Search Results for author: Raghavendra Addanki

Found 5 papers, 1 papers with code

Collaborative Causal Discovery with Atomic Interventions

no code implementations NeurIPS 2021 Raghavendra Addanki, Shiva Prasad Kasiviswanathan

We introduce a new Collaborative Causal Discovery problem, through which we model a common scenario in which we have multiple independent entities each with their own causal graph, and the goal is to simultaneously learn all these causal graphs.

Causal Discovery

How to Design Robust Algorithms using Noisy Comparison Oracle

no code implementations12 May 2021 Raghavendra Addanki, Sainyam Galhotra, Barna Saha

Metric based comparison operations such as finding maximum, nearest and farthest neighbor are fundamental to studying various clustering techniques such as $k$-center clustering and agglomerative hierarchical clustering.

Intervention Efficient Algorithms for Approximate Learning of Causal Graphs

no code implementations27 Dec 2020 Raghavendra Addanki, Andrew Mcgregor, Cameron Musco

Our goal is to recover the directions of all causal or ancestral relations in $G$, via a minimum cost set of interventions.

Efficient Intervention Design for Causal Discovery with Latents

no code implementations ICML 2020 Raghavendra Addanki, Shiva Prasad Kasiviswanathan, Andrew Mcgregor, Cameron Musco

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.

Causal Discovery

Snomed2Vec: Random Walk and Poincaré Embeddings of a Clinical Knowledge Base for Healthcare Analytics

1 code implementation19 Jul 2019 Khushbu Agarwal, Tome Eftimov, Raghavendra Addanki, Sutanay Choudhury, Suzanne Tamang, Robert Rallo

Representation learning methods that transform encoded data (e. g., diagnosis and drug codes) into continuous vector spaces (i. e., vector embeddings) are critical for the application of deep learning in healthcare.

Link Prediction Node Classification +1

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