Raj Agrawal

According to our database, Raj Agrawal authored at least 11 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Automated Efficient Estimation using Monte Carlo Efficient Influence Functions.

Raj Agrawal, Sam Witty, Andy Zane, Eli Bingham

CoRR, 2024

2023

The SKIM-FA Kernel: High-Dimensional Variable Selection and Nonlinear Interaction Discovery in Linear Time.

Raj Agrawal, Tamara Broderick

J. Mach. Learn. Res., 2023

2022

Causal Structure Discovery between Clusters of Nodes Induced by Latent Factors.

Chandler Squires, Annie Yun, Eshaan Nichani, Raj Agrawal, Caroline Uhler

Proceedings of the 1st Conference on Causal Learning and Reasoning, 2022

2021

Practical Methods for Scalable Bayesian and Causal Inference with Provable Quality Guarantees.

Raj Agrawal

PhD thesis, 2021

The CPD Data Set: Personnel, Use of Force, and Complaints in the Chicago Police Department.

Thibaut Horel, Lorenzo Masoero, Raj Agrawal, Daria Roithmayr, Trevor Campbell

Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021

2020

Hamiltonian Monte Carlo using an adjoint-differentiated Laplace approximation: Bayesian inference for latent Gaussian models and beyond.

Charles C. Margossian, Aki Vehtari, Daniel Simpson, Raj Agrawal

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019

LR-GLM: High-Dimensional Bayesian Inference Using Low-Rank Data Approximations.

Brian L. Trippe, Jonathan H. Huggins, Raj Agrawal, Tamara Broderick

Proceedings of the 36th International Conference on Machine Learning, 2019

The Kernel Interaction Trick: Fast Bayesian Discovery of Pairwise Interactions in High Dimensions.

Raj Agrawal, Brian L. Trippe, Jonathan H. Huggins, Tamara Broderick

Proceedings of the 36th International Conference on Machine Learning, 2019

ABCD-Strategy: Budgeted Experimental Design for Targeted Causal Structure Discovery.

Raj Agrawal, Chandler Squires, Karren D. Yang, Karthikeyan Shanmugam, Caroline Uhler

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

Data-dependent compression of random features for large-scale kernel approximation.

Raj Agrawal, Trevor Campbell, Jonathan H. Huggins, Tamara Broderick

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

Minimal I-MAP MCMC for Scalable Structure Discovery in Causal DAG Models.

Raj Agrawal, Caroline Uhler, Tamara Broderick

Proceedings of the 35th International Conference on Machine Learning, 2018