Continuous Methods For Submodular Maximization

Media Summary: The study of combinatorial problems with a A brief introduction to the NIPS 2017 paper "Non-monotone NIPS 2016 Workshop on Nonconvex Optimization: Francis Bach (

Continuous Methods For Submodular Maximization - Detailed Analysis & Overview

The study of combinatorial problems with a A brief introduction to the NIPS 2017 paper "Non-monotone NIPS 2016 Workshop on Nonconvex Optimization: Francis Bach ( Mahdi Soltanolkotabi, University of Southern California Fast ... Stefanie Jegelka, MIT Foundations of Machine ... Tim Roughgarden and Joshua Wang An Optimal Learning Algorithm for Online Unconstrained

A Google Algorithms TechTalk, 2021/01/14, presented by Mehrdad Ghadiri. Niv Buchbinder, Tel Aviv University Discrete Optimization via ...

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Continuous Methods for Submodular Maximization

Non-monotone Continuous DR-submodular Maximization: Structure and Algorithms

5-2 Submodular Maximization

Submodularity - Stefanie Jegelka - MLSS 2017

NIPS 2016 Workshop on Nonconvex Optimization: Francis Bach (Submodularity: Discrete to Continuous)

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

Nonconvex Optimization for High-dimensional Learning: From ReLUs to Submodular Maximization

Fast and Simple Algorithms for Constrained Submodular Maximization

Submodularity: Theory and Applications I

An Optimal Learning Algorithm for Online Unconstrained Submodular Maximization

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

Submodular Optimization

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Continuous Methods for Submodular Maximization

Continuous Methods for Submodular Maximization

The study of combinatorial problems with a

Non-monotone Continuous DR-submodular Maximization: Structure and Algorithms

Non-monotone Continuous DR-submodular Maximization: Structure and Algorithms

A brief introduction to the NIPS 2017 paper "Non-monotone

5-2 Submodular Maximization

5-2 Submodular Maximization

Submodular Maximization

Submodularity - Stefanie Jegelka - MLSS 2017

Submodularity - Stefanie Jegelka - MLSS 2017

This is Stefanie Jegelka's lecture on

NIPS 2016 Workshop on Nonconvex Optimization: Francis Bach (Submodularity: Discrete to Continuous)

NIPS 2016 Workshop on Nonconvex Optimization: Francis Bach (Submodularity: Discrete to Continuous)

NIPS 2016 Workshop on Nonconvex Optimization: Francis Bach (

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

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

Lecture #10: Discrete and

Nonconvex Optimization for High-dimensional Learning: From ReLUs to Submodular Maximization

Nonconvex Optimization for High-dimensional Learning: From ReLUs to Submodular Maximization

Mahdi Soltanolkotabi, University of Southern California https://simons.berkeley.edu/talks/mahdi-soltanolkotabi-10-05-17 Fast ...

Fast and Simple Algorithms for Constrained Submodular Maximization

Fast and Simple Algorithms for Constrained Submodular Maximization

Submodular maximization

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 ...

An Optimal Learning Algorithm for Online Unconstrained Submodular Maximization

An Optimal Learning Algorithm for Online Unconstrained Submodular Maximization

Tim Roughgarden and Joshua Wang An Optimal Learning Algorithm for Online Unconstrained

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.

Submodular Optimization

Submodular Optimization

Niv Buchbinder, Tel Aviv University https://simons.berkeley.edu/talks/niv-buchbinder-09-13-17 Discrete Optimization via ...

Continuous Methods for Discrete Optimization: From Convex Relaxations, to Iterative Schemes...

Continuous Methods for Discrete Optimization: From Convex Relaxations, to Iterative Schemes...

Aleksander Mądry, MIT https://simons.berkeley.edu/talks/alexander-madry-10-02-17 Fast Iterative