1,205 records
- Wasserstein Contraction of Coordinate Ascent Variational Inference
- Leave a Window Out: Modifying the Jackknife for Predictive Inference in Time Series
- Improved Guarantees for Heterogeneous Treatment-Effect Estimation via Matrix Completion
- M-estimation with e-statistics
- Diagnosing the conditional-mean barrier in scientific machine-learning surrogates
- Identification and Inference for Structural Accelerated Failure Time Models via Instrument Interactions
- Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models
- Dynamics of Stochastic Momentum with Sparse Updates in High Dimensions
- Target-Oriented Statistical Compression: Sufficiency, Reverse Martingales, and Sequential Monitoring
- Marginal likelihoods for finite-support Huber contamination
- Estimation and Inference for Win Measures with Multiple Ordinal Endpoints Subject to Missingness
- Triangular-Reference Schrödinger Bridges for Time Series Generation
- Learning manifold diffusion semigroups from graph transition matrices
- PAC Learning with Bandit Feedback: Sharp Sample Complexity in the Realizable Setting
- High-Dimensional Robust Change-Point Detection via Angular Kernel Statistics
- Weighted NPMLE for the Marginal Mean of Recurrent Events with a Competing Terminal Event
- Distributional Conformal Prediction for Markov Processes
- Nyström Kernel Stein Discrepancy Tests
- Conformity-Based Bayesian Projective Prediction
- Operationalizing Individual Fairness via Gradient Descent and Bradley-Terry Models
- Generalized Stochastic Approximation of the Log-Likelihood Ratio for Robust Sequential Change-Point Detection
- Asymmetric Scaling Laws from Sparse Features
- Move on Muon : A Hamiltonian probability gradient flow perspective of Muon optimizer
- On the Stability of Spherical Hellinger-Kantorovich Flows and Their Implications for Differential Privacy
- Convergence and non-asymptotic error analysis for kinetic Langevin samplers using the exact harmonic Langevin integrator
- Optimal Non-Asymptotic Edgeworth Expansions for Multivariate Neural Network Outputs
- Mad Props: Parallelism in Markov Chain Monte Carlo Through the Lens of the Infinite Proposal Limit
- From Betting to Empirical Bernstein LIL
- Finite-Particle Convergence Rates for Conservative and Non-Conservative Drifting Models
- Diffusion-based Denoising Beats Vanilla Score Matching in Parameter Estimation: A Theoretical Explanation
- New Confidence Regions for Linear Regression Parameters with Stationary-Ergodic Dependent Errors
- Particle filtering methods for partially observed branching processes
- Unbiased Gradients, Moving Stability Boundaries: Exact Mini-Batch Geometry in Linear Self-Attention
- Likelihood-informed dimension reduction across tempered Bayesian posteriors
- Factor Augmented High-Dimensional SGD
- Gaussian Approximation and Multiplier Bootstrap for Federated Linear Stochastic Approximation
- Federated Learning with Incomplete Data: When to Use Complete Cases and When to Weight
- Simple Approximation and Derivative Free Inference-Time Scaling for Diffusion Models via Sequential Monte Carlo on Path Measures
- Conditional Predictive Inference for General Structured Data with Group Symmetries
- Connections between the Föllmer process and the denoising diffusion probabilistic model
- Wasserstein bounds for denoising diffusion probabilistic models via the Föllmer process
- Compositional Periodic Spline Approximation for Circular Density Data in Bayes Spaces
- On Stability and Decomposition of Sample Quantiles under Heavy-Tailed Distributions
- Generalized Functional ANOVA: A Complete Theoretical Framework
- SURGE: Approximation and Training Free Particle Filter for Diffusion Surrogate
- Bayesian Latent Space Models for Graphs Are Misspecified: Toward Robust Inference via Generalized Posteriors
- Weak Convergence and Gaussian Limits for a General-Dimensional Origin-Invariant Cramér--von Mises Statistic
- Modelling pairs of Poissons and binomials with negative correlation
- Do Stationarity Transformations Actually Improve Time Series Forecasts? A Controlled Experimental Evaluation
- Splitting schemes and estimators for stochastic differential equations with Hölder multiplicative noise