Sriraam Natarajan

Orcid: 0000-0001-9889-6260

Affiliations:
  • University of Texas at Dallas, Center for Machine Learning, TX, USA
  • Indiana University, School of Informatics and Computing, Bloomington, IN, USA
  • Wake Forest University, School of Medicine, NC, USA
  • University of Wisconsin Madison, Department of Computer Science, WI, USA
  • Oregon State University, School of Electrical Engineering and Computer Science, Corvallis, OR, USA (PhD 2007)


According to our database1, Sriraam Natarajan authored at least 167 papers between 2005 and 2024.

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Bibliography

2024
Explainable models via compression of tree ensembles.
Mach. Learn., 2024

A Unified Framework for Human-Allied Learning of Probabilistic Circuits.
CoRR, 2024

Credibility-Aware Multi-Modal Fusion Using Probabilistic Circuits.
CoRR, 2024

Utilizing Threat Partitioning for More Practical Network Anomaly Detection.
Proceedings of the 29th ACM Symposium on Access Control Models and Technologies, 2024

Building Expressive and Tractable Probabilistic Generative Models: A Review.
Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, 2024

On the Robustness and Reliability of Late Multi-Modal Fusion using Probabilistic Circuits.
Proceedings of the 27th International Conference on Information Fusion, 2024

Deep Tractable Probabilistic Models.
Proceedings of the 7th Joint International Conference on Data Science & Management of Data (11th ACM IKDD CODS and 29th COMAD), 2024

Modeling Multiple Adverse Pregnancy Outcomes: Learning from Diverse Data Sources.
Proceedings of the Artificial Intelligence in Medicine - 22nd International Conference, 2024

Promoting Research Collaboration with Open Data Driven Team Recommendation in Response to Call for Proposals.
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023
RePReL: a unified framework for integrating relational planning and reinforcement learning for effective abstraction in discrete and continuous domains.
Neural Comput. Appl., August, 2023

Learning with privileged and sensitive information: a gradient-boosting approach.
Frontiers Artif. Intell., February, 2023

Active feature elicitation: An unified framework.
Frontiers Artif. Intell., February, 2023

Knowledge-based Refinement of Scientific Publication Knowledge Graphs.
CoRR, 2023

MACOptions: Multi-Agent Learning with Centralized Controller and Options Framework.
CoRR, 2023

Probabilistic Flow Circuits: Towards Unified Deep Models for Tractable Probabilistic Inference.
Proceedings of the Uncertainty in Artificial Intelligence, 2023

Knowledge Intensive Learning of Cutset Networks.
Proceedings of the Uncertainty in Artificial Intelligence, 2023

Active Feature Acquisition via Human Interaction in Relational domains.
Proceedings of the 6th Joint International Conference on Data Science & Management of Data (10th ACM IKDD CODS and 28th COMAD), 2023

Never Ending Reasoning and Learning: Opportunities and Challenges.
Proceedings of the AAAI Bridge Program on Continual Causality, 2023

2022
Human-guided Collaborative Problem Solving: A Natural Language based Framework.
CoRR, 2022

Explaining Deep Tractable Probabilistic Models: The sum-product network case.
Proceedings of the International Conference on Probabilistic Graphical Models, 2022

Hybrid Deep RePReL: Integrating Relational Planning and Reinforcement Learning for Information Fusion.
Proceedings of the 25th International Conference on Information Fusion, 2022

Relational Active Feature Elicitation for DDDAS.
Proceedings of the Dynamic Data Driven Applications Systems - 4th International Conference, 2022

Relational Neural Markov Random Fields.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

An Anytime Querying Algorithm for Predicting Cardiac Arrest in Children: Work-in-Progress.
Proceedings of the Artificial Intelligence in Medicine, 2022

2021
Structure learning for relational logistic regression: an ensemble approach.
Data Min. Knowl. Discov., 2021

Explaining Deep Tractable Probabilistic Models: The sum-product network case.
CoRR, 2021

Dynamic probabilistic logic models for effective abstractions in RL.
CoRR, 2021

Bridging Graph Neural Networks and Statistical Relational Learning: Relational One-Class GCN.
CoRR, 2021

