Archive for INRIA

SEINE AI

Posted in pictures, Statistics, University life with tags , , , , , , , , , , , , , , , , , , , , , , on March 23, 2026 by xi'an

Ten days ago I took part in the SEINE AI 2026 workshop in Jouy-en-Josas, near Paris (homestead of HEC), organised by the Huawei Paris Research Center.. In which I was invited to speak, even though I felt sort of an outlier given the deeply machine-learning, entreprenarial orientation of the meeting, with its theme being Building the Agentic Future of ICT, given that I chose to present our most recent Bayesian adversarial privacy paper. Hence, I stood within a game-theoretic, Bayesian, formal landscape, presumably loosing most of the audience and keeping them away from their lunch!

Other speakers included Simon Lucas from Queen Mary London on Simulation-based AI, which I had trouble distinguishing from building a statistical model by goodness of fit (and using bandits used for update), while focussing on competing on some computer game challenges. And Volker Tresp from LMU München on a tensor brain model that he opposes to a Bayesian brain (with a related paper entitled Bayes or Heisenberg: Who(se) rules? which we discussed in general terms over lunch, namely Bayesian learning vs. quantum updating. And Michal Valko from INRIA Paris (and other companies), who went full blast against the Bradley-Terry model!, with a title of Nash and Nemirovski walk into a bar! With a half-time technique approximating Nash equilibria that reminded me of leapfrog. Much entertaining talk that further provided a game-theoretic transition to mine’s.

As an aside, I played yesterday with ChatGPT composing my talk slides out of our arXiv document and it proved a disaster, with hallucinations of results and concepts not in the paper and a complete mess of handling graphs, first creating generic, fake, unrelated pictures, then inserting actual graphs haphazardly throughout the slides. The sorry result I obviously did not use as the workshop did not seem the ideal place for this sort of prank! The actual version only recycles a few of its summarising slides. (With ye Norse farce proper colour choice!)

 

mostly Monte Carlo [13/03]

Posted in Statistics, Travel, University life with tags , , , , , , , , , , , , , , , , , , on March 10, 2026 by xi'an

A new episode of our mostly Monte Carlo seminar, very soon coming near you (if in Paris):

On Friday 13/02/26, from 3-5pm at PariSanté Campus

15h00: Pierre Del Moral (INRIA, Bordeaux)

On the Kantorovich contraction of Markov semigroup

We present a novel operator theoretic framework to study the contraction properties of Markov semigroups with respect to a general class of Kantorovich semi-distances, which notably includes Wasserstein distances. This rather simple contraction cost framework combines standard Lyapunov techniques with local contraction conditions. Our results can be applied to both discrete time and continuous time Markov semigroups, and we illustrate their wide applicability in the context of (i) Markov transitions on models with boundary states, including bounded domains with entrance boundaries, (ii) operator products of a Markov kernel and its adjoint, including two-block-type Gibbs samplers, (iii) iterated random functions and (iv) diffusion models, including overdampted Langevin diffusion with convex at infinity potentials.

16h00: Bob Carpenter (Flatiron Institute, New York)

GIST, WALNUTS, and Continuous Nutpie: mass-matrix and step-size adaptation for Hamiltonian Monte Carlo

I will introduce Gibbs self tuning (GIST), our new technique for coupling tuning parameters and conditionally Gibbs-sampling them per iteration in Hamiltonian Monte Carlo. Then I will turn to the within-orbit adaptive NUTS (WALNUTS) sampler, which adapts the step size every leapfrog step in order to conserve the Hamiltonian. Empirical evaluations on varying multi-scale target distributions, including Neal’s funnel and the Stock-Watson stochastic volatility time-series model, demonstrate that WALNUTS achieves substantial improvements in sampling efficiency and robustness. I will review the Nutpie mass-matrix adaptation scheme, which is designed to minimize Fisher divergence by estimating the mass matrix as the geometric midpoint (aka barycenter) between the inverse covariance of the draws and the covariance of the scores of the draws. Then I will describe a continuously adapting version that adapts per iteration by continuously discounting the past rather than updating in fixed blocks. I will also show how the Adam optimizer outperforms dual averaging for step-size adaptation. I will conclude by considering a lock-free multi-threading implementation that automatically monitors adaptation and sampling for convergence for automatic stopping.

Bayesian decision-theory for data privacy [surfin’ the Oce’n, 30 April, INRIA Paris]

Posted in Statistics, University life with tags , , , , , , , , , , , , , , , , , on April 23, 2025 by xi'an

Abstract

The scientific and economic value of data continues to grow alongside technology advances. New hardware and software developments enable, but often require, larger and more complex datasets to function effectively. As the importance of input data to these systems becomes increasingly recognized, so too does the loss of privacy for data providers. In this context, data privacy emerges as a critical issue for fields such as statistics and machine learning, as well as for scientific and industrial endeavours that rely on sensitive data. We propose a framework for measuring privacy from a Bayesian decision-theoretic perspective. This framework enables the creation of new, purpose-driven privacy principles that are rigorously justified, while also allowing for the assessment of existing privacy definitions through decision theory. We pay particular attention to the privacy of deterministic algorithms, which are overlooked by current privacy standards, and to the privacy of N Monte Carlo samples drawn from an invariant distribution as N goes to infinity. We show that Probabilistic Differential Privacy is a special case of our framework and provide some new interpretations for Differential Privacy as a result.

mostly MC’nuary [10 Jan 2025]

Posted in Books, Statistics, University life with tags , , , , , , , , , , , , , , , on January 6, 2025 by xi'an

Fondation Sciences mathématiques de Paris scholarships for math masters [2025-2026]

Posted in Kids, Statistics, Travel, University life with tags , , , , , , , , , , , , , on November 22, 2024 by xi'an