5 citations · 14 across the 17 of their papers we have counts for
6 papers · 1 filter
Learning bounded-degree polytrees with known skeleton
Davin Choo, Joy Qiping Yang, Arnab Bhattacharyya +1
We establish finite-sample guarantees for efficient proper learning of bounded-degree polytrees, a rich class of high-dimensional probability distributions and a subclass of Bayesi…
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 …
Active causal structure learning with advice
Davin Choo, Themis Gouleakis, Arnab Bhattacharyya
We introduce the problem of active causal structure learning with advice. In the typical well-studied setting, the learning algorithm is given the essential graph for the observati…
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…
The Sharp Power Law of Local Search on Expanders
Simina Brânzei, Davin Choo, Nicholas Recker
Local search is a powerful heuristic in optimization and computer science, the complexity of which was studied in the white box and black box models. In the black box model, we are…
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…