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Renaud Lustrat

2 accepted papers

2026

Fisher-Rao Sensitivity for Out-of-Distribution Detection in Deep Neural Networks

ICLR 2026poster

Deep neural networks often remain overconfident on Out-of-Distribution (OoD) inputs. We revisit this problem through Riemannian information geometry. We model the network's predictions as a statistical manifold and find that OoD inputs exhibit higher local Fisher-Rao sensitivity. By quantifying this…

Cited by 0SourceScholar
2025

Stochastic Embeddings : A Probabilistic and Geometric Analysis of Out-of-Distribution Behavior

UAI 2025

Deep neural networks perform well in many applications but often fail when exposed to out-of-distribution (OoD) inputs. We identify a geometric phenomenon in the embedding space: in-distribution (ID) data show higher variance than OoD data under stochastic perturbations. Using high-dimensional geome

Cited by 0SourcePDFScholar