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 Speaker: Fabien Mathieu (Swapcard). Webpage: Many problems in machine learning that involve discrete structures or subset selection may be phrased in the language of ... A Google Algorithms TechTalk, 2021/01/14, presented by Mehrdad Ghadiri.

Jeff Bilmes, University of Washington Interactive Learning. This videos from ICSI660 class in 12/03/2018. The professor is Feng Chen. He comes from University at Albany, State University ... The study of combinatorial problems with a The next two lectures revisit the problem of maximizing a monotone

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10.1 Submodular Functions, Part I
10.2 Submodular Functions, Part II
Stefanie Jegelka 1: Submodularity
10.3 Submodular Functions, Part III
Lecture 10, Submodular Functions, Optimization, & Applications to Machine Learning
Introduction to Submodular Functions
Submodular Optimization and Machine Learning - Part 1
Submodularity and Optimization -- Jeff Bilmes (Part 1)
Beyond Submodular Maximization via One-Sided Smoothness and Meta-Submodularity
Interactive Learning of Mixtures of Submodular Functions
Submodular Optimization 1
Continuous Methods for Submodular Maximization
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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 ...

Stefanie Jegelka 1: Submodularity

Stefanie Jegelka 1: Submodularity

Other we go there assume that

10.3 Submodular Functions, Part III

10.3 Submodular Functions, Part III

In this lecture we consider the problem of maximizing a monotone

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

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

Submodular Functions

Introduction to Submodular Functions

Introduction to Submodular Functions

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

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 ...

Submodularity and Optimization -- Jeff Bilmes (Part 1)

Submodularity and Optimization -- Jeff Bilmes (Part 1)

Intro ...

Beyond Submodular Maximization via One-Sided Smoothness and Meta-Submodularity

Beyond Submodular Maximization via One-Sided Smoothness and Meta-Submodularity

A Google Algorithms TechTalk, 2021/01/14, presented by Mehrdad Ghadiri.

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.

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 ...

Continuous Methods for Submodular Maximization

Continuous Methods for Submodular Maximization

The study of combinatorial problems with a

10.6 Continuous Greedy, Part I

10.6 Continuous Greedy, Part I

The next two lectures revisit the problem of maximizing a monotone