Interventional Sum-Product Networks: Causal Inference with Tractable Probabilistic Models.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Beyond Simple Images: Human Knowledge-Guided GANs for Clinical Data Generation.
Proceedings of the 18th International Conference on Principles of Knowledge Representation and Reasoning, 2021

Non-parametric Learning of Embeddings for Relational Data Using Gaifman Locality Theorem.
Proceedings of the Inductive Logic Programming - 30th International Conference, 2021

A Clustering based Selection Framework for Cost Aware and Test-time Feature Elicitation.
Proceedings of the CODS-COMAD 2021: 8th ACM IKDD CODS and 26th COMAD, 2021

Human-Guided Learning of Column Networks: Knowledge Injection for Relational Deep Learning.
Proceedings of the CODS-COMAD 2021: 8th ACM IKDD CODS and 26th COMAD, 2021

RePReL: Integrating Relational Planning and Reinforcement Learning for Effective Abstraction.
Proceedings of the Thirty-First International Conference on Automated Planning and Scheduling, 2021

A Probabilistic Approach to Extract Qualitative Knowledge for Early Prediction of Gestational Diabetes.
Proceedings of the Artificial Intelligence in Medicine, 2021

Predicting Drug-Drug Interactions from Heterogeneous Data: An Embedding Approach.
Proceedings of the Artificial Intelligence in Medicine, 2021

Relational Boosted Bandits.
Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020
Interactive Transfer Learning in Relational Domains.
Künstliche Intell., 2020

Non-parametric learning of lifted Restricted Boltzmann Machines.
Int. J. Approx. Reason., 2020

Few-Shot Induction of Generalized Logical Concepts via Human Guidance.
Frontiers Robotics AI, 2020

Causal Learning From Predictive Modeling for Observational Data.
Frontiers Big Data, 2020

Fitted Q-Learning for Relational Domains.
CoRR, 2020

Knowledge Graph Alignment using String Edit Distance.
CoRR, 2020

A Preliminary Approach for Learning Relational Policies for the Management of Critically Ill Children.
CoRR, 2020

Non-Parametric Learning of Gaifman Models.
CoRR, 2020

The Best of SIAM Data Mining 2020.
Big Data, 2020

Discriminative Non-Parametric Learning of Arithmetic Circuits.
Proceedings of the International Conference on Probabilistic Graphical Models, 2020

Knowledge Intensive Learning of Generative Adversarial Networks.
Proceedings of the ACM SIGKDD Workshop on Knowledge-infused Mining and Learning for Social Impact co-located with 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (Virtual) (SIGKDD 2020), 2020

Cost Aware Feature Elicitation.
Proceedings of the ACM SIGKDD Workshop on Knowledge-infused Mining and Learning for Social Impact co-located with 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (Virtual) (SIGKDD 2020), 2020

Lifted Hybrid Variational Inference.
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020

The Curious Case of Stacking Boosted Relational Dependency Networks.
Proceedings of the "I Can't Believe It's Not Better!" at NeurIPS Workshops, 2020

A Unified Framework for Knowledge Intensive Gradient Boosting: Leveraging Human Experts for Noisy Sparse Domains.
Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019
Planning with actively eliciting preferences.
Knowl. Based Syst., 2019

Editorial: Statistical Relational Artificial Intelligence.
Frontiers Robotics AI, 2019

One-Shot Induction of Generalized Logical Concepts via Human Guidance.
CoRR, 2019

Predicting Drug-Drug Interactions from Molecular Structure Images.
CoRR, 2019

Knowledge-augmented Column Networks: Guiding Deep Learning with Advice.
CoRR, 2019

Human-Guided Learning of Column Networks: Augmenting Deep Learning with Advice.
CoRR, 2019

Neural Networks for Relational Data.
Proceedings of the Inductive Logic Programming - 29th International Conference, 2019

Lifted Message Passing for Hybrid Probabilistic Inference.
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

Fast Relational Probabilistic Inference and Learning: Approximate Counting via Hypergraphs.
Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018
Human-Guided Learning for Probabilistic Logic Models.
Frontiers Robotics AI, 2018

On Whom Should I Perform this Lab Test Next? An Active Feature Elicitation Approach.
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018

