Agrawal, Raj

9 publications

NeurIPS 2024 Automated Efficient Estimation Using Monte Carlo Efficient Influence Functions Raj Agrawal, Sam Witty, Andy Zane, Eli Bingham

JMLR 2023 The SKIM-FA Kernel: High-Dimensional Variable Selection and Nonlinear Interaction Discovery in Linear Time Raj Agrawal, Tamara Broderick

CLeaR 2022 Causal Structure Discovery Between Clusters of Nodes Induced by Latent Factors Chandler Squires, Annie Yun, Eshaan Nichani, Raj Agrawal, Caroline Uhler

NeurIPS 2020 Hamiltonian Monte Carlo Using an Adjoint-Differentiated Laplace Approximation: Bayesian Inference for Latent Gaussian Models and Beyond Charles Margossian, Aki Vehtari, Daniel Simpson, Raj Agrawal

AISTATS 2019 ABCD-Strategy: Budgeted Experimental Design for Targeted Causal Structure Discovery Raj Agrawal, Chandler Squires, Karren Yang, Karthikeyan Shanmugam, Caroline Uhler

AISTATS 2019 Data-Dependent Compression of Random Features for Large-Scale Kernel Approximation Raj Agrawal, Trevor Campbell, Jonathan Huggins, Tamara Broderick

ICML 2019 LR-GLM: High-Dimensional Bayesian Inference Using Low-Rank Data Approximations Brian Trippe, Jonathan Huggins, Raj Agrawal, Tamara Broderick

ICML 2019 The Kernel Interaction Trick: Fast Bayesian Discovery of Pairwise Interactions in High Dimensions Raj Agrawal, Brian Trippe, Jonathan Huggins, Tamara Broderick

ICML 2018 Minimal I-MAP MCMC for Scalable Structure Discovery in Causal DAG Models Raj Agrawal, Caroline Uhler, Tamara Broderick