42 citations · 94 across the 13 of their papers we have counts for
13 papers
Distributional Balancing with Machine Learning for Clinical Trial Augmentation Using Real-World Data
Zern Ke, Mingshi Cui, Gemma Moran +1
In clinical trials, randomization of treatment and control groups is typically used to ensure the groups have similar covariate distributions on average, resulting in unbiased caus…
Aggregated Posterior Predictive Checks for Generative Modeling
Shweta Dutta, Gemma E. Moran
Latent variable generative models are commonly fit using simple priors over latent variables, but draws from these priors often fail to produce realistic data. This failure is due…
Stochastic weather generators for high-frequency wind vector time series
Mingshi Cui, Kevin Eng, Justin T. Greene +5
Surface winds can vary substantially from one minute to the next, so there is scope for studying its variation on this fine time scale. Restricting to the month of June to minimize…
Can Cross-Layer Transcoders Replace Vision Transformer Activations? An Interpretable Perspective on Vision
Gerasimos Chatzoudis, Konstantinos D. Polyzos, Zhuowei Li +4
Understanding the internal activations of Vision Transformers (ViTs) is critical for building interpretable and trustworthy models. While Sparse Autoencoders (SAEs) have been used…
Nonlinear multi-study sparse factor analysis
Gemma E. Moran, Anandi Krishnan
High-dimensional data often exhibit variation that can be captured by lower-dimensional factors. For high-dimensional data from multiple studies, one goal is to understand which un…
Beyond Interpretability: When, Why, and How Sparse Autoencoders Enable Label-Free Visual Steering
Gerasimos Chatzoudis, Zhuowei Li, Gemma E. Moran +2
Sparse Autoencoders (SAEs) are increasingly used to interpret foundation models, but their role as an actionable intervention space remains less understood, especially in vision. W…
Towards Interpretable Deep Generative Models via Causal Representation Learning
Gemma E. Moran, Bryon Aragam
Recent developments in generative artificial intelligence (AI) rely on machine learning techniques such as deep learning and generative modeling to achieve state-of-the-art perform…
Towards scientific discovery with dictionary learning: Extracting biological concepts from microscopy foundation models
Konstantin Donhauser, Kristina Ulicna, Gemma Elyse Moran +4
Sparse dictionary learning (DL) has emerged as a powerful approach to extract semantically meaningful concepts from the internals of large language models (LLMs) trained mainly in…
The Posterior Predictive Null
Gemma E. Moran, John P. Cunningham, David M. Blei
Bayesian model criticism is an important part of the practice of Bayesian statistics. Traditionally, model criticism methods have been based on the predictive check, an adaptation…
Identifiable Deep Generative Models via Sparse Decoding
Gemma E. Moran, Dhanya Sridhar, Yixin Wang +1
We develop the sparse VAE for unsupervised representation learning on high-dimensional data. The sparse VAE learns a set of latent factors (representations) which summarize the ass…
Holdout predictive checks for Bayesian model criticism
Gemma E. Moran, David M. Blei, Rajesh Ranganath
Bayesian modeling helps applied researchers articulate assumptions about their data and develop models tailored for specific applications. Thanks to good methods for approximate po…
Spike-and-Slab Group Lassos for Grouped Regression and Sparse Generalized Additive Models
Ray Bai, Gemma E. Moran, Joseph Antonelli +2
We introduce the spike-and-slab group lasso (SSGL) for Bayesian estimation and variable selection in linear regression with grouped variables. We further extend the SSGL to sparse…
Variance prior forms for high-dimensional Bayesian variable selection
Gemma E. Moran, Veronika Rockova, Edward I. George
Consider the problem of high dimensional variable selection for the Gaussian linear model when the unknown error variance is also of interest. In this paper, we show that the use o…