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
arXiv:2502. 04646v2 Announce Type: replace-cross Abstract: Weighted sampling -- sampling from a probability density function (PDF) proportional to the product of a base PDF and a weight function -- is a fundamental technique with wide-ranging applications in variance reduction, biased sampling, data augmentation, and more.
By Heasung Kim, Taekyun Lee, Hyeji Kim, Gustavo de Veciana
arXiv:2503.17894v4 Announce Type: replace-cross
Abstract: We propose a generative learner for estimating conditional average treatment effects and characterizing the full distribution of these effect...
By Maria Nareklishvili, Nicholas Polson, Vadim Sokolov
arXiv:2609.24328v1 Announce Type: new
Abstract: Combining predictions from different models can improve performance at machine learning tasks, but the training of the individual models and the rule u...
By Congye Wang, Yan Lin, Zheyang Shen, Matthew A. Fisher, Chris. J. Oates
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
arXiv:2510. 12744v2 Announce Type: replace-cross Abstract: We develop a unified statistical framework for softmax-gated Gaussian mixture of experts (SGMoE) that addresses three long-standing obstacles in parameter estimation and model selection: (i) non-identifiability of gating parameters up to common translations, (ii) intrinsic gate-expert interactions that induce coupled differential relations in the likelihood, and (iii) the tight numerator-denominator coupling in the softmax-induced conditional density.
By Do Tien Hai, Trung Nguyen Mai, TrungTin Nguyen, Nhat Ho, Binh T. Nguyen, Christopher Drovandi