As ICML 2023 is happening this week, in Hawai’i, many did not have the opportunity to get there, for whatever reason, and hence the ellis (European Lab for Learning {and} Intelligent Systems] board launched [fairly late!] with the help of Hi! Paris an unconference (i.e., a mirror) that is taking place in HEC, Jouy-en-Josas, SW of Paris, for AI researchers presenting works (theirs or others’) presented at ICML 2023. Or not. There was no direct broadcasting of talks as we had (had) in CIRM for ISBA 2020 2021. But some presentations based on preregistered talks. Over 50 people showed up in Jouy.
As it happened, I had quite an exciting bike ride to the HEC campus from home, under a steady rain, crossing a (modest) forest (de Verrières) I had never visited before, despite it being a few km from home, getting a wee bit lost, stopped by a train Xing between Bièvre and Jouy, and ending up at the campus just in time for the first talk (as I had not accounted for the huge altitude differential). Among curiosities met on the way, “giant” sequoias, a Tonkin pond, Chateaubriand’s house.
As always I am rather impressed by the efficiency of AI-ML conferences run, with papers+slides+reviews online, plus extra material as in this example. Lots of papers on diffusion models this year, apparently. (In conjunction with the trend observed at the Flatiron workshop last Fall.) Below are incoherent tidbits from the presentations I attended:
- exponential convergence of the Sinkhorn algorithm by Alain Durmus and co-authors, with the surprise occurrence of a left Haar measure
- a paper (by Jerome Baum, Heishiro Kanagawa, and my friend Arthur Gretton) on Stein discrepancy, with an Zanella Stein operator relating to Metropolis-Hastings/Barker since it has expectation zero under stationarity, interesting approach to variable length random variables, not a RJMCMC, but nearby.
- the occurance of a criticism of the EU GDPR that did not feel appropriate for synthetic data used in privacy protection.
- the alternative Sliced Wasserstein distance, making me wonder if we could optimally go from measure μ to measure ζ using random directions or how much was lost this way.
- Information Maximizing Optimal Transport with dubious substitute for conditional expectation:
![\mathbb E[y|X=x] = \mathbb E\left[y\frac{f_{XY}(x,y)}{f_X(x)f_Y(y)}|X=x\right] = \frac{\mathbb E\left[y\frac{f_{XY}(x,y)}{f_Y(y)}|X=x\right]}{f_X(x)}](https://s0.wp.com/latex.php?latex=%5Cmathbb+E%5By%7CX%3Dx%5D+%3D+%5Cmathbb+E%5Cleft%5By%5Cfrac%7Bf_%7BXY%7D%28x%2Cy%29%7D%7Bf_X%28x%29f_Y%28y%29%7D%7CX%3Dx%5Cright%5D+%3D+%5Cfrac%7B%5Cmathbb+E%5Cleft%5By%5Cfrac%7Bf_%7BXY%7D%28x%2Cy%29%7D%7Bf_Y%28y%29%7D%7CX%3Dx%5Cright%5D%7D%7Bf_X%28x%29%7D&bg=000000&fg=B0B0B0&s=0&c=20201002)
as (a) densities are replaced with kernel estimates, (b) the outer density may be very small, (c) no variance assessment is provided.
- Markov score climbing and transport score climbing using a normalising flow, for variational approximation, presented by Christian Naesseth, with a warping transform that sounded like inverting the flow (?)
- Yazid Janati not presenting their ICML paper State and parameter learning with PARIS particle Gibbs written with Gabriel Cardoso, Sylvain Le Corff, Eric Moulines and Jimmy Olsson, but another work with a diffusion based model to be learned by SMC and a clever call to Tweedie’s formula. (Maurice Kenneth Tweedie, not Richard Tweedie!) Which I just realised I have used many times when working on Bayesian shrinkage estimators
