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We propose a new least-squares Monte Carlo algorithm for the approximation ofconditional expectations in the presence of stochastic derivative weights. Thealgorithm can serve as a building block for solving dynamic programmingequations, which arise, e.g., in non-linear option pricing problems or inprobabilistic discretization schemes for fully non-linear parabolic partialdifferential equations. Our algorithm can be generically applied when theunderlying dynamics stem from an Euler approximation to a stochasticdifferential equation. A built-in variance reduction ensures that theconvergence in the number of samples to the true regression function takesplace at an arbitrarily fast polynomial rate, if the problem underconsideration is smooth enough.

`Regression Anytime' with Brute-Force SVD Truncation