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Found 9 papers, 1 papers with code
Interpolating Discriminant Functions in High-Dimensional Gaussian Latent Mixtures
A generalized least squares estimator is used to estimate the direction of the optimal separating hyperplane.
Optimal Discriminant Analysis in High-Dimensional Latent Factor Models
In high-dimensional classification problems, a commonly used approach is to first project the high-dimensional features into a lower dimensional space, and base the classification on the resulting lower dimensional projections.
Likelihood estimation of sparse topic distributions in topic models and its applications to Wasserstein document distance calculations
When $A$ is unknown, we estimate $T$ by optimizing the likelihood function corresponding to a plug in, generic, estimator $\hat{A}$ of $A$.
Prediction in latent factor regression: Adaptive PCR and beyond
Our primary contribution is in establishing finite sample risk bounds for prediction with the ubiquitous Principal Component Regression (PCR) method, under the factor regression model, with the number of principal components adaptively selected from the data -- a form of theoretical guarantee that is surprisingly lacking from the PCR literature.
Interpolating Predictors in High-Dimensional Factor Regression
If the effective rank of the covariance matrix $\Sigma$ of the $p$ regression features is much larger than the sample size $n$, we show that the min-norm interpolating predictor is not desirable, as its risk approaches the risk of trivially predicting the response by 0.
Optimal estimation of sparse topic models
We derive a finite sample upper bound for our estimator, and show that it matches the minimax lower bound in many scenarios.
A fast algorithm with minimax optimal guarantees for topic models with an unknown number of topics
We propose a new method of estimation in topic models, that is not a variation on the existing simplex finding algorithms, and that estimates the number of topics K from the observed data.
Adaptive Estimation in Structured Factor Models with Applications to Overlapping Clustering
This work introduces a novel estimation method, called LOVE, of the entries and structure of a loading matrix A in a sparse latent factor model X = AZ + E, for an observable random vector X in Rp, with correlated unobservable factors Z \in RK, with K unknown, and independent noise E. Each row of A is scaled and sparse.
Adaptive estimation of the copula correlation matrix for semiparametric elliptical copulas
Then we study a factor model of $\Sigma$, for which we propose a refined estimator $\widetilde{\Sigma}$ by fitting a low-rank matrix plus a diagonal matrix to $\hat{\Sigma}$ using least squares with a nuclear norm penalty on the low-rank matrix.
