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Every paper Pith has read. Search by title, abstract, or pith.
883 papers in math.ST · page 2
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Log-ratio transform powers periodic splines for circular densities
Compositional Periodic Spline Approximation for Circular Density Data in Bayes Spaces
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DDPMs reach optimal Wasserstein bounds in any dimension
Wasserstein bounds for denoising diffusion probabilistic models via the F\"ollmer process
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C-SymmPI achieves near-conditional coverage for symmetric structured data
Conditional Predictive Inference for General Structured Data with Group Symmetries
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s-step self-distillation optimizes shrinkage for s-spike covariances
Self-Distillation is Optimal Among Spectral Shrinkage Estimators in Spiked Covariance Models
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Stationarity transforms improve forecasts only 18% of the time
Do Stationarity Transformations Actually Improve Time Series Forecasts? A Controlled Experimental Evaluation
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Adjustment lets Poisson and binomial pairs show negative correlation
Modelling pairs of Poissons and binomials with negative correlation
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Brownian sheet tests check multivariate normality and symmetry
Multivariate EDF tests for uniformity, normality,spherical and elliptical symetry, and independence based on a Brownian sheet deconstruction
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L2 CVP distance to log-unit lattice converges to π sqrt(n)/(2 sqrt(6))
Module Lattice Security (Part III): Structured CVP Distance on the Log-Unit Lattice
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Adjoint equations remove S-dependence from discrete diffusion convergence
Dimension-Free Convergence of Discrete Diffusion Models: Adjoint Equations Induce the Right Space
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Noisy matrix completion cuts samples to side info dimension
Sample efficient inductive matrix completion with noise and inexact side information
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SGD on diagonal linear networks converges exponentially to zero risk
High-dimensional Limit of SGD for Diagonal Linear Networks
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New index reaches zero exactly when random vectors are sub-independent
Quantifying Dependence Between Random Vectors: A New Index with Applications
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Private tests keep finite-sample power in survival analysis
Differentially private hypothesis testing in survival analysis
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Lamperti splitting yields convergent estimators for Hölder SDEs
Splitting schemes and estimators for stochastic differential equations with H\"older multiplicative noise
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Finite context converges to infinite limits at rate n to the minus 1 over d
Propagation of Chaos in Contextual Flow Maps
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Effective ranks bound sample cross-covariance deviations
Concentration Inequalities for Sample Cross-Covariances
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Hypothesis test picks samples to unlearn data domains
Statistical Unlearning of Distributions: A Hypothesis Testing Approach
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Dependent observations reach zero entropy after O(log(1/Pmin)) samples
Breaking the Finite-Sample Barrier in Entropy Coupling
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Covert probing yields square-root delay gain in change detection
Covert Bayesian Quickest Change Detection
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Entropy rate sets optimal steps for low-budget flow samplers
Entropy Across the Bridge: Conditional-Marginal Discretization for Flow and Schr\"odinger Samplers
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Bootstrap norm test works for high-dimensional means without sparsity
Tests for the mean of high-dimensional data
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Nearest-neighbour rates hold without compact covariate supports
Nearest-Neighbour Matching on Unbounded Supports and Covariate Shift Transfer
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Tree properties testable with sub-quadratic covariance queries
Testing properties of trees in graphical models with covariance queries
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Node-private algorithms recover communities in SBMs if epsilon grows fast enough
Node-private community estimation in stochastic block models: Tractable algorithms and lower bounds
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Graph OU model for edges improves network forecasts
Edge-indexed network time series with graph Ornstein-Uhlenbeck dynamics
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Non-log-concave sampling matches log-concave dimension dependence
Complexity of Non-Log-Concave Sampling in Fisher Information
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Unitary transform frees point-process tests from parameter dependence
Goodness-of-Fit Testing for Point Processes in Large Populations
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Gaussian mixtures have algebraically bounded modes
Bounds on the Number of Modes of a Gaussian Mixture Density
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Neural point-forms turn point clouds into comparison matrices
Neural Point-Forms
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Operator concentration bounds now depend only on intrinsic dimension
Intrinsic-dimension empirical Bernstein inequalities for bounded self-adjoint operators
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AGOP from kernel regression recovers central subspace with fewer samples
Average Gradient Outer Product in kernel regression provably recovers the central subspace for multi-index models
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U-statistics obtain anytime-valid sequences at optimal rates
Asymptotic Anytime-Valid Inference for U-statistics
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Alpha parameter extends Kunchenko polynomials to fractional powers
Parametrically Adaptive Transition Polynomial: a Signed-Parity Continuous-alpha Extension of Kunchenko Stochastic Polynomials
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Sequential feature recovery produces power-law scaling
Scaling Laws from Sequential Feature Recovery: A Solvable Hierarchical Model
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Bayesian model cuts brain deviation map error by 45-54%
A Bayesian Longitudinal Spatial Normative Model for Individualized Brain Deviation Mapping
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Bayesian model cuts brain deviation mapping error by 54%
A Bayesian Longitudinal Spatial Normative Model for Individualized Brain Deviation Mapping
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Long-run variance thresholding recovers sparse covariances in time series
Adaptive Long-Run Variance Thresholding for Sparse Covariance Estimation in High-Dimensional Time Series
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SPADE gains subleading edge over direct imaging for aligned sources
Singular Asymptotics of SPADE in Quantum Source Discrimination
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NN radii converge almost surely under polynomial mixing
Nearest-Neighbor Radii under Dependent Sampling
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Double/debiased machine learning of quantile treatment effects on long-term outcomes in…
Double/debiased machine learning of quantile treatment effects on long-term outcomes in clinical trials
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Unique equilibrium found for nonlinear network pricing
Equilibrium and Pricing in Consumer Networks with Nonlinear Utilities: An Online Shape-Constrained Learning Approach
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Regret equals covariance between costs and decisions
Regret Equals Covariance: A Closed-Form Characterization for Stochastic Optimization
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Estimator flags noisy eigenvalues in finite data spectra
Sampling pseudospectrum for data-driven matrices
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The paper proves that predictive processes built from the classic kernel density…
Predictive Inference via Kernel Density Estimates
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Valiant learnability equals poly-size query compression
What is Learnable in Valiant's Theory of the Learnable?
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Pattern frequencies in rank plots obey a functional CLT
Pattern-based tests for two-dimensional copulas
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Weighted tail estimators work for any censoring strength
Weighted and Truncated Tail Index Estimation under Random Censoring: A Unified Full-Range Framework
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Markov product turns Chatterjee coefficient into dependence functions
Dependence functions based on Chatterjee's rank correlation
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Markov product extends Chatterjee's correlation to dependence functions
Dependence functions based on Chatterjee's rank correlation
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Learned continuous perturbations boost LLM extrapolation to new domains
Learning Perturbations to Extrapolate Your LLM