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Every paper Pith has read. Search by title, abstract, or pith.
883 papers in math.ST · page 3
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Change-point sample needs depend on both jumps and positions
The Sample Complexity of Multiple Change Point Identification under Bandit Feedback
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Bell results fit by dropping realism and avoiding conspiracy
Three ways to find comfort with the Bell proof and the results of the Bell experiments
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Imaginary bispectrum detects cluster orientation
Orientation in Poisson Cluster Processes via Imaginary Bispectra
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Optimal tests aggregate into log-optimal e-processes
Optimal sequential tests yield log-optimal e-processes
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Sampler matches smooth-case rate for composite log-concave densities
A proximal gradient algorithm for composite log-concave sampling
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Bootstrap yields valid CIs for offline RL value functions
Model-based Bootstrap of Controlled Markov Chains
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Monotone operators approximated via graph convergence
Approximation of Maximally Monotone Operators : A Graph Convergence Perspective
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Pattern tests for independence get explicit null limits
Efficiency of pattern-based independence test
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Weaker likelihood ratio shapes still give stochastic orders
Stochastic Ordering under Weaker Likelihood-Ratio Shape Conditions
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Random sphere points give strong quantization for moderate n
Non-asymptotic quantisation of spherically symmetric distributions
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Augmented KRR separates linear and nonlinear parts
Adaptive Kernel Ridge Regression with Linear Structure: Sharp Oracle Inequalities and Minimax Optimality
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Bayesian bootstrap recovers Efron method as special case
Bayesian and Empirical Bayesian Bootstrapping
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Sparse Bayesian KANs achieve near-minimax contraction
Posterior Contraction Rates for Sparse Kolmogorov-Arnold Networks in Anisotropic Besov Spaces
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Machine learning corrects covariances to improve small-ensemble data assimilation
Enabling High-Accuracy Data Assimilation with Limited Ensembles via Machine Learning-Based Covariance Correction
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MLP scales small-ensemble covariances to cut EnKF error
Enabling High-Accuracy Data Assimilation with Limited Ensembles via Machine Learning-Based Covariance Correction
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McDiarmid bound removes K-scaling barrier in streaming decision trees
MIST: Reliable Streaming Decision Trees for Online Class-Incremental Learning via McDiarmid Bound
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McDiarmid bound fixes streaming trees for class-incremental learning
MIST: Reliable Streaming Decision Trees for Online Class-Incremental Learning via McDiarmid Bound
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Gaussian limits for spectral statistics survive fourth-moment corrections
The Geometry of Spectral Fluctuations: On Near-Optimal Conditions for Universal Gaussian CLTs, with Statistical Applications
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Deterministic residual update removes stochastic variance in ensemble filters
A Data-Consistent Approach to Ensemble Filtering
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The paper develops a polynomial-time algorithm using semidefinite programming relaxation…
Efficient Robust Constrained Signal Detection via Kolmogorov Width Approximations
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Stable barcodes track how dependency clusters evolve in dynamic Bayesian networks
A Stable Distance Persistence Homology for Dynamic Bayesian Network Clustering
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Even-order GW functionals converge at rate n^{-2/max{min(d,4)}}
Empirical Convergence of Even-Order Gromov-Wasserstein Functionals
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Even-order GW errors converge at n^{-2/max{min d,4}} rate
Empirical Convergence of Even-Order Gromov-Wasserstein Functionals
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Bahadur representation yields high quantile homogeneity test
A Generative High Quantile Homogeneity Test Using Bahadur Representation for Heteroskedastic High Quantile Regression of Tail Dependent Time Series
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Modularity shows overlap gap on stochastic block model
The stochastic block model has the overlap graph property for modularity
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Moments made orthogonal to any order with fixed extra parameters
Higher-Order Neyman Orthogonality in Moment-Condition Models
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Finite VC dimension enables finite-sample tests for distribution trade-offs
When Are Trade-Off Functions Testable from Finite Samples?
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LASSO matches homogeneous threshold for mixed-quality sparse data
Price of Quality: Sufficient Conditions for Sparse Recovery using Mixed-Quality Data
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Closed-form spectral formulas estimate density ratios from moments
A Spectral Framework for Closed-Form Relative Density Estimation
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Infinite measures admit unique cyclically monotone zero-couplings
Zero-couplings of infinite measures with cyclically monotone support and multivariate regular variation
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New measure tracks tail dependence in heavy-tailed linear processes
Measuring Tail Dependence in Linear Processes: Theory and Empirics
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Measure quantifies tail dependence in linear processes
Measuring Tail Dependence in Linear Processes: Theory and Empirics
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Test error in augmented random features depends only on data and augmentation moments
Characterizing the Generalization Error of Random Feature Regression with Arbitrary Data-Augmentation
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Gaussian priors hit minimax rates for point-process intensity
Increasing domain asymptotics for covariate-based nonparametric Bayesian intensity estimation with Gaussian and Besov-Laplace priors
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GAN method estimates full causal distributions with minimax optimality
Extended Wasserstein-GAN Approach to Causal Distribution Learning: Density-Free Estimation and Minimax Optimality
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Kaplan-Meier estimators give unbiased ARL and ADD for finite sequences
Accurate Evaluation of Quickest Changepoint Detectors via Non-parametric Survival Analysis
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Tests for exposure mapping models cannot beat random rejection
On the Impossibility of Specification Testing of Interference Models Based on Exposure Mappings
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Normalizing flows recover fast equilibrium from slow data alone
Learning stochastic multiscale models through normalizing flows
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Regularization scheme extends to conditional density estimation
The general regularisation scheme applied to conditional density estimation
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Matching bounds set exact mu threshold for submatrix detection
Minimax optimal submatrix detection: Sharp non-asymptotic rates
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Exact signal thresholds derived for submatrix detection
Minimax optimal submatrix detection: Sharp non-asymptotic rates
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Multi-source transfer carries adaptation cost past phase transition
The Statistical Cost of Adaptation in Multi-Source Transfer Learning
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Bridge functions identify path-specific effects with hidden confounders
Proximal Path-Specific Inference
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Feature selection tolerates noise and weak symmetry
Universal Feature Selection with Noisy Observations and Weak Symmetry Conditions
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Forward KL regularization yields first fast rates for offline contextual bandits
Fast Rates for Offline Contextual Bandits with Forward-KL Regularization under Single-Policy Concentrability
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Smoothed Wasserstein costs converge at moment-based rates
Two-Sample Inference for Gaussian-Smoothed Wasserstein Costs with Finite Moments
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Online EM inherits batch CLT with small tracking lag
Higher-Order Equilibrium Tracking for EM-Compressible Online Estimation
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Estimators achieve minimax optimal rates for unbalanced transport-growth pairs
Minimax Optimal Estimation of Transport-Growth Pairs in Unbalanced Optimal Transport
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Framework links algorithm outputs to MLE for latent space networks
Bridging Theory and Practice: Statistical Inference for Latent Space Models of Networks