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1,205 records
- Scalable Kernel-Based Distances for Statistical Inference and Integration
- Robust Model Selection for Discovery of Latent Mechanistic Processes
- Flow Matching is Adaptive to Manifold Structures
- Local Fréchet regression with toroidal predictors
- Estimating the Partially Linear Zero-Inflated Poisson Regression Model: a Robust Approach Using a EM-like Algorithm
- On the non-uniformity of the 2026 FIFA World Cup draw
- Detecting Where Effects Occur by Testing Hypotheses in Order
- Zero Variance Portfolio
- The generalized underlap coefficient with an application in clustering
- Smoothness Adaptivity in Constant-Depth Neural Networks: Optimal Rates via Smooth Activations
- Path-conditioned training: a principled way to rescale ReLU neural networks
- Optimality of the Half-Order Exponent in the Turing-Good Identities for Bayes Factors
- Conformal Risk Control for Non-Monotonic Losses
- Implicit Bias and Convergence of Matrix Stochastic Mirror Descent
- Distributional Discontinuity Design
- Stochastic Gradient Variational Inference with Price's Gradient Estimator from Bures-Wasserstein to Parameter Space
- Optimal and Structure-Adaptive CATE Estimation with Kernel Ridge Regression
- Model Error Embedding with Orthogonal Gaussian Processes
- A variational framework for modal estimation
- On the Generalization and Robustness in Conditional Value-at-Risk
- Semiparametric Uncertainty Quantification via Isotonized Posterior for Deconvolutions
- Design-based theory for covariate adjustment in generalized pairwise comparisons
- Minimally Discrete and Minimally Randomized p-Values
- genriesz: A Python Package for Automatic Debiased Machine Learning with Generalized Riesz Regression
- Interactive Learning of Single-Index Models via Stochastic Gradient Descent
- Learning from Biased and Costly Data Sources: Minimax-optimal Data Collection under a Budget
- Introducing the b-value: combining unbiased and biased estimators from a sensitivity analysis perspective
- Generalised Linear Models Driven by Latent Processes: Asymptotic Theory and Applications
- First versus full or first versus last: U-statistic change-point tests under fixed and local alternatives
- M-estimation under Two-Phase Multiwave Sampling with Applications to Prediction-Powered Inference
- Space-filling lattice designs for computer experiments
- Functional Central Limit Theorem for Stochastic Gradient Descent
- Fixed-Horizon Self-Normalized Inference for Adaptive Experiments via Martingale AIPW/DML with Logged Propensities
- Profiling vs. Case-specific Evidence: A Probabilistic Analysis
- Constrained and Composite Sampling via Proximal Sampler
- A Bayesian Approach to Low-Discrepancy Subset Selection
- Weak Poincaré inequalities for two-component Deterministic-scan Metropolis-within-Gibbs samplers
- Block Empirical Likelihood Inference for Longitudinal Generalized Partially Linear Single-Index Models
- Why Self-Training Helps and Hurts: Denoising vs. Signal Forgetting
- MPL-HMC: A Tunable Parameterized Leapfrog Framework for Robust Hamiltonian Monte Carlo
- Estimation and Inference of the Win Ratio for Two Hierarchical Endpoints Subject to Censoring and Missing Data
- Linear Regression with Unknown Truncation Beyond Gaussian Features
- Some bivariate distributions on a discrete torus with application to wind direction datasets
- A unified testing approach for log-symmetry using Fourier methods
- Stratified Sampling for Model-Assisted Estimation with Surrogate Outcomes
- Random Forests as Statistical Procedures: Design, Variance, and Dependence
- AdaGrad-Diff: A New Version of the Adaptive Gradient Algorithm
- Detecting Parameter Instabilities in Functional Concurrent Linear Regression
- Representation Learning with Blockwise Missingness and Signal Heterogeneity
- Improving the adjusted Benjamini--Hochberg method using e-values in knockoff-assisted variable selection