Sriraam Natarajan

Predicting Drug-Drug Interactions from Heterogeneous Data: An Embedding Approach

Predicting and discovering drug-drug interactions (DDIs) using machine l...
03/19/2021 ∙ by Devendra Singh Dhami, et al. ∙ 0 ∙ share

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Interventional Sum-Product Networks: Causal Inference with Tractable Probabilistic Models

While probabilistic models are an important tool for studying causality,...
02/20/2021 ∙ by Matej Zečević, et al. ∙ 16 ∙ share

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Bridging Graph Neural Networks and Statistical Relational Learning: Relational One-Class GCN

We consider the problem of learning Graph Convolutional Networks (GCNs) ...
02/13/2021 ∙ by Devendra Singh Dhami, et al. ∙ 0 ∙ share

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Relational Boosted Bandits

Contextual bandits algorithms have become essential in real-world user i...
12/16/2020 ∙ by Ashutosh Kakadiya, et al. ∙ 1 ∙ share

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Fitted Q-Learning for Relational Domains

We consider the problem of Approximate Dynamic Programming in relational...
06/10/2020 ∙ by Srijita Das, et al. ∙ 0 ∙ share

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Knowledge Graph Alignment using String Edit Distance

In this work, we propose a novel knowledge base alignment technique base...
03/13/2020 ∙ by Navdeep Kaur, et al. ∙ 0 ∙ share

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A Preliminary Approach for Learning Relational Policies for the Management of Critically Ill Children

The increased use of electronic health records has made possible the aut...
01/13/2020 ∙ by Michael A. Skinner, et al. ∙ 0 ∙ share

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Non-Parametric Learning of Lifted Restricted Boltzmann Machines

We consider the problem of discriminatively learning restricted Boltzman...
01/09/2020 ∙ by Navdeep Kaur, et al. ∙ 0 ∙ share

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Lifted Hybrid Variational Inference

A variety of lifted inference algorithms, which exploit model symmetry t...
01/08/2020 ∙ by Yuqiao Chen, et al. ∙ 0 ∙ share

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Non-Parametric Learning of Gaifman Models

We consider the problem of structure learning for Gaifman models and lea...
01/02/2020 ∙ by Devendra Singh Dhami, et al. ∙ 0 ∙ share

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User Friendly Automatic Construction of Background Knowledge: Mode Construction from ER Diagrams

One of the key advantages of Inductive Logic Programming systems is the ...
12/16/2019 ∙ by Alexander L. Hayes, et al. ∙ 22 ∙ share

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One-Shot Induction of Generalized Logical Concepts via Human Guidance

We consider the problem of learning generalized first-order representati...
12/15/2019 ∙ by Mayukh Das, et al. ∙ 0 ∙ share

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Predicting Drug-Drug Interactions from Molecular Structure Images

Predicting and discovering drug-drug interactions (DDIs) is an important...
11/14/2019 ∙ by Devendra Singh Dhami, et al. ∙ 0 ∙ share

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Neural Networks for Relational Data

While deep networks have been enormously successful over the last decade...
08/28/2019 ∙ by Navdeep Kaur, et al. ∙ 0 ∙ share

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Knowledge-augmented Column Networks: Guiding Deep Learning with Advice

Recently, deep models have had considerable success in several tasks, es...
05/31/2019 ∙ by Mayukh Das, et al. ∙ 0 ∙ share

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Human-Guided Learning of Column Networks: Augmenting Deep Learning with Advice

Recently, deep models have been successfully applied in several applicat...
04/15/2019 ∙ by Mayukh Das, et al. ∙ 0 ∙ share

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Structure Learning for Relational Logistic Regression: An Ensemble Approach

We consider the problem of learning Relational Logistic Regression (RLR)...
08/06/2018 ∙ by Nandini Ramanan, et al. ∙ 14 ∙ share

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Preference-Guided Planning: An Active Elicitation Approach

Planning with preferences has been employed extensively to quickly gener...
04/19/2018 ∙ by Mayukh Das, et al. ∙ 0 ∙ share

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Sum-Product Networks for Hybrid Domains

While all kinds of mixed data -from personal data, over panel and scient...
10/09/2017 ∙ by Alejandro Molina, et al. ∙ 0 ∙ share

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Application of Statistical Relational Learning to Hybrid Recommendation Systems

Recommendation systems usually involve exploiting the relations among kn...
07/04/2016 ∙ by Shuo Yang, et al. ∙ 0 ∙ share

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Learning Relational Dependency Networks for Relation Extraction

We consider the task of KBP slot filling -- extracting relation informat...
07/01/2016 ∙ by Dileep Viswanathan, et al. ∙ 0 ∙ share

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Counting Belief Propagation

A major benefit of graphical models is that most knowledge is captured i...
05/09/2012 ∙ by Kristian Kersting, et al. ∙ 0 ∙ share

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Sriraam Natarajan

Featured Co-authors

Dan Roth

Yang Yu

Kaushik Roy

Balaraman Ravindran

Kristian Kersting

Shuo Yang

Tushar Khot

Janardhan Rao Doppa

Yibo Yang

Antonio Vergari

David Page

Predicting Drug-Drug Interactions from Heterogeneous Data: An Embedding Approach

Interventional Sum-Product Networks: Causal Inference with Tractable Probabilistic Models

Bridging Graph Neural Networks and Statistical Relational Learning: Relational One-Class GCN

Relational Boosted Bandits

Fitted Q-Learning for Relational Domains

Knowledge Graph Alignment using String Edit Distance

A Preliminary Approach for Learning Relational Policies for the Management of Critically Ill Children

Non-Parametric Learning of Lifted Restricted Boltzmann Machines

Lifted Hybrid Variational Inference

Non-Parametric Learning of Gaifman Models

User Friendly Automatic Construction of Background Knowledge: Mode Construction from ER Diagrams

One-Shot Induction of Generalized Logical Concepts via Human Guidance

Predicting Drug-Drug Interactions from Molecular Structure Images

Neural Networks for Relational Data

Knowledge-augmented Column Networks: Guiding Deep Learning with Advice

Human-Guided Learning of Column Networks: Augmenting Deep Learning with Advice

Structure Learning for Relational Logistic Regression: An Ensemble Approach

Preference-Guided Planning: An Active Elicitation Approach

Sum-Product Networks for Hybrid Domains

Application of Statistical Relational Learning to Hybrid Recommendation Systems

Learning Relational Dependency Networks for Relation Extraction

Counting Belief Propagation