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

Cyberwarzone
Cyberwarzone
T
The Blog of Author Tim Ferriss
人人都是产品经理
人人都是产品经理
博客园 - 叶小钗
博客园_首页
量子位
B
Blog RSS Feed
H
Help Net Security
aimingoo的专栏
aimingoo的专栏
F
Fortinet All Blogs
D
DataBreaches.Net
云风的 BLOG
云风的 BLOG
罗磊的独立博客
K
Kaspersky official blog
S
Securelist
C
Cyber Attacks, Cyber Crime and Cyber Security
P
Palo Alto Networks Blog
I
Intezer
Know Your Adversary
Know Your Adversary
S
Security Affairs
B
Blog
Engineering at Meta
Engineering at Meta
Recent Commits to openclaw:main
Recent Commits to openclaw:main
G
GRAHAM CLULEY
T
The Exploit Database - CXSecurity.com
L
LINUX DO - 热门话题
T
Threat Research - Cisco Blogs
Threat Intelligence Blog | Flashpoint
Threat Intelligence Blog | Flashpoint
P
Privacy International News Feed
Cisco Talos Blog
Cisco Talos Blog
T
Tor Project blog
Scott Helme
Scott Helme
Simon Willison's Weblog
Simon Willison's Weblog
Help Net Security
Help Net Security
A
Arctic Wolf
NISL@THU
NISL@THU
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
IT之家
IT之家
爱范儿
爱范儿
有赞技术团队
有赞技术团队
cs.AI updates on arXiv.org
cs.AI updates on arXiv.org
V
Vulnerabilities – Threatpost
The Hacker News
The Hacker News
博客园 - 聂微东
I
InfoQ
Schneier on Security
Schneier on Security
Recent Announcements
Recent Announcements
GbyAI
GbyAI
D
Darknet – Hacking Tools, Hacker News & Cyber Security
小众软件
小众软件

