5 citations · 6 across the 5 of their papers we have counts for
6 papers
Gaussian Comparison Theorems for High-Dimensional Posterior Inference
Wenxuan Zou, Galen Reeves
We develop a Gaussian comparison theory for posterior inference in non-Gaussian high-dimensional models. The framework allows for model misspecification and does not require the po…
Free Energy Universality in Tensor Estimation via Generic Chaining
Wenxuan Zou, Galen Reeves
We study high-dimensional inference problems with tensor-structured data and establish conditions under which their free energy can be approximated by that of a Gaussian comparison…
Statistical Limits for Finite-Rank Tensor Estimation
Riccardo Rossetti, Galen Reeves
This paper provides a unified framework for analyzing tensor estimation problems that allow for nonlinear observations, heteroskedastic noise, and covariate information. We study a…
What happens when generative AI models train recursively on each others' outputs?
Hung Anh Vu, Galen Reeves, Emily Wenger
The internet serves as a common source of training data for generative AI (genAI) models but is increasingly populated with AI-generated content. This duality raises the possibilit…
Linear Operator Approximate Message Passing (OpAMP)
Riccardo Rossetti, Bobak Nazer, Galen Reeves
This paper introduces a framework for approximate message passing (AMP) in dynamic settings where the data at each iteration is passed through a linear operator. This framework is…
k-Sliced Mutual Information: A Quantitative Study of Scalability with Dimension
Ziv Goldfeld, Kristjan Greenewald, Theshani Nuradha +1
Sliced mutual information (SMI) is defined as an average of mutual information (MI) terms between one-dimensional random projections of the random variables. It serves as a surroga…