A Cookbook for Machine Learning: compiling ML goals into tractable optimization
goyal__pramod · x · 2026-10-16
The author shares the direct link to inFERENCe's 2017 post, which frames ML research as a compiler that turns abstract ML objectives into the canonical problem of finding stable attractors of a tractable vector field. Given an intractable objective, you recursively apply problem transformations until something efficiently optimizable remains. The first batch covers variational bounds, adversarial games, evolution strategies, and convex relaxation, with duality, half-quadratic splitting, and Lagrangian multipliers listed as future candidates.
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