random matrix theory

EigenShield: Inference-Time, Model-Agnostic Jailbreaking Defense via Causal Subspace Filtering AAAI 2026 Eigen-Spike Emergence and Quadratic Equivalents for Conjugate Kernels on Nonlinearly Separable Data COLT 2026 Transfer Learning via Regularized Random-effects Linear Discriminant Analysis JMLR 2026 A Novel Convex Gaussian Min Max Theorem for Repeated Features AISTATS 2025 A Random Matrix Approach to Low-Multilinear-Rank Tensor Approximation JMLR 2025 A Random Matrix Theory Perspective on the Spectrum of Learned Features and Asymptotic Generalization Capabilities AISTATS 2025 Asymptotics of Linear Regression with Linearly Dependent Data L4DC 2025 Bayesian Treatment of the Spectrum of the Empirical Kernel in (Sub)Linear-Width Neural Networks ICLR 2025 Maximizing the Potential of Synthetic Data: Insights from Random Matrix Theory ICLR 2025 Mind the Gap: a Spectral Analysis of Rank Collapse and Signal Propagation in Attention Layers ICML 2025 Newton Meets Marchenko-Pastur: Massively Parallel Second-Order Optimization with Hessian Sketching and Debiasing ICLR 2025 Precise High-Dimensional Asymptotics for Quantifying Heterogeneous Transfers JMLR 2025 Universality of Kernel Random Matrices and Kernel Regression in the Quadratic Regime JMLR 2025 Weight matrices compression based on PDB model in deep neural networks ICML 2025 Analysing Multi-Task Regression via Random Matrix Theory with Application to Time Series Forecasting NeurIPS 2024 Asymptotically Free Sketched Ridge Ensembles: Risks, Cross-Validation, and Tuning ICLR 2024 Distributed Least Squares in Small Space via Sketching and Bias Reduction NeurIPS 2024 Double-Descent Curves in Neural Networks: A New Perspective Using Gaussian Processes AAAI 2024 Entrywise error bounds for low-rank approximations of kernel matrices NeurIPS 2024 Matrix Denoising with Doubly Heteroscedastic Noise: Fundamental Limits and Optimal Spectral Methods NeurIPS 2024 Near-Interpolators: Rapid Norm Growth and the Trade-Off between Interpolation and Generalization AISTATS 2024 No Double Descent in Principal Component Regression: A High-Dimensional Analysis ICML 2024 Random Fully Connected Neural Networks as Perturbatively Solvable Hierarchies JMLR 2024 Random matrix theory improved Fréchet mean of symmetric positive definite matrices ICML 2024 Understanding Inverse Scaling and Emergence in Multitask Representation Learning AISTATS 2024