Github user mengxr commented on the pull request:
https://github.com/apache/spark/pull/582#issuecomment-42623415
LGTM. Thanks!
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Github user asfgit closed the pull request at:
https://github.com/apache/spark/pull/582
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Github user pwendell commented on the pull request:
https://github.com/apache/spark/pull/582#issuecomment-42623759
Thanks, merged.
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Github user mengxr commented on the pull request:
https://github.com/apache/spark/pull/582#issuecomment-42257123
@dbtsai Could you please update the `mllib-optimization.md` and include an
example of L-BFGS?
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Please remove itthere is no stochastic bfgswe will put an admm
wrapper over bfgs which has better optimization properties than sgd
On Apr 28, 2014 10:34 PM, "mengxr" wrote:
> Github user mengxr commented on the pull request:
>
> https://github.com/apache/spark/pull/582#issuecomme
Github user AmplabJenkins commented on the pull request:
https://github.com/apache/spark/pull/582#issuecomment-41753419
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Github user AmplabJenkins commented on the pull request:
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Github user AmplabJenkins commented on the pull request:
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Github user AmplabJenkins commented on the pull request:
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Github user dbtsai commented on the pull request:
https://github.com/apache/spark/pull/582#issuecomment-41751464
Make sense from the inverse of hessian point of view. Just remove it!
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Github user mengxr commented on the pull request:
https://github.com/apache/spark/pull/582#issuecomment-41749238
I prefer removing the miniBatchFraction. Those quasi-Newton methods
approximate the inverse of Hessian. It doesn't make sense if the gradients are
computed from a varying o
Github user dbtsai commented on the pull request:
https://github.com/apache/spark/pull/582#issuecomment-41740842
@mengxr Just did some hack on trying to implement the right "stochastic"
L-BFGS, and it kind of works as long as we don't change the objective function.
But there is no go
Github user mengxr commented on the pull request:
https://github.com/apache/spark/pull/582#issuecomment-41643015
I think it is good to remove `miniBatchFraction` from `LBFGS`'s params in
this PR, unless someone has a good understanding of the behavior of
"stochastic" L-BFGS.
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Github user AmplabJenkins commented on the pull request:
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GitHub user dbtsai opened a pull request:
https://github.com/apache/spark/pull/582
[SPARK-1157][MLlib] Bug fix: lossHistory should be monotonically decresing
Instead of recording the loss in the costFun for each time that optimizer
calls costFun, we get the loss from the api provide
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