It hasn't disappointed thus far!

@sharky6000.bsky.social this may be of interest!

I was following this one during the COVID pandemic, but it has been inactive for quite some time. The original talks' recordings are amazing, though!

Yeah, it's been a period for all of us simultaneously! I have also been pretty busy with thesis/job search. Hopefully, it will be back running in the Fall term!

In the afternoon we will be giving a tutorial on general evaluation of AI agents.

sites.google.com/view/aamas20... 10/N

Congrats, Seth!

The quality of London's museums is just amazing! Enjoy!

If the AAMAS website is a good reference for this, it may not be, but uncertain atm.

Re #2: The key finding there is that the stationary points of SCO contain the margin matrix but, as I said in the note, there is still more work to do!

Thanks! I have been meaning to update the manuscript to standalone without the main paper but instead I may have change the content to a different format 😉. Coming soon!

Ah, I see the confusion... I never used the "identically distributed assumption," only the independence assumption (from 8 to 9).

I'm not sure if I understood your question correctly, but yes? As the post you shared says, "Voila! We have shown that minimizing the KL divergence amounts to finding the maximum likelihood estimate of θ." Maybe I am missing your point 😬

I had the convexity results for the online pairwise update (Section B.1.1.1) in my notes (manfreddiaz.github.io/assets/pdf/s...), but it is not clear to me if they hold for the other non-online settings. Worth taking a more detailed pass over the paper!

That's a nice finding, @sacha2.bsky.social! @sharky6000.bsky.social I skimmed over it, and it seems neat! There is an important distinction, though. They work with the "online" Elo regime, departing from the traditional gradient/batch gradient descent updates. (e.g., FIDE doesn't use online updates)

lol 😀

Not that Michael Jordan, but this one en.wikipedia.org/wiki/Michael...