In the recent years, there have been numerous advances in approximate inference methods, which have enabled Bayesian inference in increasingly challenging scenarios involving complex probabilistic models and large datasets. The 2nd Symposium on Advances in Approximate Bayesian Inference (AABI) will discuss this impact of Bayesian inference, connecting both variational and Monte Carlo methods with other fields. We encourage submissions that relate Bayesian inference to the fields of reinforcement learning, causal inference, decision processes, Bayesian compression, or differential privacy, among others. We also encourage submissions that contribute to connecting different approximate inference methods.
This symposium is a continuation of past years events:
For the call for papers and submission instructions, click here.