Media Summary: Presentation video for the CDC'22 paper " ... correct another optimization related to subm modular functions is the subm modular Vahab Mirrokni, Google Learning, Algorithm Design and Beyond ...

Resource Aware Distributed Submodular Maximization - Detailed Analysis & Overview

Presentation video for the CDC'22 paper " ... correct another optimization related to subm modular functions is the subm modular Vahab Mirrokni, Google Learning, Algorithm Design and Beyond ... A wide variety of problems in machine learning, including exemplar clustering, document summarization, and sensor placement, ... Presentation video for the RSS'23 paper "Bandit The study of combinatorial problems with a

Stefanie Jegelka, MIT Foundations of Machine ... Yandex School of Data Analysis Conference Machine Learning: Prospects and Applications ... A Google Algorithms TechTalk, 2021/01/14, presented by Mehrdad Ghadiri. Morteza Zadimoghaddam: Randomized Composable Core-sets for Solving stochastic optimization problems under partial observability, where one needs to adaptively make decisions with ... Models, Inference and Algorithms Broad Institute of MIT and Harvard September 26, 2018 MIA Meeting: ...

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Resource-Aware Distributed Submodular Maximization: A Paradigm for Multi-Robot Decision-Making
5-2 Submodular Maximization
Sketching and Randomization for Distributed Submodular and Coverage Optimization
Alina Ene: The Power of Randomization Distributed Submodular Maximization on Massive Datasets
Bandit Submodular Maximization for Multi-Robot Coordination in Unpredictable Environments
Continuous Methods for Submodular Maximization
Submodularity: Theory and Applications I
Summarizing Massive Data Sets via Large-Scale Submodular Maximization - Prof. Andreas Krause
Beyond Submodular Maximization via One-Sided Smoothness and Meta-Submodularity
Session 1C - A polynomial lower bound on adaptive complexity of submodular maximization
M. Zadimoghaddam: Randomized Composable Core-sets for Submodular Maximization
Adaptive Submodularity: A New Approach to Active Learning and Stochastic Optimization
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Resource-Aware Distributed Submodular Maximization: A Paradigm for Multi-Robot Decision-Making

Resource-Aware Distributed Submodular Maximization: A Paradigm for Multi-Robot Decision-Making

Presentation video for the CDC'22 paper "

5-2 Submodular Maximization

5-2 Submodular Maximization

... correct another optimization related to subm modular functions is the subm modular

Sketching and Randomization for Distributed Submodular and Coverage Optimization

Sketching and Randomization for Distributed Submodular and Coverage Optimization

Vahab Mirrokni, Google https://simons.berkeley.edu/talks/vahab-mirrokni-2016-11-17 Learning, Algorithm Design and Beyond ...

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, ...

Bandit Submodular Maximization for Multi-Robot Coordination in Unpredictable Environments

Bandit Submodular Maximization for Multi-Robot Coordination in Unpredictable Environments

Presentation video for the RSS'23 paper "Bandit

Continuous Methods for Submodular Maximization

Continuous Methods for Submodular Maximization

The study of combinatorial problems with a

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 ...

Summarizing Massive Data Sets via Large-Scale Submodular Maximization - Prof. Andreas Krause

Summarizing Massive Data Sets via Large-Scale Submodular Maximization - Prof. Andreas Krause

Yandex School of Data Analysis Conference Machine Learning: Prospects and Applications ...

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.

Session 1C - A polynomial lower bound on adaptive complexity of submodular maximization

Session 1C - A polynomial lower bound on adaptive complexity of submodular maximization

Link to slides: https://cs.stanford.edu/people/paulliu/files/stoc-2020-slides.pdf Link to paper: ...

M. Zadimoghaddam: Randomized Composable Core-sets for Submodular Maximization

M. Zadimoghaddam: Randomized Composable Core-sets for Submodular Maximization

Morteza Zadimoghaddam: Randomized Composable Core-sets for

Adaptive Submodularity: A New Approach to Active Learning and Stochastic Optimization

Adaptive Submodularity: A New Approach to Active Learning and Stochastic Optimization

Solving stochastic optimization problems under partial observability, where one needs to adaptively make decisions with ...

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: ...