921 citations · 9.3k across the 292 of their papers we have counts for
53 papers · 1 filter
Context-Adaptive Inference: A Unified Statistical and Foundation-Model View
Yue Yao, Caleb N. Ellington, Jingyun Jia +9
Modern predictive systems are expected to adapt their behavior to the specific situation they are facing. A clinical model should not treat every patient the same; a retrieval-augm…
Contextualized Machine Learning
Benjamin Lengerich, Caleb N. Ellington, Andrea Rubbi +2
We examine Contextualized Machine Learning (ML), a paradigm for learning heterogeneous and context-dependent effects. Contextualized ML estimates heterogeneous functions by applyin…
NOTMAD: Estimating Bayesian Networks with Sample-Specific Structures and Parameters
Ben Lengerich, Caleb Ellington, Bryon Aragam +2
Context-specific Bayesian networks (i.e. directed acyclic graphs, DAGs) identify context-dependent relationships between variables, but the non-convexity induced by the acyclicity…
Distributed, partially collapsed MCMC for Bayesian Nonparametrics
Avinava Dubey, Michael Minyi Zhang, Eric P. Xing +1
Bayesian nonparametric (BNP) models provide elegant methods for discovering underlying latent features within a data set, but inference in such models can be slow. We exploit the f…
Learning Sample-Specific Models with Low-Rank Personalized Regression
Benjamin Lengerich, Bryon Aragam, Eric P. Xing
Modern applications of machine learning (ML) deal with increasingly heterogeneous datasets comprised of data collected from overlapping latent subpopulations. As a result, traditio…
Learning Sparse Nonparametric DAGs
Xun Zheng, Chen Dan, Bryon Aragam +2
We develop a framework for learning sparse nonparametric directed acyclic graphs (DAGs) from data. Our approach is based on a recent algebraic characterization of DAGs that led to…