惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

Cloudbric
Cloudbric
有赞技术团队
有赞技术团队
CTFtime.org: upcoming CTF events
CTFtime.org: upcoming CTF events
T
Threat Research - Cisco Blogs
L
LangChain Blog
Simon Willison's Weblog
Simon Willison's Weblog
Project Zero
Project Zero
Latest news
Latest news
S
Schneier on Security
Cisco Talos Blog
Cisco Talos Blog
MyScale Blog
MyScale Blog
C
Check Point Blog
IT之家
IT之家
P
Palo Alto Networks Blog
Threat Intelligence Blog | Flashpoint
Threat Intelligence Blog | Flashpoint
C
CERT Recently Published Vulnerability Notes
Scott Helme
Scott Helme
The Hacker News
The Hacker News
C
CXSECURITY Database RSS Feed - CXSecurity.com
G
Google Developers Blog
T
Tor Project blog
T
Threatpost
D
DataBreaches.Net
博
博客园 - 【当耐特】
酷 壳 – CoolShell
酷 壳 – CoolShell
T
Troy Hunt's Blog
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
Vercel News
Vercel News
云风的 BLOG
云风的 BLOG
NISL@THU
NISL@THU
P
Privacy & Cybersecurity Law Blog
cs.AI updates on arXiv.org
cs.AI updates on arXiv.org
C
Cisco Blogs
博
博客园_首页
S
Securelist
T
The Exploit Database - CXSecurity.com
Last Week in AI
Last Week in AI
量
量子位
U
Unit 42
Know Your Adversary
Know Your Adversary
Hugging Face - Blog
Hugging Face - Blog
S
Security Affairs
Google Online Security Blog
Google Online Security Blog
Hacker News: Ask HN
Hacker News: Ask HN
Webroot Blog
Webroot Blog
S
SegmentFault 最新的问题
Engineering at Meta
Engineering at Meta
N
News and Events Feed by Topic
P
Proofpoint News Feed
阮一峰的网络日志
阮一峰的网络日志

