Media Summary: Stefanie Jegelka, MIT Foundations of Machine ... Models, Inference and Algorithms Broad Institute of MIT and Harvard September 26, 2018 MIA Meeting: ... Professor Stephen Boyd, of the Stanford University Electrical Engineering department,

Lecture 10 Submodular Functions Optimization - Detailed Analysis & Overview

Stefanie Jegelka, MIT Foundations of Machine ... Models, Inference and Algorithms Broad Institute of MIT and Harvard September 26, 2018 MIA Meeting: ... Professor Stephen Boyd, of the Stanford University Electrical Engineering department, A wide variety of problems in machine learning, including exemplar clustering, document summarization, and sensor placement, ... Online primal/dual: e/(e-1) ski rental, set cover; approximation algorithms via dual fitting: set cover. n this talk we consider polynomial matroid

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

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Lecture 10, Submodular Functions, Optimization, & Applications to Machine Learning
Submodularity: Theory and Applications I
10.1 Submodular Functions, Part I
10.6 Continuous Greedy, Part I
MIA: Yaron Singer, Maximizing submodular functions exponentially faster; Primer: Adam Breuer
Lecture 10 | Convex Optimization I (Stanford)
Alina Ene: The Power of Randomization Distributed Submodular Maximization on Massive Datasets
Advanced Algorithms (COMPSCI 224), Lecture 10
Anja Fischer: Polynomial Matroid Optimisation Problems
5-1 Submodularity
Budget-Smoothed Analysis for Submodular Maximization
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

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 ...

10.1 Submodular Functions, Part I

10.1 Submodular Functions, Part I

This is the first

10.6 Continuous Greedy, Part I

10.6 Continuous Greedy, Part I

The next two

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: ...

Lecture 10 | Convex Optimization I (Stanford)

Lecture 10 | Convex Optimization I (Stanford)

Professor Stephen Boyd, of the Stanford University Electrical Engineering department,

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, ...

Advanced Algorithms (COMPSCI 224), Lecture 10

Advanced Algorithms (COMPSCI 224), Lecture 10

Online primal/dual: e/(e-1) ski rental, set cover; approximation algorithms via dual fitting: set cover.

Anja Fischer: Polynomial Matroid Optimisation Problems

Anja Fischer: Polynomial Matroid Optimisation Problems

n this talk we consider polynomial matroid

5-1 Submodularity

5-1 Submodularity

Submodular functions

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 ...

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 ...

[2024/25 Winter Lecture] Lecture 10. Discrete and Continuous Submodular Function Maximization

[2024/25 Winter Lecture] Lecture 10. Discrete and Continuous Submodular Function Maximization

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