Nested Sampling: A Critical and Comprehensive Theoretical Guide - 惯性聚合

推荐订阅源

Proofpoint News Feed

The Hacker News

Google Developers Blog

Schneier on Security

freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More

Security Archives - TechRepublic

博客园 - Franky

Recent Announcements

Hacker News - Newest: "LLM"

Kaspersky official blog

Engineering at Meta

Java Code Geeks

Google Online Security Blog

Last Week in AI

Vulnerabilities – Threatpost

News and Events Feed by Topic

cs.CL updates on arXiv.org

Y Combinator Blog

博客园 - 【当耐特】

Hacker News: Ask HN

Tor Project blog

Apple Machine Learning Research

Microsoft Security Blog

Exploit-DB.com RSS Feed

Security Affairs

About on SuperTechFans

Darknet – Hacking Tools, Hacker News & Cyber Security

博客园 - 聂微东

奇客Solidot–传递最新科技情报

Check Point Blog

宝玉的分享

Visual Studio Blog

The Blog of Author Tim Ferriss

stat.ML updates on arXiv.org

A Polyak-Ruppert Central Limit Theorem for SA-Adam with Momentum and Non-Convergent Adaptive Preconditioning Non-asymptotic Tail Bounds for the Kostlan--Shub--Smale Field: Tensor PCA and Spherical $k$-Spin Complexity Conformal Prediction Intervals with Tail-Specific Guarantees Another Look at Log-PCA for Probability Measures: A Dynamical Formulation and Statistical Convergence MA-SBI: Misspecification-Aware Simulation-Based Inference via Side-Channel Guidance Policy Regret for Embedding Model Routing: Contextual Bandits with Low-Rank Experts LLMs on Tabular Data with Limited Semantics: Evidence from Industrial Car Retrofit Prediction Service-Induced Congestion in Memory-Constrained LLM Serving Proximal Policy Optimization for Amortized Discrete Sampling Topological Flow Matching Attention is Just Another Name for Coupling?: A Fast-Slow ODE Perspective on Hierarchical Pretraining Phantoms and Disclosures: a Causal Framework for Auditing Synthetic Data Learning a Sampling-Free Variational DNN Plugin from Tiny Training Sets to Refine OOD Segmentation With Uncertainty Estimation Exact Posterior Score Estimation for Solving Linear Inverse Problems Bridging data-driven priors via the score function for posterior sampling -- Comparative review and experimental study Audited Conformal Prediction for Classification under Unknown Distribution Shift A Koopman-PINN Framework for Epidemic Models: Parameter Inference and Forecasting Conformal Candidate Certification for Offline Model-Based Optimization Finite Resources False Discovery Rate Control in Structured Hypothesis Spaces The Reverse Telescoping Coordinate System for Positive Definite Matrices: Geometry, Computation, and Generative Modeling Structured Nonparametric Variational Inference for Dependent Latent Modeling Ricci-Filtration: Boosting Retrieval-Augmented Generation Reranker to Query-Answer Tasks by Discrete Ricci Flow Phase Transition in Convex Relaxations for Graph Alignment Information Gap and Feasibility-Aware Inference in Binomial Logistic Mixtures Stochastic trace estimation with tensor train random vectors Score-Based Martingale Posteriors for Deep Neural Networks Amortized mean-shift interacting particles Spectral Adaptive Conformal Prediction for Structured Non-Exchangeable Data Representation Costs in Data Science: Foundations and the Quasi-Banach Spaces of Deep Neural Networks PromptShift-CRC: Drift-Aware Conformal Risk Control for Foundation Models Under Prompt and Domain Shift Closing the Approximation Gap in Simulation-free Latent SDEs On the Geometry of Separation in Finite Gaussian Mixtures Generative Modeling on Metric Graphs via Neural Optimal Transport Diffusion Flow Matching: Dimension-Improved KL Bounds and Wasserstein Guarantees Spectral Sparsification of Laplacian-Constrained Gaussian and Hüsler-Reiss Graphical Models Optimal Multiscale Learning of Linear Operators A nonparametric two-sample test using a parametric integral probability metric Sobolev Approximation by Fixed-Size Neural Networks with Arbitrary Accuracy Dynestyx: A Probabilistic Programming Library for Dynamical Systems Learning the Geometry of Data: A Mathematical Review of Shape Space Analysis Learning Topological Representations for Molecular Dynamics Event Generation with Parallel Langevin Sampling and Learned Stein Diagnostics PHINN: Persistent Homology