Stochastic Numerics and Statistical Learning

This week, I am taking part in the Stochastic Numerics and Statistical Learning workshop, in KAUST, which should prove an interesting experience!

Fusing Simulation with Data Science [ CRiSM workshop, 18-9/07/23]

hit by Brexit!

After realising while at ISBA²² that Probabilistic Numerics,  the book of Philipp Heinig, Michael Osborne, and Hans Kersting, had appeared, I requested a copy for review in CHANCE from Cambridge University Press, which they kindly sent me. However, I received it with a 21€ bill for the novel VAT tax the EU has just (re)established for goods imported from outside the EU. From now on, I will have review books delivered to my Warwick address or sent from within the EU! (I have attempted to complain about paying VAT on free goods, but customs were not at all sympathetic!!!)

day two at ISBA 22

Still woke up early too early, which let me go for a long run in Mont Royal (which felt almost immediately familiar from earlier runs at MCM 2017!) at dawn and at a pleasant temperature (but missed the top bagel bakery on the way back!). Skipped the morning plenary lectures to complete recommendation letters and finishing a paper submission. But had a terrific lunch with a good friend I had not seen in Covid-times, at a local branch of Kinton Ramen which I already enjoyed in Vancouver as my Airbnb was located on top of it.

I chaired the afternoon Bayesian computations session with Onur Teymur presenting the general spirit of his Neurips 21 paper on black box probabilistic numerics. Mentioning that a new textbook on the topic by Phillip Henning, Michael Osborne, and Hans Kersting had appeared today! The second talk was by Laura Bondi who discussed an ABC model choice approach to assess breast cancer screening. With enough missing data (out of 78051 women followed over 12 years) to lead to an intractable likelihood. Starting with vanilla ABC using 32 summaries and moving to our random forest approach. Unsurprisingly concluding with different top models, but not characterising the identifiability provided by the choice of the summaries. The third talk was by Ryan Chan (fresh Warwick PhD recipient), about a Fusion divide-and-conquer approach that avoids the approximation of earlier approaches. In particular he uses a clever accept-reject algorithm to generate a product of densities using the component densities. A nice trick that Murray explained to me while visiting in Paris lg ast month. (The approach appears to be parameterisation dependent.) The final talk was by Umberto Picchini and in a sort the synthetic likelihood mirror of Massi’s talk yesterday, in the sense of constructing a guided proposal relying on observed summaries. If not comparing both approaches on a given toy like the g-and-k distribution.

Savage Award session today at JSM

Pleased to broadcast the JSM session dedicated to the 2020 Savage Award, taking place today at 13:00 ET (17:00 GMT), with two of the Savage nominees being former OxWaSP students (and Warwick PhD students). For those who have not registered for JSM, the talks are also available on Bayeslab. (As it happens, I was also a member of the committee this year, but do not think this could be deemed a CoI!)

112	Mon, 8/3/2020, 1:00 PM – 2:50 PM		Virtual
Savage Award Session — Invited Papers
International Society for Bayesian Analysis (ISBA)
Organizer(s): Maria De Iorio, University College London
Chair(s): Maria De Iorio, University College London
1:05 PM	Bayesian Dynamic Modeling and Forecasting of Count Time Series Lindsay Berry, Berry Consultants
1:30 PM	Machine Learning Using Approximate Inference: Variational and Sequential Monte Carlo Methods Christian Andersson Naesseth, Columbia University
1:55 PM	Recent Advances in Bayesian Probabilistic Numerical Integration Francois-Xavier Briol, University College London
2:20 PM	Factor regression for dimensionality reduction and data integration techniques with applications to cancer data Alejandra Avalos Pacheco, Harvard Medical School
2:45 PM	Floor Discussion