no code implementations • 14 Mar 2024 • 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 numerical simulations.
no code implementations • NeurIPS 2023 • Babak Esmaeili, Robin Walters, Heiko Zimmermann, Jan-Willem van de Meent
Incorporating geometric inductive biases into models can aid interpretability and generalization, but encoding to a specific geometric structure can be challenging due to the imposed topological constraints.
no code implementations • 14 Oct 2022 • Heiko Zimmermann, Fredrik Lindsten, Jan-Willem van de Meent, Christian A. Naesseth
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 function or a reward.
no code implementations • NeurIPS 2021 • Heiko Zimmermann, Hao Wu, Babak Esmaeili, Jan-Willem van de Meent
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 level of nesting.
1 code implementation • 1 Mar 2021 • Sam Stites, Heiko Zimmermann, Hao Wu, Eli Sennesh, Jan-Willem van de Meent
Proposals in these samplers can be parameterized using neural networks, which in turn can be trained by optimizing variational objectives.
1 code implementation • ICML 2020 • Hao Wu, Heiko Zimmermann, Eli Sennesh, Tuan Anh Le, Jan-Willem van de Meent
We develop amortized population Gibbs (APG) samplers, a class of scalable methods that frames structured variational inference as adaptive importance sampling.