arXiv:2606. 30489v1 Announce Type: cross Abstract: Normalizing Flows excel at modeling a single fixed density, yet many problems across the sciences, such as high energy physics, instead require modeling how that density deforms as a function of continuous parameters: the strength of a physical effect, a calibration constant, or a source of systematic uncertainty.
By Davide Valsecchi, Mauro Doneg\`a, Rainer Wallny
The paper introduces Normalizing Flow Maps (NFM), a new class of exactly invertible pushforward maps that extend normalizing flows and flow maps by learning the integrator of an underlying dynamical system while enabling efficient exact likelihood evaluation. NFMs incorporate two consistency axes—pointwise matching for β1-based regression losses and distributional matching for aligning pushforward distributions with a forward process—allowing scalable training. Experiments on CIFAR-10, ImageNet-64, and Boltzmann Generators demonstrate that NFMs achieve state‑of‑the‑art sample quality among parameter‑matched normalizing flows and outperform prior normalizing flows on peptide benchmarks.
By Francesco M. Ruscio, Timothy James Hitge, Fred Zhangzhi Peng, Danyal Rehman, Zander W. Blasingame, Nicholas M. Boffi, Michael M. Bronstein, Alexander Tong, Avishek Joey Bose
arXiv:2504. 01894v2 Announce Type: replace Abstract: We present a bifidelity method for uncertainty quantification of parameter estimates in complex systems, leveraging generative models trained to sample the target conditional distribution.
By Caroline Tatsuoka, Minglei Yang, Dongbin Xiu, Guannan Zhang
The paper introduces Transformed Samplers with Variance Reduction, a method that extends exact control variate solutions from simple reference densities to general target distributions by learning a bijective change of variables, such as a normalizing flow. By training the flow to map the target close to the reference in latent space, the authors show that Markov kernels and their Poisson solutions transform accordingly, enabling explicit control variates in the latent space. The resulting estimator is consistent under mild tail conditions, and the approach also applies to importance sampling, with experiments demonstrating competitive performance against state‑of‑the‑art samplers and control variates.
By Siran Liu, Michalis Tisias, Petros Dellaportas
The paper introduces NeVI‑Cut, a modular variational inference method for cut‑Bayes that does not require access to upstream data or models. It approximates the cut‑posterior by minimizing the expected downstream conditional Kullback‑Leibler divergence, using conditional normalizing flows as the variational family. The authors provide fixed‑data convergence rates, establish uniform KL approximation results for flow classes, and demonstrate the algorithm’s speed and accuracy on several applications.
By Jiafang Song, Sandipan Pramanik, Abhirup Datta
arXiv:2607. 03809v1 Announce Type: new Abstract: Normalising flows provide a powerful variational family for approximate inference, yet individual architectures often fail to generalise across heterogeneous posterior geometries.
By Benjamin Wiriyapong, Oktay Karakus, Can Eyupoglu, Kirill Sidorov