Author

Alessandro Rinaldo

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#machine learning Preprint Oct 2026

Sparsity Saves Compute, Not Samples: Oracle Inequalities for Dense and Sparse Mixture of Experts

Mixture-of-Experts (MoE) models combine specialized experts through an input-dependent routing mechanism, and their sparse variants underpin many modern large-scale systems. Yet the statistical cost of adaptive routing remains only partially understood. We study the estimation error of least-squares MoE estimators when...

Sagar Ghosh, Huy Nguyen, Alessandro Rinaldo · 0 citations

Preprint Aug 2026

Partial Differential Equation Barriers to Identifiability in Infinite Mixture Models

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 differential structures of the kernel family with respect to its parameters. In our main results, we prove that when the kernel is annihilated by a...

Dung Le, Nicola Bariletto, Alessandro Rinaldo et al. · 1 citation