Faster Spectral Algorithms Via Approximation

Media Summary: Nisheeth Vishnoi, École Polytechnique Fédérale de Lausanne Fangjin Yang and Nelson Ray present at Strata NYC 2013. Simon Apers (INRIA) The Quantum Wave in Computing Seminar, Apr. 7, 2020 Graph sparsification underlies a large number of ...

Faster Spectral Algorithms Via Approximation - Detailed Analysis & Overview

Nisheeth Vishnoi, École Polytechnique Fédérale de Lausanne Fangjin Yang and Nelson Ray present at Strata NYC 2013. Simon Apers (INRIA) The Quantum Wave in Computing Seminar, Apr. 7, 2020 Graph sparsification underlies a large number of ... Sushant Sachdeva Institute for Advanced Study April 16, 2012 The goal of the Balanced Separator problem is to find a balanced ... Ryan Adams, Harvard University Computational Challenges in Machine Learning Convex optimization is a key tool in computer science, with applications ranging from machine learning to operational research.

Tselil Schramm, UC Berkeley Hierarchies, Extended Formulations and ... Nikhil Srivastava, Microsoft Research India Succinct Data Representations and Applications ...

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Faster Spectral Algorithms via Approximation Theory

Not Exactly! Fast Queries Via Approximation Algorithms

Quantum Speedup for Graph Sparsification, Cut Approximation and Laplacian Solving

Fast Flow Algorithms via Cut-Approximators

8 Spectral Estimation Algorithms in 2 Minutes

Near-Linear Time Approximation Algorithm for Balanced Separator - Sushant Sachdeva

Cut-Approximators, Approximating Undirected Max Flows, and Recursion

Unbiased Estimation of the Spectral Properties of Large Implicit Matrices

Fast Regression Algorithms Using Spectral Graph Theory

Fast Spectral Algorithms from Sum-of-Squares Analyses

Overcoming The Spectral Bias of Neural Value Approximation

Spectral Sparsification of Graphs

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Faster Spectral Algorithms via Approximation Theory

Faster Spectral Algorithms via Approximation Theory

Nisheeth Vishnoi, École Polytechnique Fédérale de Lausanne

Not Exactly! Fast Queries Via Approximation Algorithms

Not Exactly! Fast Queries Via Approximation Algorithms

Fangjin Yang and Nelson Ray present at Strata NYC 2013.

Quantum Speedup for Graph Sparsification, Cut Approximation and Laplacian Solving

Quantum Speedup for Graph Sparsification, Cut Approximation and Laplacian Solving

Simon Apers (INRIA) The Quantum Wave in Computing Seminar, Apr. 7, 2020 Graph sparsification underlies a large number of ...

Fast Flow Algorithms via Cut-Approximators

Fast Flow Algorithms via Cut-Approximators

Jonah Sherman, UC Berkeley

8 Spectral Estimation Algorithms in 2 Minutes

8 Spectral Estimation Algorithms in 2 Minutes

Visualization of 8

Near-Linear Time Approximation Algorithm for Balanced Separator - Sushant Sachdeva

Near-Linear Time Approximation Algorithm for Balanced Separator - Sushant Sachdeva

Sushant Sachdeva Institute for Advanced Study April 16, 2012 The goal of the Balanced Separator problem is to find a balanced ...

Cut-Approximators, Approximating Undirected Max Flows, and Recursion

Cut-Approximators, Approximating Undirected Max Flows, and Recursion

... Institute of Technology

Unbiased Estimation of the Spectral Properties of Large Implicit Matrices

Unbiased Estimation of the Spectral Properties of Large Implicit Matrices

Ryan Adams, Harvard University Computational Challenges in Machine Learning https://simons.berkeley.edu/talks/tba.

Fast Regression Algorithms Using Spectral Graph Theory

Fast Regression Algorithms Using Spectral Graph Theory

Convex optimization is a key tool in computer science, with applications ranging from machine learning to operational research.

Fast Spectral Algorithms from Sum-of-Squares Analyses

Fast Spectral Algorithms from Sum-of-Squares Analyses

Tselil Schramm, UC Berkeley https://simons.berkeley.edu/talks/tselil-schramm-11-9-17 Hierarchies, Extended Formulations and ...

Overcoming The Spectral Bias of Neural Value Approximation

Overcoming The Spectral Bias of Neural Value Approximation

Value

Spectral Sparsification of Graphs

Spectral Sparsification of Graphs

Nikhil Srivastava, Microsoft Research India Succinct Data Representations and Applications ...

First-Order Optimization and Online Learning Techniques in the Design of Fast Spectral Algorithms

First-Order Optimization and Online Learning Techniques in the Design of Fast Spectral Algorithms

... Technology and Boston U