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- Bayesian Spatio-temporal Modeling of Multi-Site Wastewater Surveillance Data
- Identification and Bounding of Joint Expectation over Potential Outcomes
- Adaptive Experimentation Can Hide Model Error:Detection Limits and Falsification Budget
- marp: Model-Averaged Inference for Parametric Renewal Processes in R
- Statistical and Privacy Guarantees for Deep M-Regression via Stochastic Gradient Langevin Dynamics
- Inference after selection on randomized p-values
- Changepoint localization via nested exchangeability tests: from point estimates to confidence intervals
- A Unified Coordinate Descent Framework for Penalized Longitudinal Log-Contrast Quantile Regression
- Adversarial Multiplicity: Cherry-Picked Models and the Limits of Disclosure
- When Do Sports Leagues Stop Being Competitive? Bayesian Detection of Competitive-Balance Eras from Match-Level Data
- Neural Realized Measures: Forecast-Supervised Intraday Aggregation for Volatility Forecasting
- Clouded Representatives and Score Matching for Distributed Quantile Regression
- Satisficing as competitive Bayesian weighting: a minimal model of goal-directed choice under an unattained target
- Geometry-Constrained Kolmogorov-Arnold Networks: Learning Edge Geometry via Banach Duality
- The Earth Moves, But So Does the Bias: Systematic Upward Bias of the Wasserstein (Earth Mover's) Distance and Permutation-Based Null Calibration
- Integrating Background Knowledge for Scalable Causal Discovery
- Diffusion-Driven State Space Models
- Recursively Trained Diffusion Models: Limiting Collapse Distribution and Spectral Characterization
- Empirical Bayes Rebiasing
- Improving Minority Population Sampling with BISG Probabilities: Evidence from a Survey of Jewish Americans
- Hyperpolarity of polar foliations of codimension two
- An Accelerated Proximal Gradient Method for Bures-Wasserstein Space
- Almost sure bounds and tail phases for critical non-Gaussian holomorphic chaos
- Symplectic singularities in Relative Langlands duality for products of general linear groups
- Adversarial Training with Large Step Sizes: Implicit Bias, Optimization, and Sharpness