6 citations · 9 across the 5 of their papers we have counts for
9 papers
Predicting Drug-Drug Interactions from Heterogeneous Data: An Embedding Approach
Devendra Singh Dhami, Siwen Yan, Gautam Kunapuli +2
Predicting and discovering drug-drug interactions (DDIs) using machine learning has been studied extensively. However, most of the approaches have focused on text data or textual r…
Interventional Sum-Product Networks: Causal Inference with Tractable Probabilistic Models
Matej Zečević, Devendra Singh Dhami, Athresh Karanam +2
While probabilistic models are an important tool for studying causality, doing so suffers from the intractability of inference. As a step towards tractable causal models, we consid…
Relational Boosted Bandits
Ashutosh Kakadiya, Sriraam Natarajan, Balaraman Ravindran
Contextual bandits algorithms have become essential in real-world user interaction problems in recent years. However, these algorithms rely on context as attribute value representa…
Fitted Q-Learning for Relational Domains
Srijita Das, Sriraam Natarajan, Kaushik Roy +2
We consider the problem of Approximate Dynamic Programming in relational domains. Inspired by the success of fitted Q-learning methods in propositional settings, we develop the fir…
Knowledge Graph Alignment using String Edit Distance
Navdeep Kaur, Gautam Kunapuli, Sriraam Natarajan
In this work, we propose a novel knowledge graph alignment technique based upon string edit distance that exploits the type information between entities and can find similarity bet…
Lifted Hybrid Variational Inference
Yuqiao Chen, Yibo Yang, Sriraam Natarajan +1
A variety of lifted inference algorithms, which exploit model symmetry to reduce computational cost, have been proposed to render inference tractable in probabilistic relational mo…