66 citations · 91 across the 13 of their papers we have counts for
14 papers
Contrastive Neural Embeddings Reveal Individual Traits Beyond Conversational Role
Hubert Huang, Michelle McCleod, Brendan Ames +1
Contrastive representation learning is increasingly used to recover low-dimensional structure from neural recordings, but its output is typically validated by decoding accuracy rat…
Deflation-Free Optimal Scoring
Sharmin Afroz, Brendan Ames
Sparse Optimal Scoring (SOS) reformulates linear discriminant analysis to enable feature selection through elastic net regularization, making it well-suited for high-dimensional se…
Provably Finding a Hidden Dense Submatrix among Many Planted Dense Submatrices via Convex Programming
Valentine Olanubi, Phineas Agar, Brendan Ames
We consider the densest submatrix problem, which seeks the submatrix of fixed size of a given binary matrix that contains the most nonzero entries. This problem is a natural genera…
Dyadic Neural Dynamics: Extending Representation Learning to Social Neuroscience
Maria Glushanina, Jeffrey Huang, Michelle McCleod +2
Social communication fundamentally involves at least two interacting brains, creating a unique modeling problem. We present the first application of Contrastive Embedding for Behav…
Convex optimization for the densest subgraph and densest submatrix problems
Polina Bombina, Brendan Ames
We consider the densest -subgraph problem, which seeks to identify the -node subgraph of a given input graph with maximum number of edges. This problem is well-known to be NP…
Proximal Methods for Sparse Optimal Scoring and Discriminant Analysis
Summer Atkins, Gudmundur Einarsson, Brendan Ames +1
Linear discriminant analysis (LDA) is a classical method for dimensionality reduction, where discriminant vectors are sought to project data to a lower dimensional space for optima…
Exact Clustering of Weighted Graphs via Semidefinite Programming
Aleksis Pirinen, Brendan Ames
As a model problem for clustering, we consider the densest k-disjoint-clique problem of partitioning a weighted complete graph into k disjoint subgraphs such that the sum of the de…
Solving ptychography with a convex relaxation
Roarke Horstmeyer, Richard Y. Chen, Xiaoze Ou +3
Ptychography is a powerful computational imaging technique that transforms a collection of low-resolution images into a high-resolution sample reconstruction. Unfortunately, algori…
Alternating direction method of multipliers for penalized zero-variance discriminant analysis
Brendan P. W. Ames, Mingyi Hong
We consider the task of classification in the high dimensional setting where the number of features of the given data is significantly greater than the number of observations. To a…
A Leapfrog Strategy for Pursuit-Evasion in a Polygonal Environment
Brendan Ames, Andrew Beveridge, Rosalie Carlson +4
We study pursuit-evasion in a polygonal environment with polygonal obstacles. In this turn based game, an evader is chased by pursuers . The players ha…
Guaranteed Recovery of Planted Cliques and Dense Subgraphs by Convex Relaxation
Brendan P. W. Ames
We consider the problem of identifying the densest k-node subgraph in a given graph. We write this problem as an instance of rank-constrained cardinality minimization and then rela…
Guaranteed clustering and biclustering via semidefinite programming
Brendan P. W. Ames
Identifying clusters of similar objects in data plays a significant role in a wide range of applications. As a model problem for clustering, we consider the densest k-disjoint-cliq…
Convex optimization for the planted k-disjoint-clique problem
Brendan P. W. Ames, Stephen A. Vavasis
We consider the k-disjoint-clique problem. The input is an undirected graph G in which the nodes represent data items, and edges indicate a similarity between the corresponding ite…
Nuclear norm minimization for the planted clique and biclique problems
Brendan Ames, Stephen Vavasis
We consider the problems of finding a maximum clique in a graph and finding a maximum-edge biclique in a bipartite graph. Both problems are NP-hard. We write both problems as matri…