Optimality of Sub-network Laplace Approximations: New Results and Methods
Although the Laplace approximation offers a simple route to uncertainty quantification in deep neural networks, its reliance on inverting large Hessian matrices has motivated a range of computationally feasible low-dimensional or sparse approximations. A prominent class of such methods - sub-network Laplace approximations, constructs surrogates by restricting attention to a small subset of parameters. Existing approaches in this family typically rely on diagonal, layer-wise, or other architectural heuristics for subset selection, which ignore cross-parameter interactions and lack formal optimality guarantees. In this paper, we provide a rigorous theoretical analysis of the sub-network Lap...
| Authors | Swarnali Raha, Kshitij Khare, Rohit K Patra |
|---|---|
| Primary category | stat.ML |
| Published | 9 May 2026 |
| Last updated at source | 29 July 2026 |
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