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VISA: Variational Inference with Sequential Sample-Average Approximations
Heiko Zimmermann, Christian A. Naesseth, Jan-Willem van de Meent
We present variational inference with sequential sample-average approximation (VISA), a method for approximate inference in computationally intensive models, such as those based on…
A Variational Perspective on Generative Flow Networks
Heiko Zimmermann, Fredrik Lindsten, Jan-Willem van de Meent +1
Generative flow networks (GFNs) are a class of models for sequential sampling of composite objects, which approximate a target distribution that is defined in terms of an energy fu…
Nested Variational Inference
Heiko Zimmermann, Hao Wu, Babak Esmaeili +1
We develop nested variational inference (NVI), a family of methods that learn proposals for nested importance samplers by minimizing an forward or reverse KL divergence at each lev…
Learning Proposals for Probabilistic Programs with Inference Combinators
Sam Stites, Heiko Zimmermann, Hao Wu +2
We develop operators for construction of proposals in probabilistic programs, which we refer to as inference combinators. Inference combinators define a grammar over importance sam…
Amortized Population Gibbs Samplers with Neural Sufficient Statistics
Hao Wu, Heiko Zimmermann, Eli Sennesh +2
We develop amortized population Gibbs (APG) samplers, a class of scalable methods that frames structured variational inference as adaptive importance sampling. APG samplers constru…