Control Diffusion of Information Collection for Situation Understanding Using Boosting MLNs.
Proceedings of the 21st International Conference on Information Fusion, 2018

Preference-Guided Planning: An Active Elicitation Approach.
Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems, 2018

Mixed Sum-Product Networks: A Deep Architecture for Hybrid Domains.
Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

2017
Biomedical Informatics.
Proceedings of the Encyclopedia of Machine Learning and Data Mining, 2017

Combining content-based and collaborative filtering for job recommendation system: A cost-sensitive Statistical Relational Learning approach.
Knowl. Based Syst., 2017

Markov logic networks for adverse drug event extraction from text.
Knowl. Inf. Syst., 2017

Sum-Product Networks for Hybrid Domains.
CoRR, 2017

User Friendly Automatic Construction of Background Knowledge: Mode Construction from ER Diagrams.
Proceedings of the Knowledge Capture Conference, 2017

Relational Restricted Boltzmann Machines: A Probabilistic Logic Learning Approach.
Proceedings of the Inductive Logic Programming - 27th International Conference, 2017

Boosting for Postpartum Depression Prediction.
Proceedings of the Second IEEE/ACM International Conference on Connected Health: Applications, 2017

Does Race Play a Role in Invasive Procedure Treatments? An Initial Analysis.
Proceedings of the Second IEEE/ACM International Conference on Connected Health: Applications, 2017

Modeling heart procedures from EHRs: An application of exponential families.
Proceedings of the 2017 IEEE International Conference on Bioinformatics and Biomedicine, 2017

Discriminative boosted Bayes networks for learning multiple cardiovascular procedures.
Proceedings of the 2017 IEEE International Conference on Bioinformatics and Biomedicine, 2017

Identifying Parkinson's Patients: A Functional Gradient Boosting Approach.
Proceedings of the Artificial Intelligence in Medicine, 2017

Towards Problem Solving Agents that Communicate and Learn.
Proceedings of the First Workshop on Language Grounding for Robotics, 2017

Poisson Sum-Product Networks: A Deep Architecture for Tractable Multivariate Poisson Distributions.
Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017

Knowledge-Based Morphological Classification of Galaxies from Vision Features.
Proceedings of the Workshops of the The Thirty-First AAAI Conference on Artificial Intelligence, 2017

Active Preference Elicitation for Planning.
Proceedings of the Workshops of the The Thirty-First AAAI Conference on Artificial Intelligence, 2017

2016
Statistical Relational Artificial Intelligence: Logic, Probability, and Computation
Synthesis Lectures on Artificial Intelligence and Machine Learning, Morgan & Claypool Publishers, ISBN: 978-3-031-01574-8, 2016

Relational Learning for Sustainable Health.
Proceedings of the Computational Sustainability, 2016

Application of Statistical Relational Learning to Hybrid Recommendation Systems.
CoRR, 2016

Scaling Lifted Probabilistic Inference and Learning Via Graph Databases.
Proceedings of the 2016 SIAM International Conference on Data Mining, 2016

Actively Interacting with Experts: A Probabilistic Logic Approach.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2016

Learning Relational Dependency Networks for Relation Extraction.
Proceedings of the Inductive Logic Programming - 26th International Conference, 2016

Learning Through Advice-Seeking via Transfer.
Proceedings of the Inductive Logic Programming - 26th International Conference, 2016

Inductive Logic Programming Meets Relational Databases: Efficient Learning of Markov Logic Networks.
Proceedings of the Inductive Logic Programming - 26th International Conference, 2016

Identifying Rare Diseases from Behavioural Data: A Machine Learning Approach.
Proceedings of the First IEEE International Conference on Connected Health: Applications, 2016

Deep Distant Supervision: Learning Statistical Relational Models for Weak Supervision in Natural Language Extraction.
Proceedings of the Solving Large Scale Learning Tasks. Challenges and Algorithms, 2016

A Comparison of Weak Supervision methods for Knowledge Base Construction.
Proceedings of the 5th Workshop on Automated Knowledge Base Construction, 2016

Learning Continuous-Time Bayesian Networks in Relational Domains: A Non-Parametric Approach.
Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016

