8 papers
Manifold Dimension Estimation via Local Graph Structure
Zelong Bi, Pierre Lafaye de Micheaux
Most existing manifold dimension estimators rely on the assumption that the underlying manifold is locally flat within the neighborhoods under consideration. More recently, curvatu…
Deep-testing: the case of dependence detection
Gery Geenens, Pierre Lafaye de Micheaux, Ivan Muyun Zou
Deep learning methods have proved highly effective for classification and image recognition problems. In this paper, we ask whether this success can be transferred to hypothesis te…
tBayes-MICE: A Bayesian Approach to Multiple Imputation for Time Series Data
Amuche Ibenegbu, Pierre Lafaye de Micheaux, Rohitash Chandra
Time-series analysis is often affected by missing data, a common problem across several fields, including healthcare and environmental monitoring. Multiple Imputation by Chained Eq…
A Survey and Comparative Evaluation of Intrinsic Dimension Estimators under the Manifold Hypothesis
Zelong Bi, Pierre Lafaye de Micheaux
The manifold hypothesis suggests that high-dimensional data often lie on or near a low-dimensional manifold. Estimating the dimension of this manifold is essential for leveraging i…
Adaptive Heavy-Tailed Stochastic Gradient Descent
Bodu Gong, Gustavo Enrique Batista, Pierre Lafaye de Micheaux
In the era of large-scale neural network models, optimization algorithms often struggle with generalization due to an overreliance on training loss. One key insight widely accepted…
Generative Flexible Latent Structure Regression (GFLSR) model
Clara Grazian, Qian Jin, Pierre Lafaye De Micheaux
Latent structure methods, specifically linear continuous latent structure methods, are a type of fundamental statistical learning strategy. They are widely used for dimension reduc…