10 papers
Partial Differential Equation Barriers to Identifiability in Infinite Mixture Models
Dung Le, Nicola Bariletto, Alessandro Rinaldo +1
We study identifiability of mixing measures in infinite mixture models. We show that, in many common cases, lack of identifiability can be characterized in terms of certain differe…
On the geometry of weak convergence without total variation convergence
Nicola Bariletto, Stephen G. Walker
We study some geometric consequences of the discrepancy between weak and total variation convergence of probability measures. We consider a sequence of probability measures on $\ma…
Convergence Rates for Latent Mixing Measures in Infinite Homoscedastic Location-Scale Mixture Models
Nicola Bariletto, Dung Le, Alessandro Rinaldo +1
We study posterior contraction rates for mixing measures in homoscedastic location-scale mixture models with infinitely many components. While posterior convergence at the level of…
On Bayesian Softmax-Gated Mixture-of-Experts Models
Nicola Bariletto, Huy Nguyen, Nhat Ho +1
Mixture-of-experts models provide a flexible framework for learning complex probabilistic input-output relationships by combining multiple expert models through an input-dependent…
Scalable Posterior Uncertainty for Flexible Density-Based Clustering
Nicola Bariletto, Stephen G. Walker
We introduce a novel framework for uncertainty quantification in clustering that combines martingale posterior distributions with density-based clustering. Unlike classical model-b…
Conformalized Bayesian Inference, with Applications to Random Partition Models
Nicola Bariletto, Nhat Ho, Alessandro Rinaldo
Bayesian posterior distributions naturally represent parameter uncertainty informed by data. However, when the parameter space is complex, as in many nonparametric settings where i…