16 citations · 35 across the 13 of their papers we have counts for
14 papers
Learning Submanifolds for Subsequent Inference on Random Dot Product Graphs, Part 1: Theory
Michael W. Trosset, Carey E. Priebe
We propose a framework for restricted inference on random dot product graphs whose latent positions lie on an unknown low-dimensional support manifold. For general decision problem…
Out-of-Sample Embedding with Proximity Data: Projection versus Restricted Reconstruction
Michael W. Trosset, Kaiyi Tan, Minh Tang +1
The problem of using proximity (similarity or dissimilarity) data for the purpose of "adding a point to a vector diagram" was first studied by J.C. Gower in 1968. Since then, a num…
Convergence guarantees for response prediction for latent structure network time series
Aranyak Acharyya, Francesco Sanna Passino, Michael W. Trosset +1
In this article, we propose a technique to predict the response associated with an unlabeled time series of networks in a semisupervised setting. Our model involves a collection of…
Optimizing the Induced Correlation in Omnibus Joint Graph Embeddings
Konstantinos Pantazis, Michael Trosset, William N. Frost +2
Theoretical and empirical evidence suggests that joint graph embedding algorithms induce correlation across the networks in the embedding space. In the Omnibus joint graph embeddin…
Consistent estimation of generative model representations in the data kernel perspective space
Aranyak Acharyya, Michael W. Trosset, Carey E. Priebe +1
Generative models, such as large language models and text-to-image diffusion models, produce relevant information when presented a query. Different models may produce different inf…
Continuous Multidimensional Scaling
Michael W. Trosset, Carey E. Priebe
Multidimensional scaling (MDS) is the act of embedding proximity information about a set of objects in -dimensional Euclidean space. As originally conceived by the psychomet…
Popularity Adjusted Block Models are Generalized Random Dot Product Graphs
John Koo, Minh Tang, Michael W. Trosset
We connect two random graph models, the Popularity Adjusted Block Model (PABM) and the Generalized Random Dot Product Graph (GRDPG), by demonstrating that the PABM is a special cas…
Rehabilitating Isomap: Euclidean Representation of Geodesic Structure
Michael W. Trosset, Gokcen Buyukbas
Manifold learning techniques for nonlinear dimension reduction assume that high-dimensional feature vectors lie on a low-dimensional manifold, then attempt to exploit manifold stru…
Learning 1-Dimensional Submanifolds for Subsequent Inference on Random Dot Product Graphs
Michael W. Trosset, Mingyue Gao, Minh Tang +1
A random dot product graph (RDPG) is a generative model for networks in which vertices correspond to positions in a latent Euclidean space and edge probabilities are determined by…
Approximate Information Tests on Statistical Submanifolds
Michael W. Trosset, Carey E. Priebe
Parametric inference posits a statistical model that is a specified family of probability distributions. Restricted inference, e.g., restricted likelihood ratio testing, attempts t…
Identifiability of two-component skew normal mixtures with one known component
Shantanu Jain, Michael Levine, Predrag Radivojac +1
We give sufficient identifiability conditions for estimating mixing proportions in two-component mixtures of skew normal distributions with one known component. We consider the uni…
On the Power of Likelihood Ratio Tests in Dimension-Restricted Submodels
Michael W. Trosset, Mingyue Gao, Carey E. Priebe
Likelihood ratio tests are widely used to test statistical hypotheses about parametric families of probability distributions. If interest is restricted to a subfamily of distributi…
Nonparametric semi-supervised learning of class proportions
Shantanu Jain, Martha White, Michael W. Trosset +1
The problem of developing binary classifiers from positive and unlabeled data is often encountered in machine learning. A common requirement in this setting is to approximate poste…
Fast Embedding for JOFC Using the Raw Stress Criterion
Vince Lyzinski, Youngser Park, Carey E. Priebe +1
The Joint Optimization of Fidelity and Commensurability (JOFC) manifold matching methodology embeds an omnibus dissimilarity matrix consisting of multiple dissimilarities on the sa…