Turing-Universal Learners with Optimal Scaling Laws
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For a given distribution, learning algorithm, and performance metric, the rate of convergence (or data-scaling law) is the asymptotic behavior of the algorithm's test performance as a function of number of train samples. Many learning methods in both theory and practice have power-law rates, i.e. performance scales as n^-α for some α > 0. Moreover, both theoreticians and practitioners are concerned with improving the rates of their learning algorithms under settings of interest. We observe the existence of a "universal learner", which achieves the best possible distribution-dependent asymptotic rate among all learning algorithms within a specified runtime (e.g. O(n^2)), while incurring only polylogarithmic slowdown over this runtime. This algorithm is uniform, and does not depend on the distribution, and yet achieves best-possible rates for all distributions. The construction itself is a simple extension of Levin's universal search (Levin, 1973). And much like universal search, the universal learner is not at all practical, and is primarily of theoretical and philosophical interest.
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