SNLLS
Stochastic Nonlinear Least-Squares (for Large-Scale Machine Learning)
PYTHON 3.7 implementations of algorithms from the article
"Nonlinear Least-Squares for Large-Scale Machine Learning using Stochastic Jacobian Estimates", J.J. Brust (2021),
Proceedings of the 38th International Conference on Machine Learning, PMLR 139 [article]
Content:
-
NLLS.py (Full Jacobian nonlinear least-squares algorithm (small data sizes))
-
SNLLS1.py (Rank-1 Stochastic Jacobian algorithm, large-scale)
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SNLLSL.py (Rank-L Stochastic Jacobian algorithm, large-scale)
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SNLSS_1_IRISCLASS.py (Driver Experiment I)
- Dataset: [Iris]
- Includes: NLLS,SNLLS1,SNLLSL, SGD, Adam, Adagrad
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SNLLS_2_RANKING.py (Driver Experiment II)
- Dataset: [MovieLens]
- Includes: SNLLS1,SNLLSL, SGD, Adam, Adagrad
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SNLLS_3_AUTOENCODE.py (Driver Experiment III)
- Dataset: [Fashion MNIST]
- Includes: SNLLS1,SNLLSL, SGD, Adam, Adagrad
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README.txt
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DATA/ (stored experiment outcomes)
Example
You can run a driver using a python console:
In [2]: wd = os.getcwd()
In [2]: rundir = wd+'/'+'SNLLS_1_IRISCLASS.py'
In [3]: runfile(rundir, wdir=wd)
Size variables: 193, Size data: 96
Run:0
Epoch NLLS SNLLS1 SNLLSL SGD ADAM ADAGRAD
000 0.4108 0.3068 0.2880 0.4107 0.4192 0.4046
001 0.3805 0.1365 0.1647 0.3585 0.4117 0.4358
002 0.3476 0.1314 0.1177 0.1785 0.3930 0.4288
003 0.3244 0.0897 0.0931 0.1576 0.3786 0.4340
004 0.2907 0.0871 0.0815 0.1482 0.3520 0.4323
005 0.2622 0.0828 0.0736 0.1324 0.3178 0.4306
006 0.2350 0.0645 0.0612 0.2184 0.2849 0.4306
007 0.2119 0.0566 0.0547 0.1373 0.2531 0.4323
008 0.1903 0.0521 0.0505 0.1312 0.2369 0.4323
009 0.1726 0.0710 0.0481 0.1186 0.2254 0.4340
010 0.1568 0.0526 0.0481 0.1278 0.2316 0.4392
011 0.1399 0.0538 0.0416 0.1109 0.2273 0.4358
012 0.1258 0.0432 0.0380 0.1192 0.2230 0.4323
013 0.1129 0.0486 0.0397 0.1444 0.2240 0.4306
. . . . . . .
. . . . . . .
Cite
You can cite this work as (bibtex)
@inproceedings{snlls21,
author = {Johannes J. Brust},
title = {Nonlinear Least-Squares for Large-Scale Machine Learning using Stochastic Jacobian Estimates},
booktitle = {Proceedings of the Beyond first-order methods in ML systems
workshop at the 38th International Conference on Machine
Learning, {ICML} 2021, 18-24 July 2021, Virtual Event},
publisher = {{PMLR}},
year = {2021},
url = {https://sites.google.com/view/optml-icml2021/accepted-papers?authuser=0}
}
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