Approximate Bayesian computation (ABC) or likelihood-free (LF) methods have developed mostly beyond the radar of the machine learning community, but are important tools for a large segment of the scientific community. This is particularly true for systems and population biology, computational psychology, computational chemistry, computational finance, etc. Recent work has applied both machine learning models and algorithms to general ABC inference (e.g., NN, forests, GPs, LDA) and ABC inference to machine learning (e.g. using computer graphics to solve computer vision using ABC). In general, however, there is significant room for more intense collaboration between both communities. Submissions on the following topics are encouraged (but not limited to):

Examples of topics of interest in the workshop include (but are not limited to):
* Applications of ABC to machine learning, e.g., computer vision, other inverse problems (RL)…
* ABC Reinforcement Learning (other inverse problems)
* Machine learning models of simulations, e.g., NN models of simulation responses, GPs etc.
* Selection of sufficient statistics and massive dimension reduction methods
* Online and post-hoc error
* ABC with very expensive simulations and acceleration methods (surrogate modeling, choice of design/simulation points)
* Relation between ABC and probabilistic programming
* Posterior evaluation of scientific problems/interaction with scientists
* Post-computational error assessment
* Impact on resulting ABC inference