Media Summary: Stefanie Jegelka, MIT Foundations of Machine ... Introduction to Machine Learning (PhD level) A wide variety of problems in machine learning, including exemplar clustering, document summarization, and sensor placement, ...

Lecture 10 Submodular Functions Optimization - Detailed Analysis & Overview

Stefanie Jegelka, MIT Foundations of Machine ... Introduction to Machine Learning (PhD level) A wide variety of problems in machine learning, including exemplar clustering, document summarization, and sensor placement, ... Models, Inference and Algorithms Broad Institute of MIT and Harvard September 26, 2018 MIA Meeting: ... n this talk we consider polynomial matroid A Google Algorithms TechTalk, 2021/01/14, presented by Mehrdad Ghadiri.

Francis Bach, INRIA and ENS Paris Succinct Data Representations and Applications ... 13th Innovations in Theoretical Computer Science Conference (ITCS 2022) Budget-Smoothed Analysis for ...

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Lecture 10, Submodular Functions, Optimization, & Applications to Machine Learning
10.6 Continuous Greedy, Part I
10.1 Submodular Functions, Part I
Submodularity: Theory and Applications I
Machine Learning 10-701 Lecture 8 Optimization
Alina Ene: The Power of Randomization Distributed Submodular Maximization on Massive Datasets
MIA: Yaron Singer, Maximizing submodular functions exponentially faster; Primer: Adam Breuer
Anja Fischer: Polynomial Matroid Optimisation Problems
MIT 6.854 Spring 2016 Lecture 13: Submodular Functions
10.3 Submodular Functions, Part III
Beyond Submodular Maximization via One-Sided Smoothness and Meta-Submodularity
Structured Sparsity-Inducing Norms Through Submodular Functions
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Lecture 10, Submodular Functions, Optimization, & Applications to Machine Learning

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

Submodular Functions

10.6 Continuous Greedy, Part I

10.6 Continuous Greedy, Part I

The next two

10.1 Submodular Functions, Part I

10.1 Submodular Functions, Part I

This is the first

Submodularity: Theory and Applications I

Submodularity: Theory and Applications I

Stefanie Jegelka, MIT https://simons.berkeley.edu/talks/andreas-krause-stefanie-jegelka-01-23-2017-1 Foundations of Machine ...

Machine Learning 10-701 Lecture 8 Optimization

Machine Learning 10-701 Lecture 8 Optimization

Introduction to Machine Learning (PhD level) http://alex.smola.org/teaching/cmu2013-

Alina Ene: The Power of Randomization Distributed Submodular Maximization on Massive Datasets

Alina Ene: The Power of Randomization Distributed Submodular Maximization on Massive Datasets

A wide variety of problems in machine learning, including exemplar clustering, document summarization, and sensor placement, ...

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: ...

Anja Fischer: Polynomial Matroid Optimisation Problems

Anja Fischer: Polynomial Matroid Optimisation Problems

n this talk we consider polynomial matroid

MIT 6.854 Spring 2016 Lecture 13: Submodular Functions

MIT 6.854 Spring 2016 Lecture 13: Submodular Functions

Recorded by Andrew Xia 2016.

10.3 Submodular Functions, Part III

10.3 Submodular Functions, Part III

In this

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.

Structured Sparsity-Inducing Norms Through Submodular Functions

Structured Sparsity-Inducing Norms Through Submodular Functions

Francis Bach, INRIA and ENS Paris Succinct Data Representations and Applications ...

Budget-Smoothed Analysis for Submodular Maximization

Budget-Smoothed Analysis for Submodular Maximization

13th Innovations in Theoretical Computer Science Conference (ITCS 2022) http://itcs-conf.org/ Budget-Smoothed Analysis for ...