118 citations · 440 across the 36 of their papers we have counts for
43 papers
Recovering manifold structure in LLM responses through a joint Euclidean mirror
Maximilian Baum, Aranyak Acharyya, Tianyi Chen +5
Understanding the behavior of black-box large language models and determining effective means of comparing their performance is a key task in modern machine learning. We consider h…
Data Kernel Perspective Space Performance Guarantees for Synthetic Data from Transformer Models
Michael Browder, Kevin Duh, J. David Harris +5
Scarcity of labeled training data remains the long pole in the tent for building performant language technology and generative AI models. Transformer models -- particularly LLMs --…
Concentration bounds on response-based vector embeddings of black-box generative models
Aranyak Acharyya, Joshua Agterberg, Youngser Park +1
Generative models, such as large language models or text-to-image diffusion models, can generate relevant responses to user-given queries. Response-based vector embeddings of gener…
Graph Neural Networks Powered by Encoder Embedding for Improved Node Learning
Shiyu Chen, Cencheng Shen, Youngser Park +1
Graph neural networks (GNNs) have emerged as a powerful framework for a wide range of node-level graph learning tasks. However, their performance typically depends on random or min…
Multi-rank Subspace Change-point Detection with Application in Monitoring Robotic Swarms
Jonghyeok Lee, Yao Xie, Youngser Park +3
We study real-time detection of low-rank changes in the covariance structure of high-dimensional streaming data, motivated by robotic swarm monitoring. Building on the spiked covar…
Principal Graph Encoder Embedding and Principal Community Detection
Cencheng Shen, Yuexiao Dong, Carey E. Priebe +3
In this paper, we introduce the concept of principal communities and propose a principal graph encoder embedding method that concurrently detects these communities and achieves ver…
Statistical inference on black-box generative models in the data kernel perspective space
Hayden Helm, Aranyak Acharyya, Brandon Duderstadt +2
Generative models are capable of producing human-expert level content across a variety of topics and domains. As the impact of generative models grows, it is necessary to develop s…
Tracking the perspectives of interacting language models
Hayden Helm, Brandon Duderstadt, Youngser Park +1
Large language models (LLMs) are capable of producing high quality information at unprecedented rates. As these models continue to entrench themselves in society, the content they…
Consistent response prediction for multilayer networks on unknown manifolds
Aranyak Acharyya, Jesús Arroyo Relión, Michael Clayton +3
Our paper deals with a collection of networks on a common set of nodes, where some of the networks are associated with responses. Assuming that the networks correspond to points on…
Euclidean mirrors and first-order changepoints in network time series
Tianyi Chen, Zachary Lubberts, Avanti Athreya +2
We describe a model for a network time series whose evolution is governed by an underlying stochastic process, known as the latent position process, in which network evolution can…
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…
Discovering Communication Pattern Shifts in Large-Scale Labeled Networks using Encoder Embedding and Vertex Dynamics
Cencheng Shen, Jonathan Larson, Ha Trinh +3
Analyzing large-scale time-series network data, such as social media and email communications, poses a significant challenge in understanding social dynamics, detecting anomalies,…
Discovering a change point and piecewise linear structure in a time series of organoid networks via the iso-mirror
Tianyi Chen, Youngser Park, Ali Saad-Eldin +10
Recent advancements have been made in the development of cell-based in-vitro neuronal networks, or organoids. In order to better understand the network structure of these organoids…
Graph Encoder Ensemble for Simultaneous Vertex Embedding and Community Detection
Cencheng Shen, Youngser Park, Carey E. Priebe
In this paper, we introduce a novel and computationally efficient method for vertex embedding, community detection, and community size determination. Our approach leverages a norma…
Dynamic Network Sampling for Community Detection
Cong Mu, Youngser Park, Carey E. Priebe
We propose a dynamic network sampling scheme to optimize block recovery for stochastic blockmodel (SBM) in the case where it is prohibitively expensive to observe the entire graph.…
Euclidean mirrors and dynamics in network time series
Avanti Athreya, Zachary Lubberts, Youngser Park +1
Analyzing changes in network evolution is central to statistical network inference, as underscored by recent challenges of predicting and distinguishing pandemic-induced transforma…
An Analysis of Euclidean vs. Graph-Based Framing for Bilingual Lexicon Induction from Word Embedding Spaces
Kelly Marchisio, Youngser Park, Ali Saad-Eldin +4
Much recent work in bilingual lexicon induction (BLI) views word embeddings as vectors in Euclidean space. As such, BLI is typically solved by finding a linear transformation that…
Leveraging semantically similar queries for ranking via combining representations
Hayden S. Helm, Marah Abdin, Benjamin D. Pedigo +8
In modern ranking problems, different and disparate representations of the items to be ranked are often available. It is sensible, then, to try to combine these representations to…
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…
Random line graphs and edge-attributed network inference
Zachary Lubberts, Avanti Athreya, Youngser Park +1
We extend the latent position random graph model to the line graph of a random graph, which is formed by creating a vertex for each edge in the original random graph, and connectin…
Multiscale Comparative Connectomics
Vivek Gopalakrishnan, Jaewon Chung, Eric Bridgeford +8
The connectome, a map of the structural and/or functional connections in the brain, provides a complex representation of the neurobiological phenotypes on which it supervenes. This…
Multiple Network Embedding for Anomaly Detection in Time Series of Graphs
Guodong Chen, Jesús Arroyo, Avanti Athreya +7
This paper considers the graph signal processing problem of anomaly detection in time series of graphs. We examine two related, complementary inference tasks: the detection of anom…
Distance-based Positive and Unlabeled Learning for Ranking
Hayden S. Helm, Amitabh Basu, Avanti Athreya +12
Learning to rank -- producing a ranked list of items specific to a query and with respect to a set of supervisory items -- is a problem of general interest. The setting we consider…
Multiplex graph matching matched filters
Konstantinos Pantazis, Daniel L. Sussman, Youngser Park +3
We consider the problem of detecting a noisy induced multiplex template network in a larger multiplex background network. Our approach, which extends the framework of Sussman et al…