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1. Variational Pictures

   https://doi.org/10.1007/978-3-319-91376-6_9  

   Erwig, M. ; Smeltzer, K. ( May 2018 , Int. Conf. on the Theory and Application of Diagrams)

   Diagrams and pictures are a powerful medium to communicate ideas, designs, and art. However, authors of pictures are forced to use rudimentary and ad hoc techniques in managing multiple variants of their creations, such as copying and renaming files or abusing layers in an advanced graphical editing tool. We propose a model of variational pictures as a basis for the design of editors and other tools for managing variation in pictures. This model enjoys a number of theoretical properties that support exploratory graphical design and can help systematize picture creators' workflows. 

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2. Provably Scalable Black-Box Variational Inference with Structured Variational Families

   Ko, Joohwan ; Kim, Kyurae ; Kim, Woo Chang ; Gardner, Jacob R ( July 2024 , International Conference on Machine Learning (ICML 2024))
3. Variational microcanonical estimator

   https://doi.org/10.1103/PhysRevResearch.5.033224  

   Pollock, Klée ; Orth, Peter P. ; Iadecola, Thomas ( September 2023 , Physical Review Research)
4. Nested Variational Inference

   Zimmermann, Heiko ; Wu, Hao ; Esmaeili, Babak ; van de Meent, Jan-Willem ( January 2021 , Advances in neural information processing systems)

   Ranzato, M. ; Beygelzimer, A. ; Dauphin, Y. ; Liang, P.S. ; Wortman Vaughan, J. (Ed.)

   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. NVI is applicable to many commonly-used importance sampling strategies and provides a mechanism for learning intermediate densities, which can serve as heuristics to guide the sampler. Our experiments apply NVI to (a) sample from a multimodal distribution using a learned annealing path (b) learn heuristics that approximate the likelihood of future observations in a hidden Markov model and (c) to perform amortized inference in hierarchical deep generative models. We observe that optimizing nested objectives leads to improved sample quality in terms of log average weight and effective sample size. 

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5. Variational Rejection Sampling

   Grover, Aditya ; Gummadi, Ramki ; Lazaro-Gredilla, Miguel ; Schuurmans, Dale ; Ermon, Stefano ( April 2018 , Proc. 21st International Conference on Artificial Intelligence and Statistics)