Media Summary: The Fujishige-Wolfe heuristic is empirically one of the fastest algorithms for HIM Workshop: Continuous approaches to discrete optimization. Owing to several applications in large scale learning and vision problems, fast

Deeparnab Chakrabarty Provable Submodular Function - Detailed Analysis & Overview

The Fujishige-Wolfe heuristic is empirically one of the fastest algorithms for HIM Workshop: Continuous approaches to discrete optimization. Owing to several applications in large scale learning and vision problems, fast Haotian Jiang (Microsoft Research, Redmond) ... Deeparnab Chakrabarty, Faster Matroid Intersection APPROX: Submodular Dominance and Applications

Jeff Bilmes, University of Washington Interactive Learning.

Photo Gallery

Deeparnab Chakrabarty: Provable Submodular Function Minimization via Fujishige Wolfe Algorithm
Deeparnab Chakrabarty: Provable Submodular Function Minimization via Fujishige Wolfe Algorithm
NIPS: Oral Session 1 - Deeparnab Chakrabarty
Deeparnab Chakrabarty: Polynomial Lower Bounds for Parallel Submodular Function Minimization
Provable Submodular Minimization via Wolfe’s Algorithm
MIT 6.854 Spring 2016 Lecture 13: Submodular Functions
Recent Progress on Submodular Function Minimization
Deeparnab Chakrabarty, Faster Matroid Intersection
APPROX: Submodular Dominance and Applications
Stefanie Jegelka: An introduction to Submodularity, Part 1
Deeparnab
Decomposable Submodular Function Minimization via Maximum Flow (Kyriakos Axiotis)
View Detailed Profile
Deeparnab Chakrabarty: Provable Submodular Function Minimization via Fujishige Wolfe Algorithm

Deeparnab Chakrabarty: Provable Submodular Function Minimization via Fujishige Wolfe Algorithm

The Fujishige-Wolfe heuristic is empirically one of the fastest algorithms for

Deeparnab Chakrabarty: Provable Submodular Function Minimization via Fujishige Wolfe Algorithm

Deeparnab Chakrabarty: Provable Submodular Function Minimization via Fujishige Wolfe Algorithm

The Fujishige-Wolfe heuristic is empirically one of the fastest algorithms for

NIPS: Oral Session 1 - Deeparnab Chakrabarty

NIPS: Oral Session 1 - Deeparnab Chakrabarty

Provable Submodular

Deeparnab Chakrabarty: Polynomial Lower Bounds for Parallel Submodular Function Minimization

Deeparnab Chakrabarty: Polynomial Lower Bounds for Parallel Submodular Function Minimization

HIM Workshop: Continuous approaches to discrete optimization.

Provable Submodular Minimization via Wolfe’s Algorithm

Provable Submodular Minimization via Wolfe’s Algorithm

Owing to several applications in large scale learning and vision problems, fast

MIT 6.854 Spring 2016 Lecture 13: Submodular Functions

MIT 6.854 Spring 2016 Lecture 13: Submodular Functions

Recorded by Andrew Xia 2016.

Recent Progress on Submodular Function Minimization

Recent Progress on Submodular Function Minimization

Haotian Jiang (Microsoft Research, Redmond) ...

Deeparnab Chakrabarty, Faster Matroid Intersection

Deeparnab Chakrabarty, Faster Matroid Intersection

Deeparnab Chakrabarty, Faster Matroid Intersection

APPROX: Submodular Dominance and Applications

APPROX: Submodular Dominance and Applications

APPROX: Submodular Dominance and Applications

Stefanie Jegelka: An introduction to Submodularity, Part 1

Stefanie Jegelka: An introduction to Submodularity, Part 1

Abstract:

Deeparnab

Deeparnab

Deeparnab

Decomposable Submodular Function Minimization via Maximum Flow (Kyriakos Axiotis)

Decomposable Submodular Function Minimization via Maximum Flow (Kyriakos Axiotis)

Submodular function

Interactive Learning of Mixtures of Submodular Functions

Interactive Learning of Mixtures of Submodular Functions

Jeff Bilmes, University of Washington https://simons.berkeley.edu/talks/jeff-bilmes-02-17-2017 Interactive Learning.