2019-12-09

#MLringberg2019, day 1

Today was the first day of Machine Learning Tools for Research in Astronomy in Ringberg Castle in Germany. The meeting is supposed to bring together astronomers working with new methods and also applied methodologists to make some progress. There will be a different mix of scientific presentations, participant-organized discussions, and unstructured time. Highly biased, completely subjective highlights from today included the following:

Michelle Ntampaka (Harvard) showed some nice results on using machine-learning discriminative regressions to improve cosmological inferences (the first of a few talks we will have this week along these lines). She emphasized challenges and lessons learned, which was useful to the audience. Among these, she emphasized the value she found in visualizing the weights of her networks. And she gave us some sense of her struggles with learning rate schedule, which I think is probably the bane of almost every machine learner!

Tom Charnock (Paris) made an impassioned argument that the outputs of neural networks are hard to trust if you haven't propagated the uncertainties associated with the finite information you have been provided about the weights in training. That is, the weights are a point estimate, and they are used to make a point estimate. Doubly bad! He argued that variational and Bayesian generalizations of neural networks do not currently meet the criteria of full error propagation. He showed some work that does meet it, but for very small networks, where Hamiltonian Monte Carlo has a shot of sampling. His talk generated some controversy in the room, which was excellent!

Morgan Fouesneau (MPIA) showed how the ESA Gaia project is using ideas in machine learning to speed computation. Even at one minute per object, they heat up a lot of metal for a long time! He showed that when you use your data to learn a density in the data space for different classes, you can make inferences that mitigate or adjust for class-imbalance biases. That's important, and it relates to what Bovy and I did with quasar target selection for SDSS-III.

Wolfgang Kerzendorf (Michigan State) spoke about his TARDIS code, which uses machine learning to emulate a physical model and speed it up. But he's doing proper Bayes under the hood. One thing he mentioned in his talk is the “expanding photosphere method” to get supernova distances. That's a great idea; whatever happened to that?

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