2015
Gradient-based boosting for statistical relational learning: the Markov logic network and missing data cases.
Mach. Learn., 2015

Poisson Dependency Networks: Gradient Boosted Models for Multivariate Count Data.
Mach. Learn., 2015

Statistical Relational Artificial Intelligence: From Distributions through Actions to Optimization.
Künstliche Intell., 2015

A Summary of the Twenty-Ninth AAAI Conference on Artificial Intelligence.
AI Mag., 2015

Reports of the AAAI 2014 Conference Workshops.
AI Mag., 2015

TAC KBP 2015 : English Slot Filling Track Relational Learning with Expert Advice.
Proceedings of the 2015 Text Analysis Conference, 2015

Transfer Learning via Relational Type Matching.
Proceedings of the 2015 IEEE International Conference on Data Mining, 2015

Anomaly Detection in Text: The Value of Domain Knowledge.
Proceedings of the Twenty-Eighth International Florida Artificial Intelligence Research Society Conference, 2015

Modeling Coronary Artery Calcification Levels from Behavioral Data in a Clinical Study.
Proceedings of the Artificial Intelligence in Medicine, 2015

Extracting Adverse Drug Events from Text Using Human Advice.
Proceedings of the Artificial Intelligence in Medicine, 2015

Learning Probabilistic Logic Models with Human Advice.
Proceedings of the 2015 AAAI Spring Symposia, 2015

Learning to Reject Sequential Importance Steps for Continuous-Time Bayesian Networks.
Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015

Active Advice Seeking for Inverse Reinforcement Learning.
Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015

Knowledge-Based Probabilistic Logic Learning.
Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015

2014
Boosted Statistical Relational Learners - From Benchmarks to Data-Driven Medicine
Springer Briefs in Computer Science, Springer, ISBN: 978-3-319-13644-8, 2014

Relational learning helps in three-way classification of Alzheimer patients from structural magnetic resonance images of the brain.
Int. J. Mach. Learn. Cybern., 2014

A Decision-Theoretic Model of Assistance.
J. Artif. Intell. Res., 2014

Population Size Extrapolation in Relational Probabilistic Modelling.
Proceedings of the Scalable Uncertainty Management - 8th International Conference, 2014

A graphical model approach to ATLAS-free mining of MRI images.
Proceedings of the 2014 SIAM International Conference on Data Mining, 2014

Relational Logistic Regression.
Proceedings of the Principles of Knowledge Representation and Reasoning: Proceedings of the Fourteenth International Conference, 2014

Statistical Relational Learning for Handwriting Recognition.
Proceedings of the Inductive Logic Programming - 24th International Conference, 2014

Effectively Creating Weakly Labeled Training Examples via Approximate Domain Knowledge.
Proceedings of the Inductive Logic Programming - 24th International Conference, 2014

Learning from Imbalanced Data in Relational Domains: A Soft Margin Approach.
Proceedings of the 2014 IEEE International Conference on Data Mining, 2014

A Deeper Empirical Analysis of CBP Algorithm: Grounding Is the Bottleneck.
Proceedings of the Statistical Relational Artificial Intelligence, 2014

Organizers.
Proceedings of the Statistical Relational Artificial Intelligence, 2014

Classification from One Class of Examples for Relational Domains.
Proceedings of the Statistical Relational Artificial Intelligence, 2014

Relational One-Class Classification: A Non-Parametric Approach.
Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, 2014

Relational Logistic Regression: The Directed Analog of Markov Logic Networks.
Proceedings of the Statistical Relational Artificial Intelligence, 2014

Preface.
Proceedings of the Statistical Relational Artificial Intelligence, 2014

2013
Exploiting symmetries for scaling loopy belief propagation and relational training.
Mach. Learn., 2013

The AAAI-13 Conference Workshops.
AI Mag., 2013

Bootstrapping Knowledge Base Acceleration.
Proceedings of The Twenty-Second Text REtrieval Conference, 2013

Knowledge Intensive Learning: Combining Qualitative Constraints with Causal Independence for Parameter Learning in Probabilistic Models.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2013

AR-Boost: Reducing Overfitting by a Robust Data-Driven Regularization Strategy.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2013

