ARC framework models high-dimensional compositional data with exact zeros by treating compositions as directions of latent vectors with an explicit active-set process on the hypersphere.
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Wood, Natalya Pya, and Benjamin Säfken
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The balloon mean is a computationally tractable robust differentially private mean estimator with theoretical guarantees under heavy-tailed contaminated elliptical models.
A Bayesian hypergraph inference method models EHR multi-disease risk by letting risk factors modulate latent hyperedges (disease subsets) with repulsion priors and structured variational inference for uncertainty and scalability.
APIC applies Neural Processes in a two-branch latent model to amortize Kennedy-O'Hagan-style calibration, separating instance-specific parameters from shared structural discrepancies for fast inference on new realizations.
SVI-Bench provides 35K hours of sports video with 9 tasks across four cognitive levels, revealing models drop from ~74% on action QA to 5% on agentic evidence integration.
A semiparametric framework clusters high-dimensional elliptical data with heavy tails via cluster-specific centers, a common unknown radial generator, and a shared sparse precision matrix, with GEM algorithm and high-dimensional consistency guarantees.
A GenAI-based method extracts representations from unstructured data and uses a neural network to fit marginal structural models that recover causal effects of treatment feature sequences including their positions.
HPPCA is a hierarchical extension of PPCA that uses Gaussian processes to model within-subject dynamics in longitudinal data, outperforming standard PPCA and functional PCA in imputation under missingness and misspecification.
BUGS embeds univariate marginal guidance into a regularized horseshoe prior to induce adaptive shrinkage, supplies theoretical contraction guarantees, and offers an active-set MCMC approximation that scales to p=1,000,000 while improving false-discovery control.
MSFAST extends the FAST FPCA method to multivariate sparse data via Bayesian modeling with orthonormal splines, standardization, Procrustes alignment, and efficient computation, yielding valid inferences especially in low signal-to-noise settings.
Characterizes the distributional mean-field limit of co-evolving latent space networks with feedback, including empirical measures and graphon convergence, via a conditional propagation of chaos result.
Proposes FDR-controlling posterior decision rules for signal detection under horseshoe and similar continuous shrinkage priors that attain the optimal detection boundary with asymptotic FDR and FNR control in sparse normal means models.
Choquet risk ranks valid possibilistic inferential models by linking their efficiency to expected performance of induced confidence sets under concentration penalties.
A Bayesian latent GP calibration framework for aerodynamic surrogates marginalizes input uncertainty and matches output uncertainty statistics, achieving 94.2-95.8% coverage of true 95% intervals.
A structured secant quasi-Newton method (qEFS) for smoothing parameter selection in general smooth models that approximates the Hessian and is easier to implement than exact second-order methods.
Proposes and analyzes a homogeneity test using squared L2 distance of empirical EOT maps to uniform-on-ball reference, with FCLT, Gaussian quadratic null limit, consistency, local power, and weighted multiplier bootstrap.
Joint calibrated LTMLE integrates LTMLE with joint calibrated weights to improve finite-sample efficiency and robustness to misspecification for per-protocol effect estimation in target trial emulation.
An intrinsic spherical kernel ridge regression framework is introduced for non-linear responses on spheres, reducing infinite-dimensional estimation to finite via the representer theorem with convergence rates shown.
IBFU extends KOH calibration by representing parameter corrections as GPs with shrinkage priors that nest the fixed-parameter case while permitting controlled input-dependent variation when supported by data.
A model averaging estimator for DAGs in Gaussian graphical models achieves asymptotic optimality, weight consistency, parameter consistency, and consistency even under complete misspecification of all candidate graphs.
KAPLAN-HR applies B-spline KANs to nonparametric hazard estimation in survival analysis, recovering GAMs in the single-layer case, capturing interactions via deeper layers, with convergence rates independent of covariate dimension for KAN-representable targets, and competitive performance on six cli
Derives new analytical sample size and power formulas for marginal hazard ratios in causal inference with time-to-event outcomes, applicable to randomized trials and observational studies via IPW estimators.
NBFFG combines a closed-form backward filter from a linear-Gaussian proxy process with a learned neural residual to enable efficient variational inference and unbiased pathwise subsampling for nonlinear diffusions on trees.
The paper introduces a penalized distributed lag non-linear Lee-Carter framework that adds temperature and influenza effects, negative binomial overdispersion, SARIMA dynamics, and copula dependence for improved regional weekly mortality forecasts on French data 1990-2019.
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Mean-Field Analysis of Latent Variable Process Models on Dynamically Evolving Graphs with Feedback Effects
Characterizes the distributional mean-field limit of co-evolving latent space networks with feedback, including empirical measures and graphon convergence, via a conditional propagation of chaos result.