stat updates on arXiv.org

Riemannian Archetypal Analysis: Interpretable non-linear data analysis on deformed star distributions Repeated Sequences Reveal Gaps between Large Language Models and Natural Language Characterizing the Representational Capacity of Neural Processes A lift for input-convex neural network training Private Adaptive Covariance Estimation via Gaussian Graphical Models CurveRL: Principled Distribution-Aware Context Reweighting for LLM Reasoning Assessing the Operational Viability of Foundation Models for Time Series Forecasting The Normalized Maximum Likelihood for Regular Non-Smooth Models: Measure-Theoretic Foundations and Geometric Sampling Measuring Alignment-Induced Activation Shifts Correctly: A Template-Controlled Difference-in-Differences Protocol Feature Learning in Wide Neural Networks under $μ$P: Identifiability and Sparse-Dictionary Decomposition of the Mean-Field Limit Cross-Domain Energy-Guided Diffusion Generation for Off-Dynamics Reinforcement Learning Quaternion Self-Attention with Shared Scores Inference-Time Alignment of Diffusion Models via Trust-Region Iterative Twisted Sequential Monte Carlo Learning Treatment Effects during Resource Allocation via Priority-Queue Randomization Multi-Objective Learning for Diffusion Models: A Statistical Theory under Semi-Supervised Learning On the Epistemic Uncertainty of Overparametrized Neural Networks UWM-JEPA: Predictive World Models That Imagine in Belief Space Spiking the training data to correct for test set contamination Courtroom Analogy: New Perspective on Uncertainty-Aware Classification Efficient Benchmarking Is Just Feature Selection and Multiple Regression The Behavioral Credibility Trilemma: When Calibrated Autonomy Becomes Impossible Mapping the Schedule x Bit-Width Boundary in Sub-100M Quantisation-Aware Training On the Benefits of Free Exploration for Regret Minimization in Multi-Armed Bandits Deployment-complete benchmarking Goal-driven Bayesian Optimal Experimental Design for Robust Decision-Making Under Model Uncertainty Optimal Non-Asymptotic Edgeworth Expansions for Multivariate Neural Network Outputs Causality as the Statistical Conscience of Artificial Intelligence: From Pearl's Ladder to Trustworthy Machines Detecting Metastable Basins in High Dimensions via Marginal Trajectory Distribution Discrimination Distributionally Robust Transfer Learning with Structurally Missing Covariates, with Application to Cross-National Cardiac Arrest Prediction MEDAL: Manifold Embedding Distillation via Autoencoder Learning Lean Formalization of Generalization Error Bound by Rademacher Complexity and Dudley's Entropy Integral Multicalibration Boosting: Theory, Convergence, and Transferability Clustering based on Stochastic Dominance with application for risk averters and risk seekers Physen-Noise2Noise: Physics-Guided Self-Supervised Defocus Deblurring with Bias Correction under Low-Light Conditions Affinity Graph Connectivity in Convex Clustering On the Sample Complexity of Robust Binary Hypothesis Testing How Neural Reward Models Learn Features for Policy Optimization: A Single-Index Analysis Estimating Mixture Distributions via Stochastic Mirror Descent Multimodality Stacking with Blockwise missing values and application to the PIONeeR biomarkers study for prediction of resistance to immunotherapy Counterfactually Safe Reinforcement Learning Rejoinder: The ICML 2023 Ranking Experiment: Examining Author Self-Assessment in ML/AI Peer Review HawkesLLM: Semantic Uncertainty Propagation in Agentic Text Simulation When Is Next-Token Prediction Useful? Marginalization, Ergodicity, Mixture Identifiability, Local Sufficiency, RAG, Tools, and Programming Approximate Machine Unlearning through Manifold Representation Forgetting Guided by Self Mode Connectivity Human-Centered Learning Mechanics: A Dynamical Framework for Entropy-Regulated Representation Learning Anytime Training with Schedule-Free Spectral Optimization Robust OT-Guided Generative Residual Domain Adaptation for Bike-Sharing Demand Prediction under Temporal Domain Shift Any-Dimensional Invariant Universality Understanding and Improving Noisy Embedding Techniques in Instruction Finetuning KAPLAN: Kolmogorov-Arnold Prognostic Learnable Activation Networks for Survival Analysis Instance-Optimal Estimation with Multiple LLM Judges on a Budget Optimal Dimension-Free Sampling for Regularized Classification Entrywise Error Bounds for Spectral Ranking with Semi-Random Adversaries Training-Free Looped Transformers Diffusion-based Denoising Beats Vanilla Score Matching in Parameter Estimation: A Theoretical Explanation Uncertainty-aware classification and triage of structural heart disease using electrocardiography and echocardiography metrics LLM Sparsity Prior for Robust Feature Selection Operationalizing Individual Fairness via Gradient Descent and Bradley-Terry Models Entropy Equivalence Testing Coupled Training with Privileged Information and Unlabeled Data Asymmetric Scaling Laws from Sparse Features Dirichlet-Based Monte Carlo Dropout for Uncertainty Estimation in Neural Networks Learning Kernel-Based MDPs from Episodic Preferential Feedback Move on Muon : A Hamiltonian probability gradient flow perspective of Muon optimizer On the Stability of Spherical Hellinger-Kantorovich Flows and Their Implications for Differential Privacy Causal Additive Models with Unobserved Causal Paths and Backdoor Paths Are Targeted Data Poisoning Attacks as Effective as We Think? Near-Optimal Private Linear Regression via Iterative Hessian Mixing Certified Per-Instance Unlearning Using Individual Sensitivity Bounds Vecchia-Inducing-Points Full-Scale Approximations for Gaussian Processes Decomposition-Based Modular Conformal Prediction for Two-Stage Modeling Online Partitioned Local Depth for semi-supervised applications Amortized Simulation-Based Inference in Generalized Bayes via Neural Posterior Estimation Online monotone density estimation and log-optimal calibration Linear Regression with Unknown Truncation Beyond Gaussian Features Order-Optimal Sequential 1-Bit Mean Estimation in General Tail Regimes Sample correlation adjustments for robust Multi-fidelity Monte Carlo under limited pilot sampling Mixture-of-Finite-Mixtures Wishart Model for Clustering Covariance Matrices with an Application to Brain Functional Connectivity A Direct Variance Estimation (DiVE) for Meta-Analysis of Median Differences Regulatory Considerations for Using Artificial Intelligence Models to Reduce Sample Sizes in Registrational Studies Generalized Rank Regression Generalized Stochastic Approximation of the Log-Likelihood Ratio for Robust Sequential Change-Point Detection Concomitant DAG Learning: On the Roles of Noise Adaptivity, Sparsity, and Non-negativity The frame problem in quantitative practice: ontological uncertainty and epistemic humility in an age of automated inference Directional subset simulation method for reliability analysis A note on closed-form solutions for estimating sample size when externally validating a binary prediction model based on $C$-statistic precision Joint Estimation of Marginal and Heterogeneous Treatment Effects Trajectory-Oriented Optimization Via Adaptive Thompson Sampling And Grid Refinement: A Tutorial With The ADAPTIVE\_TS Package Global Sensitivity Analysis: a novel generation of mighty estimators based on rank statistics Joint Bayesian models for validating spatial health-event databases against a gold standard: separating global and local discrepancies Anticipating Continued Global Fertility Decline via Neural Forecasting Detecting and Correcting Sample-by-Sample Scale Distortion in RNA Sequencing Data StanBKT: Rethinking Parameter Estimation in Bayesian Knowledge Tracing Mode-Shape Expansion Using Physics-Constrained Gaussian Process Regression Convex Hybrid Modeling: An Operator-Based Approach Fundamental Bounds and Efficient Estimation for Dead-Time-Constrained Event Detection, with Application to Single-Photon Lidar Diffusion Fluid Antenna Systems for Resilient ISAC Faster Hamiltonian Monte Carlo by Learning Leapfrog Scale: a self-calibrated randomized solution Joint Object Tracking and Intent Recognition Bayesian High-dimensional Grouped-regression using Sparse Projection-posterior
Bernoulli amputation
[Submitted on 26 Jul 2024 (v1), last revised 11 Jun 2026 (this v · 2026-06-12 · via stat updates on arXiv.org
arXiv:2407.18572v3 Announce Type: replace-cross Abstract: A novel, stochastic approach to amputation, the pro…