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Provable convergence guarantees for black-box variational inference
Justin Domke · Robert Gower · Guillaume Garrigos

Thu Dec 14 08:45 AM -- 10:45 AM (PST) @ Great Hall & Hall B1+B2 #1300

Black-box variational inference is widely used in situations where there is no proof that its stochastic optimization succeeds. We suggest this is due to a theoretical gap in existing stochastic optimization proofs—namely the challenge of gradient estimators with unusual noise bounds, and a composite non-smooth objective. For dense Gaussian variational families, we observe that existing gradient estimators based on reparameterization satisfy a quadratic noise bound and give novel convergence guarantees for proximal and projected stochastic gradient descent using this bound. This provides rigorous guarantees that methods similar to those used in practice converge on realistic inference problems.

Author Information

Justin Domke (University of Massachusetts, Amherst)
Robert Gower (Flatiron Institute)
Guillaume Garrigos (Université Paris Cité)

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