction to Submodular Functions
MIT 6.854 Spring 2016 Lecture 13: Submodular Functions
EE596B Lecture 3, Submodular Functions, Optimization, and Applications to Machine Learning
Submodular Optimization 1
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10.1 Submodular Functions, Part I

10.1 Submodular Functions, Part I

This is the first lecture in the series on

10.2 Submodular Functions, Part II

10.2 Submodular Functions, Part II

In this lecture we give the basic greedy algorithm, and give the proof by Wolsey, Nemhauser and Fisher stating that if \mathcal{I} is ...

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-

Lecture 10, Submodular Functions, Optimization, & Applications to Machine Learning

Lecture 10, Submodular Functions, Optimization, & Applications to Machine Learning

Submodular Functions

5-1 Submodularity

5-1 Submodularity

Submodular functions

Jan Vondrak - Submodular Functions and Their Applications

Jan Vondrak - Submodular Functions and Their Applications

Jan Vondrak from IBM Almaden presents as

10.3 Submodular Functions, Part III

10.3 Submodular Functions, Part III

In this lecture we consider the problem of maximizing a monotone

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 ...

Introduction to Submodular Functions

Introduction to Submodular Functions

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

MIT 6.854 Spring 2016 Lecture 13: Submodular Functions

MIT 6.854 Spring 2016 Lecture 13: Submodular Functions

Recorded by Andrew Xia 2016.

EE596B Lecture 3, Submodular Functions, Optimization, and Applications to Machine Learning

EE596B Lecture 3, Submodular Functions, Optimization, and Applications to Machine Learning

Submodular Functions

Submodular Optimization 1

Submodular Optimization 1

This videos from ICSI660 class in 12/03/2018. The professor is Feng Chen. He comes from University at Albany, State University ...

MIA: Yaron Singer, Maximizing submodular functions exponentially faster; Primer: Adam Breuer

MIA: Yaron Singer, Maximizing submodular functions exponentially faster; Primer: Adam Breuer

Models, Inference and Algorithms Broad Institute of MIT and Harvard September 26, 2018 MIA Meeting: ...