18 citations · 100 across the 28 of their papers we have counts for
5 papers · 1 filter
Intervention Efficient Algorithms for Approximate Learning of Causal Graphs
Raghavendra Addanki, Andrew McGregor, Cameron Musco
We study the problem of learning the causal relationships between a set of observed variables in the presence of latents, while minimizing the cost of interventions on the observed…
Diverse Data Selection under Fairness Constraints
Zafeiria Moumoulidou, Andrew McGregor, Alexandra Meliou
Diversity is an important principle in data selection and summarization, facility location, and recommendation systems. Our work focuses on maximizing diversity in data selection,…
Efficient Intervention Design for Causal Discovery with Latents
Raghavendra Addanki, Shiva Prasad Kasiviswanathan, Andrew McGregor +1
We consider recovering a causal graph in presence of latent variables, where we seek to minimize the cost of interventions used in the recovery process. We consider two interventio…
Data Structures & Algorithms for Exact Inference in Hierarchical Clustering
Craig S. Greenberg, Sebastian Macaluso, Nicholas Monath +5
Hierarchical clustering is a fundamental task often used to discover meaningful structures in data, such as phylogenetic trees, taxonomies of concepts, subtypes of cancer, and casc…
Algebraic and Analytic Approaches for Parameter Learning in Mixture Models
Akshay Krishnamurthy, Arya Mazumdar, Andrew McGregor +1
We present two different approaches for parameter learning in several mixture models in one dimension. Our first approach uses complex-analytic methods and applies to Gaussian mixt…