arXiv:2605. 18931v2 Announce Type: replace-cross Abstract: Heavy-tailed distributions are prevalent in performance evaluation, network traffic, and risk modeling.
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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.