14 citations · 32 across the 32 of their papers we have counts for
6 papers · 1 filter
Testing with Non-identically Distributed Samples
Shivam Garg, Chirag Pabbaraju, Kirankumar Shiragur +1
We examine the extent to which sublinear-sample property testing and estimation apply to settings where samples are independently but not identically distributed. Specifically, we…
Meek Separators and Their Applications in Targeted Causal Discovery
Kirankumar Shiragur, Jiaqi Zhang, Caroline Uhler
Learning causal structures from interventional data is a fundamental problem with broad applications across various fields. While many previous works have focused on recovering the…
Structured Semidefinite Programming for Recovering Structured Preconditioners
Arun Jambulapati, Jerry Li, Christopher Musco +3
We develop a general framework for finding approximately-optimal preconditioners for solving linear systems. Leveraging this framework we obtain improved runtimes for fundamental p…
Adaptivity Complexity for Causal Graph Discovery
Davin Choo, Kirankumar Shiragur
Causal discovery from interventional data is an important problem, where the task is to design an interventional strategy that learns the hidden ground truth causal graph …
New metrics and search algorithms for weighted causal DAGs
Davin Choo, Kirankumar Shiragur
Recovering causal relationships from data is an important problem. Using observational data, one can typically only recover causal graphs up to a Markov equivalence class and addit…
Subset verification and search algorithms for causal DAGs
Davin Choo, Kirankumar Shiragur
Learning causal relationships between variables is a fundamental task in causal inference and directed acyclic graphs (DAGs) are a popular choice to represent the causal relationsh…