Raj Agrawal - RankMe

16 papers A* 5A 2Journal 6Unranked 2

Year	Rank	Type	Title / Venue / Authors
2024	A*	`conf`	Automated Efficient Estimation using Monte Carlo Efficient Influence Functions. NeurIPS Raj Agrawal, Sam Witty, Andy Zane, Elias Bingham
2024	J	`jnl`	Automated Efficient Estimation using Monte Carlo Efficient Influence Functions. CoRR Raj Agrawal, Sam Witty, Andy Zane, Elias Bingham
2023	J	`jnl`	The SKIM-FA Kernel: High-Dimensional Variable Selection and Nonlinear Interaction Discovery in Linear Time. J. Mach. Learn. Res. Raj Agrawal, Tamara Broderick
2022	—	`conf`	Causal Structure Discovery between Clusters of Nodes Induced by Latent Factors. CLeaR Chandler Squires, Annie Yun, Eshaan Nichani, Raj Agrawal, Caroline Uhler
2021	—	`—`	Practical Methods for Scalable Bayesian and Causal Inference with Provable Quality Guarantees. Raj Agrawal
2021	—	`conf`	The CPD Data Set: Personnel, Use of Force, and Complaints in the Chicago Police Department. NeurIPS Datasets and Benchmarks Thibaut Horel, Lorenzo Masoero, Raj Agrawal, Daria Roithmayr, Trevor Campbell
2020	A*	`conf`	Hamiltonian Monte Carlo using an adjoint-differentiated Laplace approximation: Bayesian inference for latent Gaussian models and beyond. NeurIPS Charles C. Margossian, Aki Vehtari, Daniel Simpson, Raj Agrawal
2019	A	`conf`	ABCD-Strategy: Budgeted Experimental Design for Targeted Causal Structure Discovery. AISTATS Raj Agrawal, Chandler Squires, Karren D. Yang, Karthikeyan Shanmugam, Caroline Uhler
2019	A	`conf`	Data-dependent compression of random features for large-scale kernel approximation. AISTATS Raj Agrawal, Trevor Campbell, Jonathan H. Huggins, Tamara Broderick
2019	A*	`conf`	LR-GLM: High-Dimensional Bayesian Inference Using Low-Rank Data Approximations. ICML Brian L. Trippe, Jonathan H. Huggins, Raj Agrawal, Tamara Broderick
2019	J	`jnl`	LR-GLM: High-Dimensional Bayesian Inference Using Low-Rank Data Approximations. CoRR Brian L. Trippe, Jonathan H. Huggins, Raj Agrawal, Tamara Broderick
2019	A*	`conf`	The Kernel Interaction Trick: Fast Bayesian Discovery of Pairwise Interactions in High Dimensions. ICML Raj Agrawal, Brian L. Trippe, Jonathan H. Huggins, Tamara Broderick
2019	J	`jnl`	The Kernel Interaction Trick: Fast Bayesian Discovery of Pairwise Interactions in High Dimensions. CoRR Raj Agrawal, Jonathan H. Huggins, Brian L. Trippe, Tamara Broderick
2018	J	`jnl`	Data-dependent compression of random features for large-scale kernel approximation. CoRR Raj Agrawal, Trevor Campbell, Jonathan H. Huggins, Tamara Broderick
2018	A*	`conf`	Minimal I-MAP MCMC for Scalable Structure Discovery in Causal DAG Models. ICML Raj Agrawal, Caroline Uhler, Tamara Broderick
2018	J	`jnl`	Minimal I-MAP MCMC for Scalable Structure Discovery in Causal DAG Models. CoRR Raj Agrawal, Tamara Broderick, Caroline Uhler