In connection with the previous announcement of ABC in Montréal, a call for papers that came out today:
NIPS 2014 Workshop: ABC in Montreal
December 12, 2014
Montréal, Québec, Canada
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, etc. Recent work has both applied machine learning models and algorithms to general ABC inference (NN, forests, GPs) 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 collaboration between the two communities.
The workshop will consist of invited and contributed talks, poster spotlights, and a poster session. Rather than a panel discussion we will encourage open discussion between the speakers and the audience!
Examples of topics of interest in the workshop include (but are not limited to):
* Applications of ABC to machine learning, e.g., computer vision, inverse problems
* ABC in Systems Biology, Computational Science, etc
* ABC Reinforcement Learning
* Machine learning simulator models, e.g., NN models of simulation responses, GPs etc.
* Selection of sufficient statistics
* Online and post-hoc error
* ABC with very expensive simulations and acceleration methods (surrogate modeling, choice of design/simulation points)
* ABC with probabilistic programming
* Posterior evaluation of scientific problems/interaction with scientists
* Post-computational error assessment
* Impact on resulting ABC inference
* ABC for model selection
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