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.
Study on Gaussian ensemble of matrix products with mixed moments computed.
HD algorithm simulates dynamics on random matrix ensembles without generating full matrices.
Random matrix ensembles yield uniform distributions on manifolds.
Method detects neural network equivalence via matrix ensembles and spectral analysis.
Extends spectral number variance convergence to random matrix ensembles for twisted Laplacians.
Paper proposes a new method to improve clustering ensemble performance.
New matrix ensembles better match deep neural network spectral densities.
In this paper we examine the effect of applying ensemble learning to the performance of collaborative filtering methods. We present several systematic approaches for generating an ensemble of collaborative filtering models based on a single collaborative filtering algorithm (single-model or homogeneous ensemble). We pr…
We generalize the recently discovered relationship between JT gravity and double-scaled random matrix theory to the case that the boundary theory may have time-reversal symmetry and may have fermions with or without supersymmetry. The matching between variants of JT gravity and matrix ensembles depends on the assumed s…
In this work a novel method to quantify spectral ergodicity for random matrices is presented. The new methodology combines approaches rooted in the metrics of Thirumalai-Mountain (TM) and Kullbach-Leibler (KL) divergence. The method is applied to a general study of deep and recurrent neural networks via the analysis of…
In this paper, we solve a semi-supervised regression problem. Due to the lack of knowledge about the data structure and the presence of random noise, the considered data model is uncertain. We propose a method which combines graph Laplacian regularization and cluster ensemble methodologies. The co-association matrix of…
SPQR improves Q-ensemble diversity in reinforcement learning.
We use a cluster ensemble to determine the number of clusters, k, in a group of data. A consensus similarity matrix is formed from the ensemble using multiple algorithms and several values for k. A random walk is induced on the graph defined by the consensus matrix and the eigenvalues of the associated transition proba…
Paper solves a key problem in learning from high-dimensional covariance matrices.
The paper analyzes bootstrap ensemble classifiers in high-dimensional settings.
BatchEnsemble reduces ensemble costs by 3X in training and testing.
Sharp threshold found for Frechet mean of inhomogeneous graphs.
The paper analyzes an ensemble of randomly projected linear discriminants for high-dimensional data.
Ensemble clustering has been a popular research topic in data mining and machine learning. Despite its significant progress in recent years, there are still two challenging issues in the current ensemble clustering research. First, most of the existing algorithms tend to investigate the ensemble information at the obje…
A central problem of random matrix theory is to understand the eigenvalues of spiked random matrix models, introduced by Johnstone, in which a prominent eigenvector (or "spike") is planted into a random matrix. These distributions form natural statistical models for principal component analysis (PCA) problems throughou…
The study investigates kernel-target alignment in tree ensemble kernels.
We estimate generic statistical properties of a structural credit risk model by considering an ensemble of correlation matrices. This ensemble is set up by Random Matrix Theory. We demonstrate analytically that the presence of correlations severely limits the effect of diversification in a credit portfolio if the corre…
We improve prediction risk estimation for large datasets using sketching and ridge regression.
Study finds Calabi-Yau models' operator spectra match random matrix theory.
Expected centre of mass for random embeddings is constant.
In this article, the logic rule ensembles approach to supervised learning is applied to the unsupervised or semi-supervised clustering. Logic rules which were obtained by combining simple conjunctive rules are used to partition the input space and an ensemble of these rules is used to define a similarity matrix. Simila…
Solves weakly supervised regression using low-rank approximations and manifold regularization.
Boosting Nyström improves accuracy of matrix approximations.
In a broad range of classification and decision making problems, one is given the advice or predictions of several classifiers, of unknown reliability, over multiple questions or queries. This scenario is different from the standard supervised setting, where each classifier accuracy can be assessed using available labe…
New distributed EnKF method for non-sequential assimilation of large datasets.
Study shows energy levels on hyperbolic surfaces follow GOE fluctuations.
Topic models can provide us with an insight into the underlying latent structure of a large corpus of documents. A range of methods have been proposed in the literature, including probabilistic topic models and techniques based on matrix factorization. However, in both cases, standard implementations rely on stochastic…
A central problem of random matrix theory is to understand the eigenvalues of spiked random matrix models, in which a prominent eigenvector is planted into a random matrix. These distributions form natural statistical models for principal component analysis (PCA) problems throughout the sciences. Baik, Ben Arous and Pé…
Random Matrix Theory explains loss surface Hessians in neural networks.
The paper explores how multiway data from PDEs can be accurately tracked using EnKF with specific covariance and precision estimators.
This paper focuses on scalability and robustness of spectral clustering for extremely large-scale datasets with limited resources. Two novel algorithms are proposed, namely, ultra-scalable spectral clustering (U-SPEC) and ultra-scalable ensemble clustering (U-SENC). In U-SPEC, a hybrid representative selection strategy…
Unified treatment of eigenvalue processes using Riemannian geometry.
We provide a method to prepare covariance matrices for quantum datasets.
We define a random-matrix ensemble given by the infinite-time covariance matrices of Ornstein-Uhlenbeck processes at different temperatures coupled by a Gaussian symmetric matrix. The spectral properties of this ensemble are shown to be in qualitative agreement with some stylized facts of financial markets. Through the…
Enhances influence functions for deep models without costly Hessian inversion.
Tree ensembles like RF and GBT can be seen as kernels, improving regression and classification performance.
The paper develops a new algorithm for RBMs using dynamical mean-field theory.
Ensemble methods that average over a collection of independent predictors that are each limited to a subsampling of both the examples and features of the training data command a significant presence in machine learning, such as the ever-popular random forest, yet the nature of the subsampling effect, particularly of th…
clusterBMA combines clustering results from multiple models using Bayesian model averaging.
The rising interest in pattern recognition and data analytics has spurred the development of innovative machine learning algorithms and tools. However, as each algorithm has its strengths and limitations, one is motivated to judiciously fuse multiple algorithms in order to find the "best" performing one, for a given da…
Corrects GCV for inconsistent risk estimation in finite ensembles of penalized estimators.
New methods improve tree ensemble models by compressing them while maintaining accuracy.
Subspace clustering is the unsupervised grouping of points lying near a union of low-dimensional linear subspaces. Algorithms based directly on geometric properties of such data tend to either provide poor empirical performance, lack theoretical guarantees, or depend heavily on their initialization. We present a novel …