Spearmint VS higgs-logistic-regression

It occurs to me that a system, trained on peer-reviewed applied-machine-learning literature and Kaggle winners, that generates candidates for structured feature-engineering specifications, based on plaintext descriptions of columns' real-world meaning, should be considered a requisite part of the "meta" here.

Ah, and then you could iterate within the resulting feature-engineering-suggestion space as a hyper-parameter between experiments, which could be optimized with e.g. https://github.com/HIPS/Spearmint . The papers write themselves!

This was some time ago but I had some promising results with Bayesian optimization using a Gaussian Process prior. The method was developed by the guys who wrote Spearmint. That library doesn't support parallelization but I implemented the same technique in Scala without too much difficulty.

Oh, you touched my favorite topic of whole dataset training.

Take a look at [1] and go straight to the page 8, figure 2(b).

[1] http://proceedings.mlr.press/v48/taylor16.pdf

The paper talks about whole dataset training and one of the datasets used is HIGGS [2]. The figure 2(b) shows two whole dataset training approaches (L-BFGS and ADMM) vs SGD. SGD tops at the accuracy with which both whole dataset approaches start, basically.

[2] https://archive.ics.uci.edu/ml/datasets/HIGGS#

HIGGS is strange dataset. It is narrow, having only 29 features. It is also relatively long, about 11M samples (10M to train, 0.5M to validate and last 0.5M to test). It is also hard to get right with SGD.

But if you perform whole dataset optimization, even linear regression can get you good accuracy [3] (some experiments of mine).

[3] https://github.com/thesz/higgs-logistic-regression

I beg to disagree.

[1] provides one with a whole-data-set training method (ADMM, one of such methods). Page 8 contains figure 2(b) - accuracy of training after specified amount of time. Note that ADMM start where stochastic gradient stops.

[1] https://arxiv.org/pdf/1605.02026.pdf

At [2] I tried to apply logistic regression trained using reweighted least squares algorithm on the same Higgs boson data set. I've got the same accuracy (64%) as mentioned in the ADMM paper with much less number of coefficients - basically, just the size of input vector + 1 instead of 300 such rows of coefficients and then 300x1 affine transformation. When I added squares of inputs (for the simplest approximation of polynomial regression) and used the same reweighted iterative least squares algorithm, I've got even better accuracy (66%) for double the number of coefficients.

[2] https://github.com/thesz/higgs-logistic-regression

There's a hypothesis [3] that SGD and ADAM are best optimizers because that everyone use and report on. Rarely if ever you get anything that differ.

[3] https://parameterfree.com/2020/12/06/neural-network-maybe-ev...

So, answering your question of "how do you know" - researchers at Google cannot do IRLS (search provides IRLS only for logistic regression in Tensorflow), they cannot do Hessian-free optimization ([4], closed due lack of activity - notice the "we can't support RNN due to the WHILE loop" bonanza), etc. All due to the fact they have to use Tensorflow - it just does not support these things.

https://github.com/tensorflow/tensorflow/issues/2682

I haven't seen anything about whole-data-set optimization from Google at all. That's why I (and only me - due to standing I take and experiments I did) conclude that they do not quite care about parameter efficiency.