stat updates on arXiv.org

Can Adaptive Gradient Methods Converge under Heavy-Tailed Noise? A Case Study of AdaGrad Shallow ReLU$^s$ Networks in $L^p$-Type and Sobolev Spaces: Approximation and Path-Norm Controlled Generalization On Stability and Decomposition of Sample Quantiles under Heavy-Tailed Distributions Improved Baselines with Representation Autoencoders Symmetry-Compatible Principle for Optimizer Design: Embeddings, LM Heads, SwiGLU MLPs, and MoE Routers Feature Learning in Linear-Width Two-Layer Networks: Two vs. One Step of Gradient Descent A Two-Parameter Weibull Framework for Diagnosing Transformer Weight Distributions Calibeating for general proper losses: A Bregman divergence approach Dimension-Free Convergence of Discrete Diffusion Models: Adjoint Equations Induce the Right Space Sample-efficient inductive matrix completion with noise and inexact side-information Multi-task Linear Regression without Eigenvalue Lower Bounds: Adaptivity, Robustness, and Safety XAI and Statistical Analysis for Reliable Intrusion Detection in the UAVIDS-2025 Dataset: From Tree to Hybrid and Tabular DNN Ensembles Reasoning Models Don't Just Think Longer, They Move Differently TabPFN-3: Technical Report Reframing preprocessing selection as model-internal calibration in near-infrared spectroscopy: A large-scale benchmark of operator-adaptive PLS and Ridge models Towards a holistic understanding of Selection Bias for Causal Effect Identification Adaptive Kernel Density Estimation with Pre-training Coreset-Induced Conditional Velocity Flow Matching RISED: A Pre-Deployment Evaluation Framework for High-Stakes AI Decision-Support Systems, with Application to Healthcare ISOMORPH: A Supply Chain Digital Twin for Simulation, Dataset Generation, and Forecasting Benchmarks Yield Curves Dynamics Using Variational Autoencoders Under No-arbitrage Model-based Bootstrap of Controlled Markov Chains Online Learning-to-Defer with Varying Experts Self-Supervised Laplace Approximation for Bayesian Uncertainty Quantification Keeping Score: Efficiency Improvements in Neural Likelihood Surrogate Training via Score-Augmented Loss Functions One-Step Generative Modeling via Wasserstein Gradient Flows Exact Stiefel Optimization for Probabilistic PLS: Closed-Form Updates, Error Bounds, and Calibrated Uncertainty A Composite Activation Function for Learning Stable Binary Representations Adaptive Calibration in Non-Stationary Environments Real vs. Semi-Simulated: Rethinking Evaluation for Treatment Effect Estimation Federated Language Models Under Bandwidth Budgets: Distillation Rates and Conformal Coverage On Variance Reduction in Learning Mean Flows When Attention Beats Fourier: Multi-Scale Transformers for PDE Solving on Irregular Domains A Refined Generalization Analysis for Extreme Multi-class Supervised Contrastive Representation Learning Ensemble Distributionally Robust Bayesian Optimisation The Proxy Presumption: From Semantic Embeddings to Valid Social Measures Modulated learning for private and distributed regression with just a single sample per client device Query-efficient model evaluation using cached responses Order-Agnostic Autoregressive Modelling with Missing Data Grokking or Glitching? How Low-Precision Drives Slingshot Loss Spikes Tuning Derivatives for Causal Fairness in Machine Learning Spherical Flows for Sampling Categorical Data Bayesian Rain Field Reconstruction using Commercial Microwave Links and Diffusion Model Priors Unified Framework of Distributional Regret in Multi-Armed Bandits and Reinforcement Learning Jacobian-Velocity Bounds for Deployment Risk Under Covariate Drift Self-Attention as Transport: Limits of Symmetric Spectral Diagnostics Graph Convolutional Support Vector Regression for Robust Spatiotemporal Forecasting of Urban Air Pollution Stochastic Schrödinger Diffusion Models for Pure-State Ensemble Generation Understanding Self-Supervised Learning via Latent Distribution Matching Imbalanced Classification under Capacity Constraints On the Spectral Structure and Objective Equivalence of Orthogonal Multilabel Fisher Discriminants Partially Observed Structural Causal Models Robust and Fast Training via Per-Sample Clipping Efficient Preference Poisoning Attack on Offline RLHF Joint Energy Management and Coordinated AIGC Workload Scheduling for Distributed Data Centers: A Diffusion-Aided Reward Shaping Approach Distributional Causal Mediation via Conditional Generative Modeling A Theory of Saddle Escape in Deep Nonlinear Networks Adaptive Querying with AI Persona Priors Optimal Spatio-Temporal Decoupling for Bayesian Conformal Prediction Wasserstein Distributionally Robust Regret Optimization for Reinforcement Learning from Human Feedback Electricity price forecasting across Norway's five bidding zones in the post-crisis era Adversarial Robustness of NTK Neural Networks Identifiability and Stability of Generative Drifting with Companion-Elliptic Kernel Families A Limit Theory of Foundation Models: A Mathematical Approach to Understanding Emergent Intelligence and Scaling Laws Conditional Score-Based Modeling of Effective Langevin Dynamics Inference of Online Newton Methods with Nesterov's Accelerated Sketching ProEval: Proactive Failure Discovery and Efficient Performance Estimation for Generative AI Evaluation Score-Repellent Monte Carlo: Toward Efficient Non-Markovian Sampler with Constant Memory in General State Spaces Learning to Emulate Chaos: Adversarial Optimal Transport Regularization Geometric Layer-wise Approximation Rates for Deep Networks S2MAM: Semi-supervised Meta Additive Model for Robust Estimation and Variable Selection Beyond Coefficients: Forecast-Necessity Testing for Interpretable Causal Discovery in Nonlinear Time-Series Models Curiosity-Critic: Cumulative Prediction Error Improvement as a Tractable Intrinsic Reward for World Model Training Knowing When to Quit: A Principled Framework for Dynamic Abstention in LLM Reasoning Enhancing AI and Dynamical Subseasonal Forecasts with Probabilistic Bias Correction Zeroth-Order Optimization at the Edge of Stability Generative Augmented Inference Estimating Continuous Treatment Effects with Two-Stage Kernel Ridge Regression Rare Event Analysis via Stochastic Optimal Control Adaptive Learning via Off-Model Training and Importance Sampling for Fully Non-Markovian Optimal Stochastic Control. Complete version Beyond Fixed False Discovery Rates: Post-Hoc Conformal Selection with E-Variables Beyond Augmented-Action Surrogates for Multi-Expert Learning-to-Defer Spatio-temporal probabilistic forecast using MMAF-guided learning Conformal Policy Control The Implicit Curriculum: Learning Dynamics in RL with Verifiable Rewards Probabilistic NDVI Forecasting from Sparse Satellite Time Series and Weather Covariates Constrained Policy Optimization with Cantelli-Bounded Value-at-Risk Factorizable joint shift revisited Feature Learning Dynamics in Infinite-Depth Neural Networks Statistically-Guided Meta-Learning for Cross-Deployment Activity Recognition in Distributed Fiber-Optic Sensing DAPS++: Rethinking Diffusion Inverse Problems with Decoupled Posterior Annealing Branching Flows: Discrete, Continuous, and Manifold Flow Matching with Splits and Deletions Neural ARFIMA model for forecasting BRIC exchange rates with long memory Neural Stochastic Differential Equations on Compact State Spaces: Theory, Methods, and Application to Suicide Risk Modeling BOOST: A Data-Driven Framework for the Automated Joint Selection of Kernel and Acquisition Functions in Bayesian Optimization Random Walk Learning and the Pac-Man Attack Random Matrix Theory for Deep Learning: Beyond Eigenvalues of Linear Models Post-Training Augmentation Invariance Optimizing LLM Inference: Fluid-Guided Online Scheduling with Memory Constraints Dataset-Driven Channel Masks in Transformers for Multivariate Time Series
Extension of Process Convergence With Application to Chatterjee's Rank Correlation
[Submitted on 21 Aug 2024 (v1), last revised 8 Jul 2026 (this ve · 2024-08-21 · via stat updates on arXiv.org

