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

A
About on SuperTechFans
Cloudbric
Cloudbric
C
CERT Recently Published Vulnerability Notes
G
GRAHAM CLULEY
V
Vulnerabilities – Threatpost
C
Cisco Blogs
T
Tenable Blog
P
Privacy International News Feed
T
The Exploit Database - CXSecurity.com
I
Intezer
AWS News Blog
AWS News Blog
IT之家
IT之家
博
博客园 - 司徒正美
C
Cybersecurity and Infrastructure Security Agency CISA
博
博客园 - 【当耐特】
The Hacker News
The Hacker News
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
Spread Privacy
Spread Privacy
S
SegmentFault 最新的问题
博
博客园 - Franky
人人都是产品经理
人人都是产品经理
cs.CV updates on arXiv.org
cs.CV updates on arXiv.org
V
Visual Studio Blog
C
CXSECURITY Database RSS Feed - CXSecurity.com
H
Hacker News: Front Page
Latest news
Latest news
Scott Helme
Scott Helme
腾
腾讯CDC
宝玉的分享
宝玉的分享
大猫的无限游戏
大猫的无限游戏
cs.AI updates on arXiv.org
cs.AI updates on arXiv.org
A
Arctic Wolf
S
Securelist
雷峰网
雷峰网
The GitHub Blog
The GitHub Blog
Project Zero
Project Zero
Google DeepMind News
Google DeepMind News
P
Palo Alto Networks Blog
F
Fortinet All Blogs
Schneier on Security
Schneier on Security
云风的 BLOG
云风的 BLOG
Security Archives - TechRepublic
Security Archives - TechRepublic
The Last Watchdog
The Last Watchdog
WordPress大学
WordPress大学
MongoDB | Blog
MongoDB | Blog
L
LINUX DO - 最新话题
S
Schneier on Security
NISL@THU
NISL@THU
Jina AI
Jina AI
M
MIT News - Artificial intelligence

stat updates on arXiv.org

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 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 Segmenting Human-LLM Co-authored Text via Change Point Detection 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 Generative Synthetic Data for Causal Inference: Pitfalls, Remedies, and Opportunities 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
Sequential Bootstrap for Out-of-Bag Error Estimation: A 100-Seed Replication Study and Variance-Structure Analysis
[Submitted on 22 Nov 2025 (v1), last revised 1 Jun 2026 (this ve · 2026-06-02 · via stat updates on arXiv.org

Statistics > Methodology

arXiv:2511.18065 (stat)

[Submitted on 22 Nov 2025 (v1), last revised 1 Jun 2026 (this version, v2)]

View PDF

Abstract:Out-of-Bag (OOB) estimation is the standard internal diagnostic for bootstrap-aggregated tree ensembles. Under the classical multinomial bootstrap, the number of distinct training observations in each replicate, $U_b$, is itself random, but its contribution to OOB-based variability has rarely been isolated empirically. We use Sequential Bootstrap (SB) -- a resampling scheme that holds $U_b$ at a fixed target $k_n = \lfloor 0.632 n\rfloor$ -- as a controlled perturbation of the bootstrap mechanism, and ask whether stabilizing $U_b$ produces any measurable change in OOB-based diagnostics. We reproduce Breiman's five OOB experimental families on twelve synthetic and real datasets, but unlike the three-seed presentation common in this literature, we run 100 independent random seeds with 50 internal replications per seed, enabling formal paired statistical comparison (Wilcoxon signed-rank, paired-$t$, Pitman--Morgan variance test). We report three findings. First, OOB means are essentially insensitive to stabilization of $U_b$: of 57 (experiment, dataset, metric) cells under 100 seeds, only 6 reach $p<0.05$ on the paired mean comparison, and 4 of those 6 point in the opposite direction from what a 3-seed reading would suggest. Second, a narrow but reproducible effect survives at the variance level: SB reduces the cross-seed standard deviation of node-level classification diagnostics on real datasets while slightly increasing it on synthetic ones (permutation $p=0.026$); the Vehicle dataset exhibits a 21% cross-seed sd reduction (Pitman--Morgan $p=0.017$). Third, several directional claims that appear stable across three seeds flip sign under 100-seed replication, illustrating the cost of underpowered replication protocols. We therefore treat SB as a diagnostic tool for probing the distinct-sample-count term in the variance of OOB estimators, not as an alternative to the classical bootstrap.
Comments: 22 pages, 9 tables, 1 appendix. v2: replication budget extended from 3 to 100 seeds; statistical analyses re-derived under cross-seed paired tests; Section 5 entirely rewritten; new Section 6.3 and Appendix A document the 3-seed vs 100-seed comparison. Code and data: this https URL
Subjects: Methodology (stat.ME)
MSC classes: 62G09
Cite as: arXiv:2511.18065 [stat.ME]
  (or arXiv:2511.18065v2 [stat.ME] for this version)
  https://doi.org/10.48550/arXiv.2511.18065

arXiv-issued DOI via DataCite

Submission history

From: Cheng Peng [view email]
[v1] Sat, 22 Nov 2025 13:56:50 UTC (113 KB)
[v2] Mon, 1 Jun 2026 06:15:20 UTC (133 KB)

Current browse context:

stat.ME

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。