The paper introduces Conditional-Independence-Regularized Distributional Autoencoders, a framework for learning low-dimensional representations of mixed-type data that includes both numerical and categorical variables. It uses an energy-score objective for numerical variables, a likelihood objective for categorical variables, and an auxiliary conditional independence regularization term to capture dependencies between variable types. The authors provide theoretical analysis and demonstrate that the method improves categorical distribution recovery, achieves competitive overall conditional distribution recovery, and preserves mixed-type dependence structure on synthetic and real-world datasets.
By Siyuan Tang, Gongjun Xu, Ji Zhu
arXiv:2607. 04360v1 Announce Type: cross Abstract: 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.
By Shijin Gong, Baihua He, Xinyu Zhang
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.
By Alexander Henzi, Tiange Liu, Xinwei Shen
arXiv:2609.23789v1 Announce Type: new
Abstract: Modern conditional generative models face significant challenges when learning complex covariate dependencies. While sufficient dimension reduction (SD...
By Wenxi Tan, Bing Li, Lingzhou Xue
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.
The paper introduces Survival In-Context (SIC), a prior‑fitted in‑context learning model for survival analysis that is pretrained solely on synthetic data generated from a flexible survival prior. SIC approximates Bayesian posterior predictive inference, allowing individualized survival predictions in a single forward pass without task‑specific training or hyperparameter tuning. Experiments on real‑world survival datasets show that SIC matches or outperforms classical and deep survival models, especially when data are limited.
By Dmitrii Seletkov, Paul Hager, Georgios Kaissis, Rickmer Braren, Daniel Rueckert, Raphael Rehms
OrthoGen introduces a generative orthogonal learner for estimating conditional distributional potential outcomes (CDPOs) under time‑varying treatments, addressing challenges of time‑varying confounding. It combines a generative recursive g‑computation adjustment strategy with Neyman‑orthogonal, doubly robust learning, achieving rate double robustness and quasi‑oracle efficiency. The method is flexible, supporting various generative backbones, and demonstrates strong performance on synthetic, semi‑synthetic, and real‑world datasets.
By Tom\`as Garriga, Valentyn Melnychuk, Konstantin Hess, Eduard Serrahima de Cambra, Axel Brando, Gerard Sanz, Stefan Feuerriegel
The paper introduces the transport Neural Tangent Kernel (tNTK) to enable active learning for conditional generative models that produce distributional outputs. By decomposing the Wasserstein test risk into mean and shape components, the authors show that mean‑based active learning misses distributional shape, and propose tNTK as a tractable kernel that captures sensitivity of the predicted distribution to model parameters. Using tNTK as a Gaussian process covariance, they greedily select inputs that minimize posterior variance, and demonstrate that this distribution‑aware strategy outperforms mean‑based NTK baselines across several generative model types and datasets.
By Jayoung Ryu, Kyunghyun Cho, Romain Lopez
The Marformer is a Transformer model designed to predict conditional marginal distributions of missing variables given observed data, enabling computation of Bayes risk and Value of Information. It learns a hidden-vector representation for each variable’s distribution and refines these through attention, similar to BERT’s masked language modeling but for probabilistic inference. Evaluations on synthetic Bayesian networks, discretized Gaussians, structured annotation data, and a real annotation dataset show that the Marformer matches or surpasses classical missing-data methods while being faster and not requiring explicit joint distribution modeling.
By Prabhav Singh, Xiheng Tom Wang, Haojun Shi, Jason Eisner
arXiv:2604. 05324v2 Announce Type: replace Abstract: Statistical evaluation aims to estimate the generalization performance of a model using held-out i.
By Shashaank Aiyer, Yishay Mansour, Shay Moran, Han Shao
arXiv:2603. 07108v2 Announce Type: replace-cross Abstract: Accurate and reliable forecasting of epidemic incidences is critical for public health preparedness, yet it remains a challenging task due to complex nonlinear temporal dependencies and heterogeneous spatial interactions.
By Rajdeep Pathak, Tanujit Chakraborty
arXiv:2607. 23348v1 Announce Type: cross Abstract: Mixed continuous--categorical data pose a representation problem for continuous generative models.
By Yuefei Shen, Xiaotong Shen