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A free 57-minute resource by MIT's Applied Math team covers matrix calculations and automatic differentiation for quants and optimization, highlighting Jane Street's high compensation for such skills.
This paper presents acopula, a JAX-native framework for nested Archimedean copula inference that handles arbitrary censoring, nesting trees, and exact parameter gradients using Taylor-mode automatic differentiation, achieving significant speedups over existing methods.
This paper presents methods for differentiable parameter optimization of differential-algebraic equations (DAEs) with state-dependent events, comparing automatic differentiation through simulation with explicit discrete-adjoint methods.