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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,695 papers · 148 categories

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6 results for “Eigen-spectrum”

Spectral Clustering using Eigenspectrum Shape Based Nystrom Samplingcs.LG

This paper proposes a new Nystrom-based clustering algorithm for large-scale data.

problem Spectral clustering's high computational complexity for large-scale data.

method Centroid Minimum Sum of Squared Similarities (CMS3) sampling procedure with eigen spectrum shape heuristic.

result Competitive low-rank approximations in test datasets compared to state-of-the-art methods.

spectral clustering Nystrom method low-rank approximation eigen spectrum large-scale data

2020-07-21abs ↗pdf ↗

Kernel ridge regression under power-law data: spectrum and generalizationstat.ML

Study on KRR with power-law data, showing better sample complexity.

problem High-dimensional kernel ridge regression with anisotropic power-law covariance.

method Explicit characterization of kernel spectrum and asymptotic analysis of excess risk.

result Sample complexity is governed by effective dimension, not ambient dimension.

kernel ridge regression power-law data excess risk effective dimension

2025-10-06abs ↗pdf ↗

Randomized K-FACs: Speeding up K-FAC with Randomized Numerical Linear Algebracs.LG

This paper speeds up K-FAC for deep learning by focusing on only a few eigen-modes.

problem Time-consuming computation of Kronecker factors in K-FAC for large layers.

method Theoretical analysis and randomized numerical linear algebra to approximate eigen-spectrum decay.

result Reduces time complexity from cubic to quadratic in layer width, improving efficiency.

K-FAC Natural Gradient Deep Learning Randomized Numerical Linear Algebra Eigen-spectrum

2022-06-30abs ↗pdf ↗

Regret minimization in Linear Bandits with offline data via extended D-optimal explorationcs.LG

Algorithm reduces online regret by leveraging offline data in linear bandits.

problem Online regret minimization in linear bandits with offline data.

method OOPE algorithm using extended D-optimal design.

result Substantial reduction in online regret compared to prior work.

regret minimization linear bandits offline data Frank-Wolfe approximation D-optimal design

2025-08-11abs ↗pdf ↗

Graph Spectral Feature Learning for Mixed Data of Categorical and Numerical Typestat.ML

A novel graph spectral method for mixed categorical and numerical data.

problem Feature learning for mixed data types (numerical and categorical).

method Graph spectral decomposition of the graph Laplacian to model probabilistic dependence structure.

result Increased separability and clusterability of observations in the transformed feature space.

graph spectral learning mixed data types feature transformation unsupervised learning categorical and numerical

2020-05-06abs ↗pdf ↗

Relationship between manifold smoothness and adversarial vulnerability in deep learning with local errorscs.LG

Study reveals how neural network smoothness affects their vulnerability to adversarial attacks.

problem Understanding adversarial vulnerability in deep learning networks.

method Analysis of manifold smoothness and generalization capability of deep neural networks trained with local errors.

result High generalization accuracy requires a fast power-law decay of eigen-spectrum of hidden representations.

deep learning adversarial vulnerability manifold smoothness generalization neural networks

2020-07-04abs ↗pdf ↗