By happenstance, I started my day in the cybersecurity session. With (again) hardly a soul in the room… A first talk on avoiding herding and achieving asymptotic truth learning (about a binary outcome) in a graph by putting constraints on the graph structure, without any clear connection with statistics or cybersecurity. Even less for the second talk on optimising masks. Only with the third one came cybersecurity motivations, the focus being on a two-player Stackelberg game already used in this framework. The result proper was about estimating the parameter of a (rather unrealistic) posited model reproducing the adversarial actions. The last talk about jailbreak attacks was again off-field by miles.
Then attended (as intended!) the 2024 Blackwell-Rosenbluth Award session, featuring the nominees Sharmistha Guha (Texas A&M) on multiple network inference, Simon Mak (Duke) on using Bayesian surrogate models, Guanyang Wang (Rutgers) who recently spoke at our mostly Monte Carlo seminar, Akihiko Nishimura (John Hopkins) on a unification of HMC and PDMPs, most appropriate when located next to Mount Hamilton and the Zigzag river!, and Maria Skoularidou (MIT) on evolutionary Monte Carlo for gene expression. Alas with hardly anyone in the room.
In the afternoon, I went to the (very well-attended this time!, with no seat available for many attendees, incl. yours truly!) COPSS Elizabeth L. Scott Lecture by my friend from Rutgers, Regina Liu, on the highly relevant challenge of combining inferences from diverse data sources. Using the (definitely Rutgerian!) approach of confidence distributions!
Last (late) afternoon, I went swimming from Kevin Duckworth dock, just below the conference centre. Water was quite warm (and green), with a few other swimmers, and no stomachical after-effect, so far. Hence I returned there once again this afternoon.
