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

O
OpenAI News
I
Intezer
C
CERT Recently Published Vulnerability Notes
V
Vulnerabilities – Threatpost
S
Securelist
C
Cyber Attacks, Cyber Crime and Cyber Security
G
GRAHAM CLULEY
P
Palo Alto Networks Blog
P
Privacy & Cybersecurity Law Blog
T
Tenable Blog
T
Threatpost
Latest news
Latest news
Cisco Talos Blog
Cisco Talos Blog
A
Arctic Wolf
罗
罗磊的独立博客
云风的 BLOG
云风的 BLOG
L
LangChain Blog
博
博客园 - 【当耐特】
P
Privacy International News Feed
The GitHub Blog
The GitHub Blog
Threat Intelligence Blog | Flashpoint
Threat Intelligence Blog | Flashpoint
www.infosecurity-magazine.com
www.infosecurity-magazine.com
cs.CV updates on arXiv.org
cs.CV updates on arXiv.org
T
Troy Hunt's Blog
量
量子位
Security Archives - TechRepublic
Security Archives - TechRepublic
爱范儿
爱范儿
S
Security @ Cisco Blogs
Martin Fowler
Martin Fowler
博
博客园_首页
SecWiki News
SecWiki News
Spread Privacy
Spread Privacy
I
InfoQ
博
博客园 - 司徒正美
T
Tor Project blog
cs.AI updates on arXiv.org
cs.AI updates on arXiv.org
H
Help Net Security
L
LINUX DO - 最新话题
H
Heimdal Security Blog
TaoSecurity Blog
TaoSecurity Blog
Hacker News - Newest:
Hacker News - Newest: "LLM"
Vercel News
Vercel News
Y
Y Combinator Blog
D
DataBreaches.Net
T
Threat Research - Cisco Blogs
Stack Overflow Blog
Stack Overflow Blog
C
CXSECURITY Database RSS Feed - CXSecurity.com
M
MIT News - Artificial intelligence
Recorded Future
Recorded Future
博
博客园 - 叶小钗

