Search Results for author: Raj Agrawal

Found 7 papers, 4 papers with code

Automated Efficient Estimation using Monte Carlo Efficient Influence Functions

no code implementations29 Feb 2024 Raj Agrawal, Sam Witty, Andy Zane, Eli Bingham

We prove that MC-EIF is consistent, and that estimators using MC-EIF achieve optimal $\sqrt{N}$ convergence rates.

Probabilistic Programming

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

1 code implementation23 Jun 2021 Raj Agrawal, Tamara Broderick

Often, these effects are nonlinear and include interactions, so linear and additive methods can lead to poor estimation and variable selection.

Gaussian Processes Variable Selection

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

no code implementations17 May 2019 Brian L. Trippe, Jonathan H. Huggins, Raj Agrawal, Tamara Broderick

Due to the ease of modern data collection, applied statisticians often have access to a large set of covariates that they wish to relate to some observed outcome.

Bayesian Inference Vocal Bursts Intensity Prediction

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

1 code implementation16 May 2019 Raj Agrawal, Jonathan H. Huggins, Brian Trippe, Tamara Broderick

Discovering interaction effects on a response of interest is a fundamental problem faced in biology, medicine, economics, and many other scientific disciplines.

Uncertainty Quantification

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

3 code implementations27 Feb 2019 Raj Agrawal, Chandler Squires, Karren Yang, Karthik Shanmugam, Caroline Uhler

Determining the causal structure of a set of variables is critical for both scientific inquiry and decision-making.

Methodology

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

no code implementations9 Oct 2018 Raj Agrawal, Trevor Campbell, Jonathan H. Huggins, Tamara Broderick

Random feature maps (RFMs) and the Nystrom method both consider low-rank approximations to the kernel matrix as a potential solution.

feature selection

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

1 code implementation ICML 2018 Raj Agrawal, Tamara Broderick, Caroline Uhler

Learning a Bayesian network (BN) from data can be useful for decision-making or discovering causal relationships.

Decision Making

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