I am also a user of this fantastic tool developed by Anton.

You can setup your independent variable as a matrix of independent variables.

Thad ]]>

Since I first encountered it, I’ve thought that Cover’s work with what he described informally as “natural optimization techniques”, could provide very efficient and useful optimization approaches for use in machine learning, AI, & AGI.

Cover’s universal optimization approaches grow out of the beginnings of information theory, especially John Kelly’s work at Bell Labs (see: https://www.princeton.edu/~wbialek/rome/refs/kelly_56.pdf).

Cover developed the theoretical optimization framework for identifying, at successive time steps, the mean rank-weighted “portfolio” of agents/algorithms from an infinite number of possible combinations of inputs.

Think of this as a multi-dimensional regular simplex with rank weightings as a hyper-cap. One can then find the mean rank-weighted “portfolio” of agents geometrically.

(Note: Statistical methods of doing this take lots of processing time and power. I can share Mathematica code for a geometric solution that does this).

Cover proved that successively following that mean rank-weighted “portfolio” (shifting the portfolio allocation at each time step) converges asymptotically to the best single “portfolio” of agents at any future time step with a probability of 1.

Optimization without Monte Carlo or neural nets.

No dependence on distribution of the data.

I don’t know of anyone that has incorporated Cover’s ideas into AI & AGI. Seems like a potentially fruitful path.

I’ve wondered, if human brains might optimize their responses to the world by some Cover-like method. It would seems to correspond closely with the wet-ware.

From the posts you’ve presented in this blog, I thought this might pique your interest.

Let me know.

Andreas

I am currently running some quantile regression using you fantastic package – many thanks and congratulations for creating this one.

However, I was wondering whether I correctly understood that the number of independent variables used in the quantile regression is fixed at 1 or whether there is some possibility to include more than one dependent variable.

Many thanks and all the best,

Jakob ]]>