3 papers
math.ST2026
Strong log-concavity in probit regression
Martin Chak, Zoraida F. Rico, Giacomo Zanella
We show that strong log-concavity emerges in probit regression likelihoods without ridge penalization (i.e. Gaussian priors), unlike for the logistic case. Specifically, we provide…
stat.CO2025
Complexity of Markov Chain Monte Carlo for Generalized Linear Models
Martin Chak, Giacomo Zanella
Markov Chain Monte Carlo (MCMC), Laplace approximation (LA) and variational inference (VI) methods are popular approaches to Bayesian inference, each with trade-offs between comput…
stat.CO2024
On theoretical guarantees and a blessing of dimensionality for nonconvex sampling
Martin Chak
Guarantees for algorithms sampling from nonlogconcave target measures on are studied. For the class of measures with logdensities that have bounded Hessians and are…