Ronan Perry
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View article: Inference on the Proportion of Variance Explained in Principal Component Analysis
Inference on the Proportion of Variance Explained in Principal Component Analysis Open
Principal component analysis (PCA) is a longstanding and well-studied approach for dimension reduction. It rests upon the assumption that the underlying signal in the data has low rank, and thus can be well-summarized using a small number …
View article: On the minimum strength of (unobserved) covariates to overturn an insignificant result
On the minimum strength of (unobserved) covariates to overturn an insignificant result Open
We study conditions under which the addition of variables to a regression equation can turn a previously statistically insignificant result into a significant one. Specifically, we characterize the minimum strength of association required …
View article: Infer-and-widen, or not?
Infer-and-widen, or not? Open
In recent years, there has been substantial interest in the task of selective inference: inference on a parameter that is selected from the data. Many of the existing proposals fall into what we refer to as the \emph{infer-and-widen} frame…
View article: Manifold Oblique Random Forests: Towards Closing the Gap on Convolutional Deep Networks
Manifold Oblique Random Forests: Towards Closing the Gap on Convolutional Deep Networks Open
Decision forests, in particular random forests and gradient boosting trees have demonstrated state-of-the-art accuracy compared to other methods in many supervised learning scenarios. Forests dominate other methods in tabular data, that is…
View article: Causal Discovery in Heterogeneous Environments Under the Sparse Mechanism Shift Hypothesis
Causal Discovery in Heterogeneous Environments Under the Sparse Mechanism Shift Hypothesis Open
Machine learning approaches commonly rely on the assumption of independent and identically distributed (i.i.d.) data. In reality, however, this assumption is almost always violated due to distribution shifts between environments. Although …
View article: neurodata/graspy: GraSPy 0.3
neurodata/graspy: GraSPy 0.3 Open
Announcement: GraSPy 0.3 We're happy to announce the release of GraSPy 0.3! GraSPy is a Python package for understanding the properties of random graphs that arise from modern datasets, such as social networks and brain networks. For more …
View article: mvlearn: Multiview Machine Learning in Python
mvlearn: Multiview Machine Learning in Python Open
As data are generated more and more from multiple disparate sources, multiview data sets, where each sample has features in distinct views, have ballooned in recent years. However, no comprehensive package exists that enables non-specialis…
View article: Nonpar MANOVA via Independence Testing
Nonpar MANOVA via Independence Testing Open
The $k$-sample testing problem tests whether or not $k$ groups of data points are sampled from the same distribution. Multivariate analysis of variance (MANOVA) is currently the gold standard for $k$-sample testing but makes strong, often …
View article: Universally Consistent K-Sample Tests via Dependence Measures
Universally Consistent K-Sample Tests via Dependence Measures Open
The K-sample testing problem involves determining whether K groups of data points are each drawn from the same distribution. Analysis of variance is arguably the most classical method to test mean differences, along with several recent met…
View article: Nonparametric MANOVA via Independence Testing
Nonparametric MANOVA via Independence Testing Open
The $k$-sample testing problem tests whether or not $k$ groups of data points are sampled from the same distribution. Multivariate analysis of variance (MANOVA) is currently the gold standard for $k$-sample testing but makes strong, often …
View article: Manifold Oblique Random Forests: Towards Closing the Gap on Convolutional Deep Networks
Manifold Oblique Random Forests: Towards Closing the Gap on Convolutional Deep Networks Open
Decision forests (Forests), in particular random forests and gradient boosting trees, have demonstrated state-of-the-art accuracy compared to other methods in many supervised learning scenarios. In particular, Forests dominate other method…
View article: MANIFOLD FORESTS: CLOSING THE GAP ON NEURAL NETWORKS
MANIFOLD FORESTS: CLOSING THE GAP ON NEURAL NETWORKS Open
Decision forests (DFs), in particular random forests and gradient boosting trees, have demonstrated state-of-the-art accuracy compared to other methods in many supervised learning scenarios. In particular, DFs dominate other methods in tab…
View article: Random Forests for Adaptive Nearest Neighbor Estimation of Information-Theoretic Quantities
Random Forests for Adaptive Nearest Neighbor Estimation of Information-Theoretic Quantities Open
Information-theoretic quantities, such as conditional entropy and mutual information, are critical data summaries for quantifying uncertainty. Current widely used approaches for computing such quantities rely on nearest neighbor methods an…