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Related Experiment Videos

Computational and statistical tradeoffs via convex relaxation.

Venkat Chandrasekaran¹, Michael I Jordan

¹Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA 91125, USA.

Proceedings of the National Academy of Sciences of the United States of America

|March 13, 2013

Summary

This study introduces algorithmic weakening to balance computational and statistical efficiency for massive datasets. It provides guarantees on statistical inference quality within limited computational resources, like time and space.

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Area of Science:

Computational Science
Statistical Science
Data Science

Background:

Massive datasets pose challenges for statistical inference quality under computational constraints.
Existing methods lack guarantees relating computational resources to inference accuracy.

Purpose of the Study:

To develop a framework for guaranteeing statistical inference quality with bounded computational resources.
To introduce and analyze the concept of algorithmic weakening.

Main Methods:

Defined "algorithmic weakening" to create a hierarchy of algorithms.
Ordered algorithms by computational and statistical efficiency.
Applied convex relaxation to denoising problems.

Main Results:

Demonstrated tradeoffs between data scale and processing sophistication.
Showcased how algorithmic weakening relates runtime to data amount.
Provided statistical characterization for hierarchies of convex relaxations.

Conclusions:

Algorithmic weakening offers a principled approach to managing large-scale data analysis.
This framework enables concrete tradeoffs between computational cost and statistical accuracy.
Convex relaxation hierarchies can be statistically characterized for practical applications.