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.
JSRT improves regression tree performance by incorporating global node information.
Proposes a method to stabilize Black Box Variational Inference using the James-Stein estimator.
Estimates true Sharpe ratio of selected assets with various methods.
We present a procedure for effective estimation of entropy and mutual information from small-sample data, and apply it to the problem of inferring high-dimensional gene association networks. Specifically, we develop a James-Stein-type shrinkage estimator, resulting in a procedure that is highly efficient statistically …
We present a multi-task learning approach to jointly estimate the means of multiple independent data sets. The proposed multi-task averaging (MTA) algorithm results in a convex combination of the single-task maximum likelihood estimates. We derive the optimal minimum risk estimator and the minimax estimator, and show t…
Improved estimator for least squares using random projections achieves smaller error.
This paper considers the problem of estimating a high-dimensional vector of parameters from a noisy observation. The noise vector is i.i.d. Gaussian with known variance. For a squared-error loss function, the James-Stein (JS) estimator is known to dominate the simple maximum-likelihood (…
Robust Bayesian models are appealing alternatives to standard models, providing protection from data that contains outliers or other departures from the model assumptions. Historically, robust models were mostly developed on a case-by-case basis; examples include robust linear regression, robust mixture models, and bur…
We revisit the problem of feature selection in linear discriminant analysis (LDA), that is, when features are correlated. First, we introduce a pooled centroids formulation of the multiclass LDA predictor function, in which the relative weights of Mahalanobis-transformed predictors are given by correlation-adjusted …
New method predicts binary matrix entries using empirical Bayes and low-rank structure.
C-SURE improves complex-valued deep learning models by shrinking estimates, outperforming MLE and SurReal.
Networks are a natural representation of complex systems across the sciences, and higher-order dependencies are central to the understanding and modeling of these systems. However, in many practical applications such as online social networks, networks are massive, dynamic, and naturally streaming, where pairwise inter…
Stein shrinkage improves BN robustness against adversarial attacks.
This paper compares methods for handling mixed-attribute data in GFMM neural networks.
Unified framework for shrinkage, thresholding, and regularization in normal mean estimation and linear regression.
Spatial statisticians and quantitative investors use the same mathematical object: a Schur complement, damped by one parameter.