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1,205 records
- 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
- A Fourier perspective on the learning dynamics of neural networks: from sample complexities to mechanistic insights
- Diffusion-Based Stochastic Operator Networks for Uncertainty Quantification in Stochastic Partial Differential Equations
- Sample-efficient inductive matrix completion with noise and inexact side-information
- $α$-TCAV: A Unified Framework for Testing with Concept Activation Vectors
- Testing properties of trees in graphical models with covariance queries
- Dimension-Uniform Discretization Analysis of Preconditioned Annealed Langevin Dynamics for Multimodal Gaussian Mixtures
- Adaptive Long-Run Variance Thresholding for Sparse Covariance Estimation in High-Dimensional Time Series
- A Bayesian Longitudinal Spatial Normative Model for Individualized Brain Deviation Mapping
- Scaling Laws from Sequential Feature Recovery: A Solvable Hierarchical Model
- Parametrically Adaptive Transition Polynomial: a Signed-Parity Continuous-alpha Extension of Kunchenko Stochastic Polynomials
- Differentially private inference framework for Riemannian manifold data
- Average Gradient Outer Product in kernel regression provably recovers the central subspace for multi-index models
- Harnessing Unimodality in Semiparametric Contextual Pricing via Oracle Price Map Learning
- State-of-art minibatches via novel DPP kernels: discretization, wavelets, and rough objectives
- The Sample Complexity of Multiple Change Point Identification under Bandit Feedback
- Learning Perturbations to Extrapolate Your LLM
- On the Limits of Latent Reuse in Diffusion Models
- Structural identifiability of partially-observed stochastic processes: from single-particle trajectories to total particle density data
- What is Learnable in Valiant's Theory of the Learnable?
- Predictive Inference via Kernel Density Estimates
- Posterior Contraction Rates for Sparse Kolmogorov-Arnold Networks in Anisotropic Besov Spaces
- Model-based Bootstrap of Controlled Markov Chains
- A Unified Framework for Critical Scaling of Inverse Temperature in Self-Attention
- A Statistical Framework for Learning Preferences from the Past
- Characterizing the Generalization Error of Random Feature Regression with Arbitrary Data-Augmentation