Mathematics > Probability

arXiv:2408.11547 (math)

[Submitted on 21 Aug 2024 (v1), last revised 8 Jul 2026 (this version, v3)]

View PDF

Abstract:We give conditions under which weak convergence of a stochastic process indexed in the class of $d$-dimensional hyperrectangles is sufficient to ensure convergence in the larger class of functions of uniformly bounded Hardy-Krause variation. When applied to the empirical process, this can further be extended to derive weak convergence of V-processes indexed in the class of kernel functions which are coordinate-wise of uniformly bounded Hardy-Krause variation. Our proofs use a generalisation of the Koksma-Hlawka inequality for linear operators, allowing us to establish our results without any continuity assumptions on the functions involved. Our theory is complemented by two separate applications: First, we establish asymptotic normality of Chatterjee's rank correlation in the fully general setting. Second, we present new limit theorems for U- and V-processes of strongly mixing data.
Comments: This article was previously entitled `Asymptotic Normality of Chatterjee's Rank Correlation'. Improved theory on extension of process convergence. Includes formula for the limiting variance of Chatterjee's rank correlation
Subjects: Probability (math.PR); Statistics Theory (math.ST)
MSC classes: 62H20, 60F05, 62G20, 62G30
Cite as: arXiv:2408.11547 [math.PR]
  (or arXiv:2408.11547v3 [math.PR] for this version)
  https://doi.org/10.48550/arXiv.2408.11547

arXiv-issued DOI via DataCite

Submission history

From: Marius Kroll [view email]
[v1] Wed, 21 Aug 2024 11:55:34 UTC (94 KB)
[v2] Fri, 16 May 2025 11:02:54 UTC (48 KB)
[v3] Wed, 8 Jul 2026 13:44:19 UTC (85 KB)

Current browse context:

math.PR

Bookmark

BibSonomy Reddit

Bibliographic Tools

Bibliographic and Citation Tools

Bibliographic Explorer Toggle

Code, Data, Media

Code, Data and Media Associated with this Article

Demos

Demos

Related Papers

Recommenders and Search Tools

About arXivLabs

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

此内容由惯性聚合(RSS阅读器)自动聚合整理，仅供阅读参考。 原文来自 — 版权归原作者所有。