26 citations · 93 across the 13 of their papers we have counts for
13 papers
Automated Efficient Estimation using Monte Carlo Efficient Influence Functions
Raj Agrawal, Sam Witty, Andy Zane +1
Many practical problems involve estimating low dimensional statistical quantities with high-dimensional models and datasets. Several approaches address these estimation tasks based…
Causal Structure Discovery between Clusters of Nodes Induced by Latent Factors
Chandler Squires, Annie Yun, Eshaan Nichani +2
We consider the problem of learning the structure of a causal directed acyclic graph (DAG) model in the presence of latent variables. We define latent factor causal models (LFCMs)…
Search for Lorentz Invariance Violation using Bayesian model comparison applied to Xiao et al. GRB spectral lag catalog
Shantanu Desai, Rajdeep Agrawal, Haveesh Singirikonda
We use the spectral lag catalog of 46 short GRBs aggregated by Xiao et al (2022), to carry out an independent search for Lorentz Invariance violation (LIV). For this purpose, we us…
The SKIM-FA Kernel: High-Dimensional Variable Selection and Nonlinear Interaction Discovery in Linear Time
Raj Agrawal, Tamara Broderick
Many scientific problems require identifying a small set of covariates that are associated with a target response and estimating their effects. Often, these effects are nonlinear a…
Search for Lorentz Invariance Violation from stacked Gamma-Ray Burst spectral lag data
Rajdeep Agrawal, Haveesh Singirikonda, Shantanu Desai
A number of works have claimed detections of a turn-over in the spectral lag data for individual Gamma-Ray Bursts (GRBs), caused by an energy-dependent speed of light, which could…
The DeCAMFounder: Non-Linear Causal Discovery in the Presence of Hidden Variables
Raj Agrawal, Chandler Squires, Neha Prasad +1
Many real-world decision-making tasks require learning causal relationships between a set of variables. Traditional causal discovery methods, however, require that all variables ar…
Hamiltonian Monte Carlo using an adjoint-differentiated Laplace approximation: Bayesian inference for latent Gaussian models and beyond
Charles C. Margossian, Aki Vehtari, Daniel Simpson +1
Gaussian latent variable models are a key class of Bayesian hierarchical models with applications in many fields. Performing Bayesian inference on such models can be challenging as…
Covariance Matrix Estimation under Total Positivity for Portfolio Selection
Raj Agrawal, Uma Roy, Caroline Uhler
Selecting the optimal Markowitz porfolio depends on estimating the covariance matrix of the returns of assets from periods of historical data. Problematically, is typic…
LR-GLM: High-Dimensional Bayesian Inference Using Low-Rank Data Approximations
Brian L. Trippe, Jonathan H. Huggins, Raj Agrawal +1
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. Generalized linea…
The Kernel Interaction Trick: Fast Bayesian Discovery of Pairwise Interactions in High Dimensions
Raj Agrawal, Jonathan H. Huggins, Brian Trippe +1
Discovering interaction effects on a response of interest is a fundamental problem faced in biology, medicine, economics, and many other scientific disciplines. In theory, Bayesian…
ABCD-Strategy: Budgeted Experimental Design for Targeted Causal Structure Discovery
Raj Agrawal, Chandler Squires, Karren Yang +2
Determining the causal structure of a set of variables is critical for both scientific inquiry and decision-making. However, this is often challenging in practice due to limited in…
Data-dependent compression of random features for large-scale kernel approximation
Raj Agrawal, Trevor Campbell, Jonathan H. Huggins +1
Kernel methods offer the flexibility to learn complex relationships in modern, large data sets while enjoying strong theoretical guarantees on quality. Unfortunately, these methods…
Minimal I-MAP MCMC for Scalable Structure Discovery in Causal DAG Models
Raj Agrawal, Tamara Broderick, Caroline Uhler
Learning a Bayesian network (BN) from data can be useful for decision-making or discovering causal relationships. However, traditional methods often fail in modern applications, wh…