Raj Agrawal
9 papers · 2018–2024 · 5 venues · across top CS/AI venues
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Achievements
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Machine Learning
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Artificial Intelligence
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Optimization & Theory
(6)
Mathematics & Optimization
(4)
Core Methods
(4)
Bayesian & Probabilistic Β· Artificial Intelligence
(3)
Core AI
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Bayesian & Probabilistic Β· Machine Learning
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Keywords
markov chain monte carlo
(4)
bayesian inference
(3)
causal discovery
(3)
laplace approximation
(2)
gaussian process
(1)
high-dimensional inference
(1)
variational inference
(1)
low-rank approximation
(1)
structure learning
(1)
sparse optimization
(1)
bayesian experimental design
(1)
optimal experimental design
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statistical estimation
(1)
causal structure
(1)
high-dimensional regression
(1)
kernel approximation
(1)
variable selection
(1)
probabilistic programming
(1)
posterior approximation
(1)
submodular optimization
(1)
Papers
Automated Efficient Estimation using Monte Carlo Efficient Influence Functions
NeurIPS 2024
The SKIM-FA Kernel: High-Dimensional Variable Selection and Nonlinear Interaction Discovery in Linear Time
JMLR 2023
Causal Structure Discovery between Clusters of Nodes Induced by Latent Factors
CLeaR 2022
Hamiltonian Monte Carlo using an adjoint-differentiated Laplace approximation: Bayesian inference for latent Gaussian models and beyond
NeurIPS 2020
ABCD-Strategy: Budgeted Experimental Design for Targeted Causal Structure Discovery
AISTATS 2019
Data-dependent compression of random features for large-scale kernel approximation
AISTATS 2019
LR-GLM: High-Dimensional Bayesian Inference Using Low-Rank Data Approximations
ICML 2019
The Kernel Interaction Trick: Fast Bayesian Discovery of Pairwise Interactions in High Dimensions
ICML 2019
Minimal I-MAP MCMC for Scalable Structure Discovery in Causal DAG Models
ICML 2018