Today was the final day of Machine Learning Tools for Research in Astronomy. I gave my talk, which was about causal structure. One thing I talked about is the strong differences (stronger than you might think) between generative and discriminative directions for machine learning. Another thing I talked about is the way that machine-learning methods can be used to denoise, deconvolve, and separate signals when they are designed with good causal structure.
Right after me, Timmy Gebhard (MPI-IS and ETH) gave an absolutely excellent talk about half-sibling regression ideas related to instrument calibration (think Kepler, TESS, and direct imaging). He beautifully explained exoplanet direct imaging and showed how his improvements to how they are using the data change the results. He doesn't have the killer app yet, but he is spending the time to think about the problem deeply. And if he switched from one-band direct imaging to imaging spectroscopy (which is the future!) I think his methods will kill other methods. He also spoke about the causal-inference philosophy behind his methods really well.
My talk slides are here and I also led the meeting summary discussion. My summary slides are here. The summary discussion was valuable. In general, the size and style of the meeting—and location in lovely Ringberg Castle—led to a great environment and culture at the meeting. Plus with great social engineering by Ntampaka, Nord, Pillepich, and Peek. The latter made progress on a community-driven set of Ringberg Recommendations, which might end up as a long-term outcome of the meeting.