Location: EU Supported » PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning » NIPS Workshop on Efficient Machine Learning, Whistler 2007

Who is Afraid of Non-Convex Loss Functions?

author: Yann LeCun, New York University (NYU)
published: Dec. 29, 2007,   recorded: December 2007,   views: 46370
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Description

The NIPS community has suffered of an acute convexivitis epidemic:
- ML applications seem to have trouble moving beyond logistic regression, SVMs, and exponential-family graphical models;
- For a new ML model, convexity is viewed as a virtue;
- Convexity is sometimes a virtue;
- But it is often a limitation.
ML theory has essentially never moved beyond convex models - the same way control theory has not really moved beyond linear systems.

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Reviews and comments:

Comment1 Robert Hones, August 25, 2021 at 4:49 a.m.:

I am glad that that you discuss this ML theory which has essentially never moved beyond convex models - the same way control theory has not really moved beyond linear systems.

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Comment2 Hayley, September 10, 2021 at 5:24 p.m.:

I believe the limitations you have encountered never limited the possibilities that you did during this time.

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Comment3 Fath Abv, December 2, 2021 at 1:08 p.m.:

ML applications seem to have trouble moving beyond logistic regression, SVMs, and exponential-family graphical models. This is a great discussion.
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