arXiv:2605. 18931v2 Announce Type: replace-cross Abstract: Heavy-tailed distributions are prevalent in performance evaluation, network traffic, and risk modeling.
arXiv:2608. 11544v1 Announce Type: cross Abstract: We propose CVaR-penalized Generative Particle Algorithm (CVaR-GPA), a robust, tail-agnostic algorithm for fine-tuning generative models to learn heavy-tailed distributions and capture extreme events, requiring no prior knowledge or estimation of the target's tail characteristics.
arXiv:2606. 01002v1 Announce Type: cross Abstract: Engression is a recently proposed and effective framework for conditional distribution learning.
arXiv:2608. 15314v1 Announce Type: new Abstract: Ultra-reliable low-latency communication (URLLC) requires precise identification of spatial regions where the signal-to-noise ratio (SNR) falls below an outage threshold.
The paper introduces a conditional Wasserstein GAN to approximate posterior distributions in compound loss models, conditioning on sufficient statistics, prior mean, coefficient of variation, and mixture weights. A single generator can learn the posteriors for both Poisson intensity and Pareto shape parameters across Gamma, inverse‑Gaussian, and lognormal priors. The authors validate the approach with simulation‑based calibration, analytical comparisons, and extensive MCMC, and apply it to extreme natural catastrophe loss data to generate rolling one‑year posterior predictive distributions and assess tail risk under heavy‑tailed severity and prior uncertainty.
Ultra-reliable low-latency communication (URLLC) requires precise identification of spatial regions where the signal-to-noise ratio (SNR) falls below an outage threshold. In this context, an outage re...
arXiv:2606. 10450v1 Announce Type: cross Abstract: DiffC provides a principled way to reuse pre-trained diffusion models for lossy compression, but its encoding and decoding procedures remain slow because they require many discretized forward and reverse steps.
arXiv:2607. 19332v1 Announce Type: new Abstract: Generative models have undergone many generations of evolution, from VAEs/GANs to diffusion/flow matching.
arXiv:2410. 02596v2 Announce Type: replace-cross Abstract: Generative Flow Networks (GFlowNets) are a novel class of generative models designed to sample from unnormalized distributions and have found applications in various important tasks, attracting great research interest in their training algorithms.
The paper introduces Bridge Graphical Models (BGMs), a framework that decomposes continuous‑time generative models into independent design choices: endpoint coupling, bridge law, Markovian projection, and current‑preserving dynamics. It defines the Markovization gap as the time‑integrated conditional variance of bridge velocity given the Markov state, quantifying an irreducible loss before training. Experiments on synthetic, latent, and pixel‑space tasks (CIFAR‑10 and Fashion‑MNIST) show that a feature‑space proxy of this gap, estimated quickly before training, predicts downstream training loss and FID in the same direction as full training results.
arXiv:2608.22746v1 Announce Type: new Abstract: This paper studies the Sinkhorn distributionally robust hypothesis testing (SDRHT) problem, seeking a robust detector against least-favorable distribut...
The paper presents a theoretical framework for approximating ratio-type functionals that arise in conditional generative modeling, specifically when the target density is expressed as a ratio of two kernel-based marginal densities. It proves that deep neural networks using the SignReLU activation can approximate these ratios with established L^p(Omega) bounds and convergence rates under standard regularity assumptions. Applying the framework to Denoising Diffusion Probabilistic Models, the authors construct a SignReLU-based estimator for the reverse process and derive bounds on the excess Kullback–Leibler risk, decomposing it into approximation and estimation errors to provide generalization guarantees for finite-sample training.
arXiv:2608. 09385v1 Announce Type: cross Abstract: Generative AI models are primarily designed to imitate the data distribution, an objective that neither corrects diversity lost by a learned generator nor defines how generation should extend beyond the diversity of the data itself.