math.ST updates on arXiv.org

What is Learnable in Valiant's Theory of the Learnable? Learning Perturbations to Extrapolate Your LLM Byzantine-Robust Distributed Sparse Learning Revisited The Sample Complexity of Multiple Change Point Identification under Bandit Feedback A proximal gradient algorithm for composite log-concave sampling Model-based Bootstrap of Controlled Markov Chains Approximation of Maximally Monotone Operators : A Graph Convergence Perspective Posterior Contraction Rates for Sparse Kolmogorov-Arnold Networks in Anisotropic Besov Spaces MIST: Reliable Streaming Decision Trees for Online Class-Incremental Learning via McDiarmid Bound A Spectral Framework for Closed-Form Relative Density Estimation Fast Rates for Offline Contextual Bandits with Forward-KL Regularization under Single-Policy Concentrability Higher-Order Equilibrium Tracking for EM-Compressible Online Estimation Scaling Limits of Long-Context Transformers A Note on Non-Negative $L_1$-Approximating Polynomials Susceptibilities and Patterning: A Primer on Linear Response in Bayesian Learning Linear Response Estimators for Singular Statistical Models Statistical inference with belief functions: A survey Robust stochastic first order methods in heavy-tailed noise via medoid mini-batch gradient sampling Every Feedforward Neural Network Definable in an o-Minimal Structure Has Finite Sample Complexity Adaptive auditing of AI systems with anytime-valid guarantees Locally Near Optimal Piecewise Linear Regression in High Dimensions via Difference of Max-Affine Functions Risk-Controlled Post-Processing of Decision Policies Covariate Balancing and Riesz Regression Should Be Guided by the Neyman Orthogonal Score in Debiased Machine Learning A Unified Pair-GRPO Family: From Implicit to Explicit Preference Constraints for Stable and General RL Alignment Time-Inhomogeneous Preconditioned Langevin Dynamics A Fine-Grained Understanding of Uniform Convergence for Halfspaces CITE: Anytime-Valid Statistical Inference in LLM Self-Consistency Ratio-based Loss Functions Optimal Confidence Band for Kernel Gradient Flow Estimator A renormalization-group inspired lattice-based framework for piecewise generalized linear models Direct Estimation of Schrödinger Bridge Time-Series Drifts: Finite-Sample, Asymptotic, and Adaptive Guarantees Information-theoretic Limits of Learning and Estimation Adaptivity Under Realizability Constraints: Comparing In-Context and Agentic Learning Multiscale Euclidean Network Trajectories: Second-Moment Geometry, Attribution, and Change Points Causal discovery under mean independence and linearity Perturbation is All You Need for Extrapolating Language Models Realizable Bayes-Consistency for General Metric Losses Vanishing L2 regularization for the softmax Multi Armed Bandit Imbalanced Classification under Capacity Constraints Intrinsic effective sample size for manifold-valued Markov chain Monte Carlo via kernel discrepancy On the Optimal Sample Complexity of Offline Multi-Armed Bandits with KL Regularization Extrapolation in Statistical Learning with Extreme Value Theory Adaptive Estimation and Inference in Semi-parametric Heterogeneous Clustered Multitask Learning via Neyman Orthogonality Beyond ECE: Calibrated Size Ratio, Risk Assessment, and Confidence-Weighted Metrics Self-Normalized Martingales and Uniform Regret Bounds for Linear Regression Mean Testing under Truncation beyond Gaussian Decoupled Descent: Exact Test Error Tracking Via Approximate Message Passing Hyper Input Convex Neural Networks for Shape Constrained Learning and Optimal Transport Observable Neural ODEs for Identifiable Causal Forecasting in Continuous Time Elite-Driven Support Vector Machines for Classification A Limit Theory of Foundation Models: A Mathematical Approach to Understanding Emergent Intelligence and Scaling Laws Learning Curves and Benign Overfitting of Spectral Algorithms in Large Dimensions Concave Statistical Utility Maximization Bandits via Influence-Function Gradients The Sample Complexity of Multicalibration Cover meets Robbins while Betting on Bounded Data: $\ln n$ Regret and Almost Sure $\ln\ln n$ Regret Achieving the Kesten-Stigum bound in the non-uniform hypergraph stochastic block model On two ways to use determinantal point processes for Monte Carlo integration Recovery Guarantees for Continual Learning of Dependent Tasks: Memory, Data-Dependent Regularization, and Data-Dependent Weights Structural interpretability in SVMs with truncated orthogonal polynomial kernels Cloning is as Hard as Learning for Stabilizer States Ordinary Least Squares is a Special Case of Transformer Identifiability of Potentially Degenerate Gaussian Mixture Models With Piecewise Affine Mixing NetworkNet: A Deep Neural Network Approach for Random Networks with Sparse Nodal Attributes and Complex Nodal Heterogeneity ADD for Multi-Bit Image Watermarking Cost-optimal Sequential Testing via Doubly Robust Q-learning Query Lower Bounds for Diffusion Sampling Tail-Aware Information-Theoretic Generalization for RLHF and SGLD Spatio-temporal probabilistic forecast using MMAF-guided learning The Geometry of Knowing: From Possibilistic Ignorance to Probabilistic Certainty -- A Measure-Theoretic Framework for Epistemic Convergence Generalization Properties of Score-matching Diffusion Models for Intrinsically Low-dimensional Data Conformal Policy Control Continuous-time reinforcement learning: ellipticity enables model-free value function approximation High-accuracy sampling for diffusion models and log-concave distributions Analyzing Shapley Additive Explanations to Understand Anomaly Detection Algorithm Behaviors and Their Complementarity Optimal Lower Bounds for Online Multicalibration Understanding Overparametrization in Survival Models through Interpolation Eventually LIL Regret: Almost Sure $\ln\ln T$ Regret for a sub-Gaussian Mixture on Unbounded Data Limit Theorems for Stochastic Gradient Descent in High-Dimensional Single-Layer Networks Optimal In-context Adaptivity and Distributional Robustness of Transformers Don't Pass@k: A Bayesian Framework for Large Language Model Evaluation The Good, the Bad, and the Sampled: a No-Regret Approach to Safe Online Classification GOSPA and T-GOSPA quasi-metrics for evaluation of multi-object tracking algorithms A note on the unique properties of the Kullback--Leibler divergence for sampling via gradient flows Multi-Armed Bandits With Machine Learning-Generated Surrogate Rewards Efficient compression of neural networks and datasets Out-of-Distribution Generalization of In-Context Learning: A Low-Dimensional Subspace Perspective Super-fast Rates of Convergence for Neural Network Classifiers under the Hard Margin Condition Sharp Gaussian approximations for Decentralized Federated Learning Learning Operators by Regularized Stochastic Gradient Descent with Operator-valued Kernels Smoothed Analysis of Learning from Positive Samples Statistical Impossibility and Possibility of Aligning LLMs with Human Preferences: From Condorcet Paradox to Nash Equilibrium Sharp Risk Bounds for Early-Stopping in Gaussian Linear Regression Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent Copula-enhanced Vision Transformer for high myopia diagnosis through OU UWF fundus images General Frameworks for Conditional Two-Sample Testing Improved Hardness Results for Learning Intersections of Halfspaces Consistency of Lloyd's Algorithm Under Perturbations Convergence Rates for Non-Log-Concave Sampling and Log-Partition Estimation Distribution-Free Stochastic Analysis and Robust Multilevel Vector Field Anomaly Detection Efficient Parameter Estimation of Truncated Boolean Product Distributions
Analysis of the rSVDdpd Algorithm: A Robust Singular Value Decomposition Method using Density Power Divergence
Subhrajyoty Roy, Abhik Ghosh, Ayanendranath Basu · 2023-07-20 · via math.ST updates on arXiv.org

The traditional method of computing singular value decomposition (SVD) of a data matrix is based on a least squares principle, thus, is very sensitive to the presence of outliers. Hence the resulting inferences across different applications using the classical SVD are extremely degraded in the presence of data contamination (e.g., video surveillance background modelling tasks, etc.). A robust singular value decomposition method using the minimum density power divergence estimator (rSVDdpd) has been found to provide a satisfactory solution to this problem and works well in applications. For example, it provides a neat solution to the background modelling problem of video surveillance data in the presence of camera tampering. In this paper, we investigate the theoretical properties of the rSVDdpd estimator such as convergence, equivariance and consistency under reasonable assumptions. Since the dimension of the parameters, i.e., the number of singular values and the dimension of singular vectors can grow linearly with the size of the data, the usual M-estimation theory has to be suitably modified with concentration bounds to establish the asymptotic properties. We believe that we have been able to accomplish this satisfactorily in the present work. We also demonstrate the efficiency of rSVDdpd through extensive simulations.

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。