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Machine Learning Surrogates

Fast learned approximations to expensive pricing maps.

Reviewed 2026-08-10TheQuantBateman ResearchReading note
01Intuition

Build the mental model first.

Pay a training cost once, then approximate repeated model evaluations very quickly inside a controlled domain.

ONE-LINE DEFINITION

Fast learned approximations to expensive pricing maps.

02Mathematics

Now make it exact.

V^ϕ(x)Vmodel(x)\hat{V}_\phi(x) \approx V_{model}(x)
Notation and units

Decimal rates and volatilities, year-fraction time and continuous compounding unless stated otherwise.

03Assumptions

Every model has a price.

01

Educational conventions are stated explicitly and may simplify market quotation or settlement details.

02

Rates are continuously compounded unless the section says otherwise.

03

Inputs are deterministic in the base model.

“An unstated convention is a future reconciliation break.”— THEQUANTBATEMAN
04Market use

Why a quant cares.

Active deployment area, but error control and extrapolation governance are essential.

IntuitionMathematicsImplementationDesk risk
05Desk view
FRONT OFFICE VIEW

The hedge has opinions.

Start with the quote convention, then ask which Frontier risk survives the hedge. A number without its convention is merely well-dressed ambiguity.

Ask Bateman about this model
06Related

Continue through the graph.