Implicit variational inference
Variational inference without densities
May 10, 2024 — March 30, 2024
approximation
metrics
optimization
probabilistic algorithms
probability
statistics
Variational inference using generative models whose density cannot be evaluated. See Variational Inference using Implicit Models.
Even though it does not evaluate likelihoods implicit VI still seems to use KL divergence as a loss function.
There seems to be a connection to adversarial learning too.
Related concepts
1 References
Che, Zhang, Sohl-Dickstein, et al. 2020. “Your GAN Is Secretly an Energy-Based Model and You Should Use Discriminator Driven Latent Sampling.” arXiv:2003.06060 [Cs, Stat].
Huszár. 2017. “Variational Inference Using Implicit Distributions.”
Karaletsos. 2016. “Adversarial Message Passing For Graphical Models.”
Mohamed, and Rezende. 2015. “Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning.” In Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 2. NIPS’15.
Tiao, Bonilla, and Ramos. 2018. “Cycle-Consistent Adversarial Learning as Approximate Bayesian Inference.”
Tran, Ranganath, and Blei. 2017. “Hierarchical Implicit Models and Likelihood-Free Variational Inference.” In Advances in Neural Information Processing Systems 30.
Uppal, Stensbo-Smidt, Boomsma, et al. 2023. “Implicit Variational Inference for High-Dimensional Posteriors.”