To my knowledge, all he has ever said on the subject is: "I think people would be quite surprised if they knew how simple our methods are". You probably won't ever hear more information than that, until their strategies stop working.
Likewise, search Google Scholar for "@rentec.com", or "Renaissance Technologies."
Many optimization problems that arise in machine learning can be viewed as minimizing a particular Bregman distance subject to affine constraints (I'll call these "Bregman distance optimization problems").
In [1], the authors develop some quite general and widely applicable convex analysis type results for Bregman distances and use those results to give the technique of "Auxiliary Functions" which can be used to derive and prove the convergence of algorithms for particular Bregman distance optimization problems.
In [2], the authors show that finding the optimal parameters of two quite different approaches of binary classification, AdaBoost and Logistic Regression, can both be simultaneously viewed as the same Bregman distance optimization problem (with slightly different initial parameters). They then present several algorithms for solving this unified problem (and thus, for optimizing both AdaBoost and Logistic Regression), and prove the convergence of their algorithms with the method of Auxiliary functions developed in [1]. This paper was the first general proof of convergence for AdaBoost which was proposed by Yoav and Schapire (one of the authors of [2]) and earned them the Gödel prize in 2003.
If you don't mind me plugging in some expository work of mine, I wrote an essay ([3]) giving an introduction to Bregman distances and is pitched at a lower level than [1] and [2]. The end of the first chapter discusses in particular the general Bregman distance optimization problem, setting it up in the same framework as [1] and [2], and the final chapter presents the algorithm and proof of convergence originally given in [2] but hopefully with a slightly simplified presentation due to focusing only on the Logistic Regression case of the algorithm.
In case you want to play around with it, I have implemented the algorithm from [2], as well as a related algorithm from [4] which incorporates L1 (Ivanov) regularization into that algorithm, both being available at [5]. The middle chapters contain brief discussions on the relation of Bregman distances with Exponential Families, and on Generalized Linear Models, which are relevant to the overall purpose of the essay but not so closely related to the content of [1] and [2] and may safely be skipped.
If you generally enjoy the types of problems discussed in [1] and [2], you might also enjoy some of these papers: [6] [7] [8] [9].
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[1] - "Duality and Auxiliary Functions for Bregman Distances". Stephen Della Pietra, Vincent Della Pietra, and John Lafferty, 2001.
[2] - "Logistic Regression, AdaBoost and Bregman Distances". Robert Schapire, Michael Collins, Yoram Singer, 2001.
[3] - "Optimisation of Bregman Divergences". Ragib Zaman, 2018. https://github.com/RagibZaman/mathematical-optimisation/blob...
[4] - "Bregman distance to L1 regularized logistic regression". T. Huang and M. Gupta, International Conference on Pattern Recognition, 2008.
[5] - Notebook containing implementations of Bregman divergence based algorithms for Logistic Regression from [2] and [4]. https://github.com/RagibZaman/mathematical-optimisation/tree...
[6] - "Legendre functions and the method of random Bregman projections." H. Bauschke and J. Borwein, Journal of Convex Analysis, 1997.
[7] - "Inducing features of random fields". Stephen Della Pietra, Vincent Della Pietra, and John Lafferty, 1997.
[8] - "Statistical learning algorithms based on Bregman distances". Stephen Della Pietra, Vincent Della Pietra, and John Lafferty, 1997.
[9] - " A maximum entropy approach to natural language processing". Adam L. Berger, Stephen Della Pietra and Vincent Della Pietra. Computational Linguistics, 1996.
In a world where all their statistical arbitrage has ceased to be viable, financial professionals believe that the outsize returns of the Medallion fund in recent history are siphoned from their institutional funds via shell games.
Then, their famous ability to make money even in down markets is attributed to how they smooth out an extremely profitable short term trade, that may have occurred years ago, over the course of many years. This sort of surfaced in their tax avoidance lawsuit too.
You too can make a "Medallion Fund." First, make a venture investment in something that turns out to be Facebook. Keep that equity secret, even when Facebook goes public. All that time, say that your fund has gained 15% year over year, even during a recession. Do this for years until you have taken your 2% management fee to your liking. You've turned your 200x return that happened all at once into something that looks like the world's greatest hedge fund. Lawfully of course.
1) Those tax advantages can only improve returns which are already fundamentally strong, and
2) There is no "smoothing" effect achieved; the options baskets do not defer returns for years at a time.
I get that the cynical take is, as ever, the attractive one on Hacker News. But speaking frankly, what you're saying doesn't actually make sense. Among other problems with your explanation, there's a straightforward wrinkle. While it's not available to the general public, other institutions like Bloomberg and WSJ have had (and still have) access to audited attestations of Medallion's track record over a timespan of 25 years.