Media Summary: Whether we are talking new antibiotics or an agent to fight viruses, we are constantly searching for new drugs. With the help of ... Abstract: Submodular functions capture a wide spectrum of discrete problems in machine Other we go there assume that function zer at SH and that something that can evalu Okay functions come up in machine

Robust Learning Via Robust Optimization Stefanie Jegelka - Detailed Analysis & Overview

Whether we are talking new antibiotics or an agent to fight viruses, we are constantly searching for new drugs. With the help of ... Abstract: Submodular functions capture a wide spectrum of discrete problems in machine Other we go there assume that function zer at SH and that something that can evalu Okay functions come up in machine Please find more details about the seminar on our webpage: Graph Neural Networks (GNNs) have become a popular tool for

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Robust Learning via Robust Optimization - Stefanie Jegelka
Submodularity - Stefanie Jegelka - MLSS 2017
Optimisation of graph neural networks | Humboldt Professor Stefanie Jegelka
Stefanie Jegelka: An introduction to Submodularity, Part 1
Learning with Graphs and Combinatorial Structures with Stefanie Jegelka
Stefanie Jegelka 1: Submodularity
Fireside Chat with Stefanie Jegelka
CSAIL Alliances Researcher Spotlight: Stefanie Jegelka
Stefanie Jegelka: An introduction to submodularity, Part 2
Frauke Liers - (Data-driven) distributional robustness over time: How to 'learn' relevant uncertai..
Bo Zeng - A Study of Distributionally Robust Optimization from Primal Perspective
NIPS 2016 Workshop on Nonconvex Optimization: Stefanie Jegelka (Submodularity & Nonconvexity)
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Robust Learning via Robust Optimization - Stefanie Jegelka

Robust Learning via Robust Optimization - Stefanie Jegelka

Stefanie Jegelka

Submodularity - Stefanie Jegelka - MLSS 2017

Submodularity - Stefanie Jegelka - MLSS 2017

This is

Optimisation of graph neural networks | Humboldt Professor Stefanie Jegelka

Optimisation of graph neural networks | Humboldt Professor Stefanie Jegelka

Whether we are talking new antibiotics or an agent to fight viruses, we are constantly searching for new drugs. With the help of ...

Stefanie Jegelka: An introduction to Submodularity, Part 1

Stefanie Jegelka: An introduction to Submodularity, Part 1

Abstract: Submodular functions capture a wide spectrum of discrete problems in machine

Learning with Graphs and Combinatorial Structures with Stefanie Jegelka

Learning with Graphs and Combinatorial Structures with Stefanie Jegelka

MIT CSAIL's

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Stefanie Jegelka 1: Submodularity

Stefanie Jegelka 1: Submodularity

Other we go there assume that function zer at SH and that something that can evalu Okay functions come up in machine

Fireside Chat with Stefanie Jegelka

Fireside Chat with Stefanie Jegelka

Stefanie Jegelka

CSAIL Alliances Researcher Spotlight: Stefanie Jegelka

CSAIL Alliances Researcher Spotlight: Stefanie Jegelka

Can my machine

Stefanie Jegelka: An introduction to submodularity, Part 2

Stefanie Jegelka: An introduction to submodularity, Part 2

Abstract: Submodular functions capture a wide spectrum of discrete problems in machine

Frauke Liers - (Data-driven) distributional robustness over time: How to 'learn' relevant uncertai..

Frauke Liers - (Data-driven) distributional robustness over time: How to 'learn' relevant uncertai..

Please find more details about the seminar on our webpage: https://sites.google.com/view/row-series/home.

Bo Zeng - A Study of Distributionally Robust Optimization from Primal Perspective

Bo Zeng - A Study of Distributionally Robust Optimization from Primal Perspective

More information on our webpage: https://sites.google.com/view/row-series/home.

NIPS 2016 Workshop on Nonconvex Optimization: Stefanie Jegelka (Submodularity & Nonconvexity)

NIPS 2016 Workshop on Nonconvex Optimization: Stefanie Jegelka (Submodularity & Nonconvexity)

NIPS 2016 Workshop on Nonconvex

ML4A 2021 - Stefanie Jegelka

ML4A 2021 - Stefanie Jegelka

Graph Neural Networks (GNNs) have become a popular tool for