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Found 3 papers, 0 papers with code
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.
