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ABC in Montreal
Max Welling · Neil D Lawrence · Richard D Wilkinson · Ted Meeds · Christian X Robert

Fri Dec 12 05:30 AM -- 03:30 PM (PST) @ Level 5; room 512 a,e
Event URL: https://sites.google.com/site/abcinmontreal »

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

Author Information

Max Welling (University of Amsterdam / Qualcomm AI Research)
Neil D Lawrence (Amazon)
Richard D Wilkinson ( University of Sheffield)

I am Professor of Statistics at the University of Sheffield. I graduated with a BA, MMath and PhD in Mathematics from the University of Cambridge in 2008. My research is primarily in the field of uncertainty quantification - particularly on how to do parameter estimation for complex computer models. My main technical interests are on approximate Bayesian Computation (ABC) and Gaussian processes (GP). My current research goal is to develop GP models that include mechanistic/physical elements, in order to develop machine learning methods that encode scientific knowledge.

Ted Meeds (University of Amsterdam)
Christian X Robert (Université Paris-Dauphine)

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