Robust Importance Sampling for Rare Events via Constrained Gaussian Mixtures
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
The Flow has not summarised this story yet — read it at arXiv Machine Learning.
arXiv:2609.24969v1 Announce Type: new Abstract: As agents are deployed with increased autonomy, even extremely rare events along their stochastic output trajectories can occur and prove catastrophic....
The paper introduces FAMIS, a flow-based multiple importance sampling framework that learns a nonuniform mixture of normalizing flow proposals for rare‑event estimation. It does not need presampled failure data or prior knowledge of failure modes, instead adapting the mixture through sequential evaluations of the limit state function. The method employs a smooth rare‑event surrogate, a tempered target sequence, defensive exploration, Rao‑Blackwellized weight updates, and a Jensen‑Shannon repulsion term to promote diversity, achieving accurate failure probability estimates with fewer training samples and stable variance reduction in complex reliability problems.
arXiv:2610.08652v1 Announce Type: cross Abstract: Diffusion models are increasingly used as surrogates for expensive simulators in weather prediction, molecular dynamics, and materials design. In the...
arXiv:2606. 29623v1 Announce Type: new Abstract: Rare events govern the safety profile of modern AI systems, yet their probabilities are extremely difficult to estimate: direct Monte Carlo requires prohibitive sample budgets.
arXiv:2610.19027v1 Announce Type: cross Abstract: Chance-constrained stochastic programs (CCSPs) require solutions that satisfy uncertain constraints with high probability. Standard approaches either...
arXiv:2603.12507v3 Announce Type: replace Abstract: We consider minimising a spectral risk objective, the expected cost plus a weighted conditional value-at-risk, when the distribution of the uncerta...