6 papers · 1 filter
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…
Decision Tree Embedding by Leaf-Means
Cencheng Shen, Yuexiao Dong, Carey E. Priebe
Decision trees and random forest remain highly competitive for classification on medium-sized, standard datasets due to their robustness, minimal preprocessing requirements, and in…
Unsupervised Conformal Inference: Bootstrapping and Alignment to Control LLM Uncertainty
Lingyou Pang, Lei Huang, Jianyu Lin +4
Deploying black-box LLMs requires managing uncertainty in the absence of token-level probability or true labels. We propose introducing an unsupervised conformal inference framewor…
Explaining Categorical Feature Interactions Using Graph Covariance and LLMs
Cencheng Shen, Darren Edge, Jonathan Larson +1
Modern datasets often consist of numerous samples with abundant features and associated timestamps. Analyzing such datasets to uncover underlying events typically requires complex…
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…
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…