Nicola Branchini

🇮🇹 ProbAI Research Fellow @warwickstats.bsky.social. Previously @ellis.eu Stats PhD @edinunimaths.bsky.social @aalto.fi. 🤔💭 about Monte Carlo, approximate inference, UQ

Articles & links

↻ Nicola Branchini reposted
Sam Power @spmontecarlo.bsky.social

With friends at the University of Warwick (in particular, Rocco Caprio and @adriencorenflos.bsky.social), we've recently arXived some work (arxiv.org/abs/2605.30253) on a method for approximate inference known as "Coordinate Ascent Variational Inference", or "CAVI" for short. …

Wasserstein Contraction of Coordinate Ascent Variational Inference arxiv.org
AI Weekly's analysis →
  • The paper establishes Wasserstein contraction of coordinate ascent variational inference without assuming global strong log-concavity of the target.
  • The conditions are a functional smoothness of the optimality maps plus a transportation-information inequality at their fixed points.
  • Covered models include Ising and Curie-Weiss, Bayesian Gaussian mixtures, high-dimensional Bayesian probit regression, and Pólya-Gamma logistic regression.
Read full analysis →
View on Bluesky →
↻ Nicola Branchini reposted
Pierre Alquier @pierrealquier.bsky.social

Mehdi defends our recent preprint on rho-posteriors and their variational approximations at ISBA @Nagoya Link to the preprint: arxiv.org/abs/2601.07325

Robust Bayesian Inference via Variational Approximations of Generalized Rho-Posteriors arxiv.org
AI Weekly's analysis →
  • Khribch and Alquier introduce a rho-tilde-posterior that swaps the supremum over competitor parameters for a softmax aggregation.
  • The construction yields PAC-Bayesian finite-sample oracle inequalities with explicit convergence rates that survive model misspecification and data contamination.
  • Those guarantees extend to variational approximations, with computational cost the authors report as comparable to standard variational Bayes.
Read full analysis →
View on Bluesky →

In Nicola Branchini's orbit

Center = Nicola Branchini. Left = members they follow (green edges). Right = members who follow them (blue edges). Top = mutual follows (orange edges, slightly larger). Drag any node to reposition; click to open that profile.