504 citations · 2k across the 109 of their papers we have counts for
31 papers · 1 filter
Optimal control of the future via prospective learning with control
Yuxin Bai, Aranyak Acharyya, Ashwin De Silva +3
Optimal control of the future is the next frontier for AI. Current approaches to this problem are typically rooted in reinforcement learning (RL). RL is mathematically distinct fro…
Prospective Learning: Learning for a Dynamic Future
Ashwin De Silva, Rahul Ramesh, Rubing Yang +3
In real-world applications, the distribution of the data, and our goals, evolve over time. The prevailing theoretical framework for studying machine learning, namely probably appro…
Semisupervised regression in latent structure networks on unknown manifolds
Aranyak Acharyya, Joshua Agterberg, Michael W. Trosset +2
Random graphs are increasingly becoming objects of interest for modeling networks in a wide range of applications. Latent position random graph models posit that each node is assoc…
Graph Matching via Optimal Transport
Ali Saad-Eldin, Benjamin D. Pedigo, Carey E. Priebe +1
The graph matching problem seeks to find an alignment between the nodes of two graphs that minimizes the number of adjacency disagreements. Solving the graph matching is increasing…
Towards a theory of out-of-distribution learning
Jayanta Dey, Ali Geisa, Ronak Mehta +7
Learning is a process wherein a learning agent enhances its performance through exposure of experience or data. Throughout this journey, the agent may encounter diverse learning en…
Dynamic Silos: Increased Modularity in Intra-organizational Communication Networks during the Covid-19 Pandemic
Tiona Zuzul, Emily Cox Pahnke, Jonathan Larson +9
Workplace communications around the world were drastically altered by Covid-19, related work-from-home orders, and the rise of remote work. To understand these shifts, we analyzed…
A partition-based similarity for classification distributions
Hayden S. Helm, Ronak D. Mehta, Brandon Duderstadt +5
Herein we define a measure of similarity between classification distributions that is both principled from the perspective of statistical pattern recognition and useful from the pe…
Robust Similarity and Distance Learning via Decision Forests
Tyler M. Tomita, Joshua T. Vogelstein
Canonical distances such as Euclidean distance often fail to capture the appropriate relationships between items, subsequently leading to subpar inference and prediction. Many algo…
mvlearn: Multiview Machine Learning in Python
Ronan Perry, Gavin Mischler, Richard Guo +9
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,…
The Chi-Square Test of Distance Correlation
Cencheng Shen, Sambit Panda, Joshua T. Vogelstein
Distance correlation has gained much recent attention in the data science community: the sample statistic is straightforward to compute and asymptotically equals zero if and only i…
Universally Consistent K-Sample Tests via Dependence Measures
Sambit Panda, Cencheng Shen, Ronan Perry +4
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 met…
Independence Testing for Temporal Data
Cencheng Shen, Jaewon Chung, Ronak Mehta +2
Temporal data are increasingly prevalent in modern data science. A fundamental question is whether two time series are related or not. Existing approaches often have limitations, s…
Geodesic Learning via Unsupervised Decision Forests
Meghana Madhyastha, Percy Li, James Browne +4
Geodesic distance is the shortest path between two points in a Riemannian manifold. Manifold learning algorithms, such as Isomap, seek to learn a manifold that preserves geodesic d…
Vertex Classification on Weighted Networks
Hayden Helm, Joshua Vogelstein, Carey Priebe
This paper proposes a discrimination technique for vertices in a weighted network. We assume that the edge weights and adjacencies in the network are conditionally independent and…
Learning Interpretable Characteristic Kernels via Decision Forests
Sambit Panda, Cencheng Shen, Joshua T. Vogelstein
Decision forests are widely used for classification and regression tasks. A lesser known property of tree-based methods is that one can construct a proximity matrix from the tree(s…
On a 'Two Truths' Phenomenon in Spectral Graph Clustering
Carey E. Priebe, Youngser Park, Joshua T. Vogelstein +6
Clustering is concerned with coherently grouping observations without any explicit concept of true groupings. Spectral graph clustering - clustering the vertices of a graph based o…
The Exact Equivalence of Distance and Kernel Methods for Hypothesis Testing
Cencheng Shen, Joshua T. Vogelstein
Distance-based tests, also called "energy statistics", are leading methods for two-sample and independence tests from the statistics community. Kernel-based tests, developed from "…
Kernel k-Groups via Hartigan's Method
Guilherme França, Maria L. Rizzo, Joshua T. Vogelstein
Energy statistics was proposed by Sz\' ekely in the 80's inspired by Newton's gravitational potential in classical mechanics and it provides a model-free hypothesis test for equali…
From Distance Correlation to Multiscale Graph Correlation
Cencheng Shen, Carey E. Priebe, Joshua T. Vogelstein
Understanding and developing a correlation measure that can detect general dependencies is not only imperative to statistics and machine learning, but also crucial to general scien…
Supervised Dimensionality Reduction for Big Data
Joshua T. Vogelstein, Eric Bridgeford, Minh Tang +4
To solve key biomedical problems, experimentalists now routinely measure millions or billions of features (dimensions) per sample, with the hope that data science techniques will b…
Semiparametric spectral modeling of the Drosophila connectome
Carey E. Priebe, Youngser Park, Minh Tang +8
We present semiparametric spectral modeling of the complete larval Drosophila mushroom body connectome. Motivated by a thorough exploratory data analysis of the network via Gaussia…
Discovering and Deciphering Relationships Across Disparate Data Modalities
Joshua T. Vogelstein, Eric Bridgeford, Qing Wang +3
Understanding the relationships between different properties of data, such as whether a connectome or genome has information about disease status, is becoming increasingly importan…
Sparse Projection Oblique Randomer Forests
Tyler M. Tomita, James Browne, Cencheng Shen +8
Decision forests, including Random Forests and Gradient Boosting Trees, have recently demonstrated state-of-the-art performance in a variety of machine learning settings. Decision…
Manifold Matching using Shortest-Path Distance and Joint Neighborhood Selection
Cencheng Shen, Joshua T. Vogelstein, Carey E. Priebe
Matching datasets of multiple modalities has become an important task in data analysis. Existing methods often rely on the embedding and transformation of each single modality with…