5 citations · 14 across the 17 of their papers we have counts for
5 papers · 1 filter
Probably approximately correct high-dimensional causal effect estimation given a valid adjustment set
Davin Choo, Chandler Squires, Arnab Bhattacharyya +1
Accurate estimates of causal effects play a key role in decision-making across applications such as healthcare, economics, and operations. In the absence of randomized experiments,…
Learning multivariate Gaussians with imperfect advice
Arnab Bhattacharyya, Davin Choo, Philips George John +1
We revisit the problem of distribution learning within the framework of learning-augmented algorithms. In this setting, we explore the scenario where a probability distribution is…
Learnability of Parameter-Bounded Bayes Nets
Arnab Bhattacharyya, Davin Choo, Sutanu Gayen +1
Bayes nets are extensively used in practice to efficiently represent joint probability distributions over a set of random variables and capture dependency relations. In a seminal p…
Online bipartite matching with imperfect advice
Davin Choo, Themis Gouleakis, Chun Kai Ling +1
We study the problem of online unweighted bipartite matching with offline vertices and online vertices where one wishes to be competitive against the optimal offline algori…
Causal Discovery under Off-Target Interventions
Davin Choo, Kirankumar Shiragur, Caroline Uhler
Causal graph discovery is a significant problem with applications across various disciplines. However, with observational data alone, the underlying causal graph can only be recove…