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ORCIDRaj Agrawal
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Books and Theses
- 2021
[b1]Raj Agrawal:
Practical Methods for Scalable Bayesian and Causal Inference with Provable Quality Guarantees. Massachusetts Institute of Technology, USA, 2021
Journal Articles
- 2023
- [j1]Raj Agrawal, Tamara Broderick:
The SKIM-FA Kernel: High-Dimensional Variable Selection and Nonlinear Interaction Discovery in Linear Time. J. Mach. Learn. Res. 24: 27:1-27:60 (2023)
Conference and Workshop Papers
- 2022
- [c8]Chandler Squires, Annie Yun, Eshaan Nichani, Raj Agrawal, Caroline Uhler:
Causal Structure Discovery between Clusters of Nodes Induced by Latent Factors. CLeaR 2022: 669-687 - 2021
- [c7]Thibaut Horel, Lorenzo Masoero, Raj Agrawal, Daria Roithmayr, Trevor Campbell:
The CPD Data Set: Personnel, Use of Force, and Complaints in the Chicago Police Department. NeurIPS Datasets and Benchmarks 2021 - 2020
- [c6]Charles C. Margossian, Aki Vehtari, Daniel Simpson, Raj Agrawal:
Hamiltonian Monte Carlo using an adjoint-differentiated Laplace approximation: Bayesian inference for latent Gaussian models and beyond. NeurIPS 2020 - 2019
- [c5]Raj Agrawal, Trevor Campbell, Jonathan H. Huggins, Tamara Broderick:
Data-dependent compression of random features for large-scale kernel approximation. AISTATS 2019: 1822-1831 - [c4]Raj Agrawal, Chandler Squires, Karren D. Yang, Karthikeyan Shanmugam
, Caroline Uhler:
ABCD-Strategy: Budgeted Experimental Design for Targeted Causal Structure Discovery. AISTATS 2019: 3400-3409 - [c3]Raj Agrawal, Brian L. Trippe, Jonathan H. Huggins, Tamara Broderick:
The Kernel Interaction Trick: Fast Bayesian Discovery of Pairwise Interactions in High Dimensions. ICML 2019: 141-150 - [c2]Brian L. Trippe, Jonathan H. Huggins, Raj Agrawal, Tamara Broderick:
LR-GLM: High-Dimensional Bayesian Inference Using Low-Rank Data Approximations. ICML 2019: 6315-6324 - 2018
- [c1]Raj Agrawal, Caroline Uhler, Tamara Broderick:
Minimal I-MAP MCMC for Scalable Structure Discovery in Causal DAG Models. ICML 2018: 89-98
Informal and Other Publications
- 2024
- [i5]Raj Agrawal, Sam Witty, Andy Zane, Eli Bingham:
Automated Efficient Estimation using Monte Carlo Efficient Influence Functions. CoRR abs/2403.00158 (2024) - 2019
- [i4]Raj Agrawal, Jonathan H. Huggins, Brian L. Trippe, Tamara Broderick:
The Kernel Interaction Trick: Fast Bayesian Discovery of Pairwise Interactions in High Dimensions. CoRR abs/1905.06501 (2019) - [i3]Brian L. Trippe, Jonathan H. Huggins, Raj Agrawal, Tamara Broderick:
LR-GLM: High-Dimensional Bayesian Inference Using Low-Rank Data Approximations. CoRR abs/1905.07499 (2019) - 2018
- [i2]Raj Agrawal, Tamara Broderick, Caroline Uhler:
Minimal I-MAP MCMC for Scalable Structure Discovery in Causal DAG Models. CoRR abs/1803.05554 (2018) - [i1]Raj Agrawal, Trevor Campbell, Jonathan H. Huggins, Tamara Broderick:
Data-dependent compression of random features for large-scale kernel approximation. CoRR abs/1810.04249 (2018)
Coauthor Index
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