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The paper evaluates conditional generative surrogates used in high‑energy physics, noting that common 1D marginal divergence metrics can mistakenly reward models that ignore conditioning information. Using the Continuous Ranked Probability Skill Score (CRPSS) as a more robust metric, the authors demonstrate that a simple sampler can outperform a trained Mixture Density Network on all target marginals, and that the advantage diminishes when CRPSS is evaluated within bins of the conditioning variable. They further introduce a data‑driven k‑Nearest‑Neighbor extrapolation to estimate the maximum achievable CRPSS given the conditioning information, finding that both models reach this estimate, suggesting current architectures have extracted the available conditioning information for the evaluated targets.
Conditional generative models have emerged as powerful tools for sampling from target conditional distributions, driving substantial advances across a wide range of scientific and applied domains. As these models proliferate, practitioners often face multiple plausible generators whose performance can vary with the task, data, or input condition.
arXiv:2609.23789v1 Announce Type: new Abstract: Modern conditional generative models face significant challenges when learning complex covariate dependencies. While sufficient dimension reduction (SD...
arXiv:2607. 01171v1 Announce Type: new Abstract: Sample-based generative models are increasingly used for probabilistic forecasting in high-stakes decision settings, yet their training objectives are blind to the decision maker's cost structure.
arXiv:2608. 06107v1 Announce Type: new Abstract: Machine learning offers a promising avenue to accelerate physical simulations by replacing computationally expensive traditional Partial Differential Equation (PDE) solvers with fast, differentiable surrogate models.
Sufficiently Reduced Distributional Regression (SRDR) is a generative approach that merges conditional distribution estimation with nonlinear sufficient dimension reduction (SDR). By framing SDR as a risk minimization problem using strictly proper scoring rules, SRDR jointly learns a dimension reduction map and a generative prediction model through minimization of the energy score, which can be estimated via sampling. The method extends to multi‑environment data and classification, and theoretical results show convergence of estimated conditional distributions in energy distance, implying asymptotic sufficiency. In experiments on CT slice localization, superconductivity data, and digit classification, SRDR recovers low‑dimensional sufficient structure and matches or surpasses state‑of‑the‑art nonlinear SDR methods in representation quality and predictive performance.