Inspired Neural Network for Rare-Event Time Series Generation A Decision-Theoretic View of Test-Time Training: When, How Far, and Which Directions to Adapt The Data Manifold under the Microscope The limits of interpretability in multiple linear regression Stop the Sampler! Classifier-Based Adaptive Stopping for Sampling Kernels One-Step Generalization Ratio Guided Optimization for Domain Generalization Scalable and Interpretable Representation Alignment with Ordinal Similarity Neural Bayesian Anomaly Mitigation: A Robust Loss that Doubles as an Unsupervised Contamination Classifier Generative Predictive Distributions for Time Series Functional Gradient Descent with Adaptive Representations Filtered Conformal Ellipsoids for Graph-Native Time Series Information Leakage Detection through Approximate Bayes-optimal Prediction TrIM: Transformed Iterative Mondrian Forests for Gradient-based Dimension Reduction and High-Dimensional Regression Optimality in importance sampling: a gentle survey Discrimination-free Insurance Pricing with Privatized Sensitive Attributes Enhancing Visual Feature Attribution via Weighted Integrated Gradients Optimal structure learning and conditional independence testing Kernel Two-Sample Testing via Directional Components Analysis Q-Learning with Fine-Grained Gap-Dependent Regret In-Context Learning Is Provably Bayesian Inference: A Generalization Theory for Meta-Learning deFOREST: Fusing Optical and Radar satellite data for Enhanced Sensing of Tree-loss Matching correlated VAR time series Drivers, Receivers, and Dynamic Linkages: The Directed Structure of SDG Interdependence, 2000--2024 Best Arm Identification with Minimal Regret Constraining the outputs of ReLU neural networks On the Benefits of Weight Normalization for Overparameterized Matrix Sensing Localized Kernel Projection Outlyingness: A Two-Stage Approach for Multi-Modal Outlier Detection Random Gradient-Free Optimization in Infinite Dimensional Spaces Forecasting the U.S. Treasury Yield Curve: A Distributionally Robust Machine Learning Approach for Interest Rate Risk Management Fast Non-Episodic Finite-Horizon RL with K-Step Lookahead Thresholding Sharp analysis of linear ensemble sampling CADO: From Imitation to Cost Minimization for Heatmap-based Solvers in Combinatorial Optimization Sharp One-Dimensional Sub-Gaussian Comparison in Convex Order Lyapunov-Based Sample Complexity Analysis for Weakly-Coupled MDPs Can Neural Networks Achieve Optimal Computational-statistical Tradeoff? An Analysis on Single-Index Model Relational Structural Causal Models Simultaneous Latent Budget Trees for Stratified Classification Asymptotically Optimal Sequential Testing with Markovian Data Eigen-Spike Emergence and Quadratic Equivalents for Conjugate Kernels on Nonlinearly Separable Data High-Dimensional Robust Change-Point Detection via Angular Kernel Statistics GradPower: Powering Gradients for Faster Language Model Pre-Training Improved Baselines with Representation Autoencoders Adaptive Kernel Density Estimation with Pre-training Imbalanced Classification under Capacity Constraints Stochastic Schrödinger Diffusion Models for Pure-State Ensemble Generation Conditional Score-Based Modeling of Effective Langevin Dynamics A Unified Definition of Hallucination: It's The World Model, Stupid! Branching Flows: Discrete, Continuous, and Manifold Flow Matching with Splits and Deletions Semantic Editing with Coupled Stochastic Differential Equations Prob-GParareal: A Probabilistic Numerical Parallel-in-Time Solver for Differential Equations Optimal Transport for Machine Learners Optimizing LLM Inference: Fluid-Guided Online Scheduling with Memory Constraints Golden Ratio Weighting Prevents Model Collapse Computational Safety for Generative AI: A Hypothesis Testing Perspective DAL: A Practical Prior-Free Black-Box Framework for Piecewise Stationary Bandits Conditional Local Importance by Quantile Expectations Variational autoencoders with latent high-dimensional steady geometric flows for dynamics Canonical Variates in Wasserstein Metric Space

Nested Sampling: A Critical and Comprehensive Theoretical Guide

[Submitted on 16 Jun 2026] · 2026-06-17 · via stat.ML updates on arXiv.org

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。