Omiros Papaspiliopoulos (Università Bocconi): Bilinear mixed models and variational inference
Adrien Corenflos (University of Warwick): A coupling-based approach to f-divergences diagnostics for Markov chain Monte Carlo [recently discussed on the ‘Og]
Our local Mostly (and monthly) Monte Carlo seminar is back for a new academic year, now organized by Antoine Luciano and Timothy Johnston. The first session will take place at the PariSanté Campus on Friday 17 October 2025 (3:00pm, room 07), with the organisers opening the dance, with two talks:
3pm Timothy Johnston (CEREMADE, Université Paris Dauphine–PSL): Differential Privacy of Markov Chains
Joint work with Andrea Bertazzi, Alain Durmus and Gareth Roberts
In this talk we shall discuss differential privacy, a framework for quantifying the extent to which a random output depends on the information used to produce it. After introducing several related definition of differential privacy, we shall discuss techniques used to show the differential privacy of both trajectories and single draws from Markov Chains. In doing so we shall touch on a perturbation technique which allows for Wasserstein type bounds to be converted into stronger distances like the KL and Renyi divergence.
4pm Antoine Luciano (CEREMADE, Université Paris Dauphine–PSL): Permutations accelerate Approximate Bayesian Computation
Joint work with Charly Andral, Christian P. Robert and Robin J. Ryder
Approximate Bayesian Computation (ABC) methods have become essential tools for performing inference when likelihood functions are intractable or computationally prohibitive. However, their scalability remains a major challenge in hierarchical or high-dimensional models. In this paper, we introduce permABC, a new ABC framework designed for settings with both global and local parameters, where observations are grouped into exchangeable compartments. Building upon the Sequential Monte Carlo ABC (ABC-SMC) framework, permABC exploits the exchangeability of compartments through permutation-based matching, significantly improving computational efficiency. We then develop two further, complementary sequential strategies: Over Sampling, which facilitates early-stage acceptance by temporarily increasing the number of simulated compartments, and Under Matching, which relaxes the acceptance condition by matching only subsets of the data. These techniques allow for robust and scalable inference even in high-dimensional regimes. Through synthetic and real-world experiments – including a hierarchical Susceptible-Infectious-Recover model of the early COVID-19 epidemic across 94 French departments – we demonstrate the practical gains in accuracy and efficiency achieved by our approach.
Last week, the ERC announced the winners of the Starting Grant campaign for 2025. Congrats to all recipients!
I am no longer a member of the PE1 (maths & data sciences) panel, hence did not contribute in the least to this selection. I noticed very few statistics and data science in the list (albeit obviously not hinting at any correlation with the previous sentence!), except for a project on computational stats. There is neither no one from my departments, even tough Warwick received one grant, nor from my research circles. The re-emergence of the UK as a strong proponent among the nations submitting projects is clear, while Italian candidates remain highly successful (incl. Bocconi!).
Since the EU States did not manage to unanimously agree on suspending the Article 2 of the EU-Israel Association Agreement last summer, despite Israel’s breach of human rights on the Gaza strip, there are also ten Israeli projects supported by EU on this round. (Unless the latest announcement of further sanctions by President Ursula von der Leyen suspending bilateral support to Israel does cover this financing.)
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