arXiv Statistics @arxiv_stats@qoto.org

Leveraging Sparsity to Improve No-U-Turn Sampling Efficiency for Hierarchical Bayesian Models https://arxiv.org/abs/2603.02437 #stat.CO #stat.ME

Conformal Graph Prediction with Z-Gromov Wasserstein Distances https://arxiv.org/abs/2603.02460 #stat.ML #cs.LG

CCMnet: A Software Package for Network Generation with Congruence Class Models https://arxiv.org/abs/2603.02467 #stat.CO

Transportable inference using target population summary statistics under covariate shift https://arxiv.org/abs/2603.02474 #stat.ME

How to recover a permutation group amidst errors https://arxiv.org/abs/2602.23505 #math.ST #math.GR #stat.TH

Uncovering Physical Drivers of Dark Matter Halo Structures with Auxiliary-Variable-Guided Generative Models https://arxiv.org/abs/2602.23518 #stat.ML #cs.LG

Partition Function Estimation under Bounded f-Divergence https://arxiv.org/abs/2602.23535 #stat.ML #cs.LG

VaSST: Variational Inference for Symbolic Regression using Soft Symbolic Trees https://arxiv.org/abs/2602.23561 #stat.ME #stat.CO #stat.ML #cs.LG #cs.SC

Moment Matters: Mean and Variance Causal Graph Discovery from Heteroscedastic Observational Data https://arxiv.org/abs/2602.23602 #stat.ML #cs.LG

Fairness under Graph Uncertainty: Achieving Interventional Fairness with Partially Known Causal Graphs over Clusters of Variables https://arxiv.org/abs/2602.23611 #stat.ML #cs.LG

Multivariate Spatio-Temporal Neural Hawkes Processes https://arxiv.org/abs/2602.23629 #stat.ML #math.ST #stat.AP #stat.ME #stat.TH #cs.LG

Stress-Testing Assumptions: A Guide to Bayesian Sensitivity Analyses in Causal Inference https://arxiv.org/abs/2602.23640 #stat.ME

General Bayesian Policy Learning https://arxiv.org/abs/2602.23672 #stat.ML #econ.EM #math.ST #stat.ME #stat.TH #cs.LG

Predictive Hotspot Mapping for Data-driven Crime Prediction https://arxiv.org/abs/2602.23750 #stat.AP #cs.LG

Sparse Bayesian Modeling of EEG Channel Interactions Improves P300 Brain-Computer Interface Performance https://arxiv.org/abs/2602.17772 #stat.ME #cs.LG

Topological Exploration of High-Dimensional Empirical Risk Landscapes: general approach, and applications to phase retrieval https://arxiv.org/abs/2602.17779 #cond-mat.dis-nn #stat.ML #cs.LG

Spatial Confounding: A review of concepts, challenges, and current approaches https://arxiv.org/abs/2602.17792 #stat.ME

Drift Estimation for Stochastic Differential Equations with Denoising Diffusion Models https://arxiv.org/abs/2602.17830 #stat.ML #cs.LG

Interactive Learning of Single-Index Models via Stochastic Gradient Descent https://arxiv.org/abs/2602.17876 #stat.ML #math.ST #stat.TH #cs.LG

Learning from Biased and Costly Data Sources: Minimax-optimal Data Collection under a Budget https://arxiv.org/abs/2602.17894 #stat.ML #math.ST #stat.TH #cs.LG