I have just return from the 29th Australasian Joint Conference on Artificial Intelligence, held at Hobart, Tasmania, Australia from the 5th through 9th of December. This conference is usually an interesting, open and friendly environment to discuss topics in applied machine learning, and this year was no different. There was quite a focus on “deep learning”, as would be expected given the current hype surrounding this neural network revival, but there was also a number of other interesting topics covered in the technical sessions.
I presented, or was involved with the presentation of, three papers: “Approximating Message Lengths of Hierarchical Bayesian Models Using Posterior Sampling”, “Bayesian Robust Regression with the Horseshoe+ Estimator” and “Bayesian Grouped Horseshoe Regression with Application to Additive Model”, which were all quite “horseshoe”-centric, given my current interest in global-local shrinkage models.
If you are an Australian — or even international — researcher with interests in applied machine learning and artificial intelligence, I recommend you give this conference a visit one time. Next year is particularly attractive as it co-incides with IJCAI and is being held in Melbourne.