Gaurav Arya

Undergraduate at MIT

Talks:

20:30 UTC

StochasticAD.jl: Differentiating discrete randomness

07/26/2023, 8:30 PM9:00 PM UTC
32-D463 (Star)

Automatic differentiation (AD) is great: use gradients to optimize, sample faster, or just for fun! But what about coin flips? Agent-based models? Nope, these aren’t differentiable... or are they? StochasticAD.jl is an open-source research package for AD of stochastic programs, implementing AD algorithms for handling programs that can contain discrete randomness.

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