The paper introduces a Conditional Kernel Stein Discrepancy (CKSD) that extends traditional Kernel Stein Discrepancies to evaluate goodness‑of‑fit for conditional distributions. By employing an operator‑valued kernel on the covariate space, the authors lift unconditional KSDs to settings where only a conditional target model and joint samples are available, covering both smooth manifolds and discrete spaces. They prove that the CKSD statistic vanishes exactly when the conditional model matches the true conditional distribution for almost all covariates, and demonstrate its practical effectiveness through experiments on level, power, and runtime.
By Federico Matteucci, Florian Kalinke
arXiv:2609.37705v1 Announce Type: cross
Abstract: Goodness-of-fit testing is a basic tool for assessing whether a fitted procedure has captured the systematic information contained in the covariates....
By Xingwei Liu, Yuhong Yang, Wangli Xu
arXiv:2608. 07609v1 Announce Type: cross Abstract: High-throughput screening (HTS) assays are central to early-stage drug discovery but are often limited by extreme data sparsity, as primary screens typically use only a single replicate per test substance.
By Xiaohua Douglas Zhang
arXiv:2511. 03963v3 Announce Type: replace-cross Abstract: We introduce a density-power weighted variant of the Stein operator, called the $\gamma$-Stein operator, for robust inference with unnormalized probability models.
By Shinto Eguchi
arXiv:2608. 04860v1 Announce Type: cross Abstract: This paper develops procedures for nonparametric goodness-of-fit testing under covariate shift, where labelled data are drawn from a source population but goodness-of-fit is evaluated for a target population.
By Zhen Hou, Dong Xia
The paper introduces a unified framework for measuring divergences between conditional distributions using kernel embeddings, termed conditional maximum mean discrepancy (CMMD). It defines a family of CMMD metrics—CMMD_0, CMMD_1, and CMMD_2—each based on different RKHS embeddings, and generalizes them to a level‑s CMMD with clear assumptions and operator‑based smoothing connections. Additionally, the authors present a novel doubly robust estimator for CMMD and demonstrate through experiments that the metric captures complex conditional dependencies for statistical testing.
By Peter Moskvichev, Siu Lun Chau, Dino Sejdinovic
arXiv:2605. 21731v2 Announce Type: replace Abstract: Deep learning models are increasingly used in scientific prediction tasks where strong benchmark performance is often interpreted as evidence of scientifically meaningful behavior.
By Barbara Tarantino, Gennaro Auricchio, Paolo Giudici
arXiv:2303. 08777v3 Announce Type: replace-cross Abstract: Cross-validation is one of the most widely used tools for risk estimation and model selection in statistics and machine learning, yet its theoretical properties when embedded in a learning procedure remain insufficiently understood.
By Diego Marcondes, Cl\'audia Peixoto
arXiv:2607. 04401v1 Announce Type: cross Abstract: How robust and generalisable are pathology foundation models and have their scaling limites been reached?
By Dhyey Yajnik, Amina Asif, Fayyaz Minhas
arXiv:2602. 13362v2 Announce Type: replace-cross Abstract: A key challenge in probabilistic regression is ensuring that predictive distributions accurately reflect true empirical uncertainty.
By \'Ad\'am Jung, Domokos M. Kelen, Andr\'as A. Bencz\'ur
arXiv:2604. 11305v3 Announce Type: replace Abstract: Conformal selection (CS) uses calibration data to identify test inputs whose unobserved outcomes are likely to satisfy a pre-specified minimal quality requirement, while controlling the false discovery rate (FDR).
By Meiyi Zhu, Osvaldo Simeone
arXiv:2411.02771v3 Announce Type: replace-cross
Abstract: Doubly robust estimators are widely used for estimating average treatment effects and other linear summaries of regression functions. While c...
By Lars van der Laan, Alex Luedtke, Marco Carone