Accelerating Imitation Learning in Relational Domains via Transfer by Initialization.
Proceedings of the Inductive Logic Programming - 23rd International Conference, 2013

Guiding Autonomous Agents to Better Behaviors through Human Advice.
Proceedings of the 2013 IEEE 13th International Conference on Data Mining, 2013

Early Prediction of Coronary Artery Calcification Levels Using Machine Learning.
Proceedings of the Twenty-Fifth Innovative Applications of Artificial Intelligence Conference, 2013

Learning When to Reject an Importance Sample.
Proceedings of the Late-Breaking Developments in the Field of Artificial Intelligence, 2013

Using Commonsense Knowledge to Automatically Create (Noisy) Training Examples from Text.
Proceedings of the Statistical Relational Artificial Intelligence, 2013

Preface.
Proceedings of the Statistical Relational Artificial Intelligence, 2013

MapReduce Lifting for Belief Propagation.
Proceedings of the Statistical Relational Artificial Intelligence, 2013

2012
Gradient-based boosting for statistical relational learning: The relational dependency network case.
Mach. Learn., 2012

A relational hierarchical model for decision-theoretic assistance.
Knowl. Inf. Syst., 2012

Machine Learning for Personalized Medicine: Predicting Primary Myocardial Infarction from Electronic Health Records.
AI Mag., 2012

Aggregation and Population Growth: The Relational Logistic Regression and Markov Logic Cases.
Proceedings of the 2nd International Workshop on Statistical Relational AI (StaRAI-12), 2012

Accelarating Imitation Learning in Relational Domains via Transfer by Initialization.
Proceedings of the 2nd International Workshop on Statistical Relational AI (StaRAI-12), 2012

Integrating Human Instructions and Reinforcement Learners: An SRL Approach.
Proceedings of the 2nd International Workshop on Statistical Relational AI (StaRAI-12), 2012

Learning Relational Structure for Temporal Relation Extraction.
Proceedings of the 2nd International Workshop on Statistical Relational AI (StaRAI-12), 2012

Initial Empirical Evaluation of Anytime Lifted Belief Propagation.
Proceedings of the 2nd International Workshop on Statistical Relational AI (StaRAI-12), 2012

Lifted Online Training of Relational Models with Stochastic Gradient Methods.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2012

Multiplicative Forests for Continuous-Time Processes.
Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

A Novel Hierarchical Level Set with AR-boost for White Matter Lesion Segmentation in Diabetes.
Proceedings of the 11th International Conference on Machine Learning and Applications, 2012

A Machine Learning Pipeline for Three-Way Classification of Alzheimer Patients from Structural Magnetic Resonance Images of the Brain.
Proceedings of the 11th International Conference on Machine Learning and Applications, 2012

Statistical Relational Learning to Predict Primary Myocardial Infarction from Electronic Health Records.
Proceedings of the Twenty-Fourth Conference on Innovative Applications of Artificial Intelligence, 2012

Identifying Adverse Drug Events by Relational Learning.
Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence, 2012

2011
Imitation Learning in Relational Domains: A Functional-Gradient Boosting Approach.
Proceedings of the IJCAI 2011, 2011

Learning Markov Logic Networks via Functional Gradient Boosting.
Proceedings of the 11th IEEE International Conference on Data Mining, 2011

A machine learning based approach to improve sidechain optimization.
Proceedings of the ACM International Conference on Bioinformatics, 2011

2010
Biomedical Informatics.
Proceedings of the Encyclopedia of Machine Learning, 2010

Reports of the AAAI 2010 Conference Workshops.
AI Mag., 2010

Automating the ILP Setup Task: Converting User Advice about Specific Examples into General Background Knowledge.
Proceedings of the Inductive Logic Programming - 20th International Conference, 2010

Multi-Agent Inverse Reinforcement Learning.
Proceedings of the Ninth International Conference on Machine Learning and Applications, 2010

Exploiting Causal Independence in Markov Logic Networks: Combining Undirected and Directed Models.
Proceedings of the Statistical Relational Artificial Intelligence, 2010

2009
Counting Belief Propagation.
Proceedings of the UAI 2009, 2009

Speeding Up Inference in Markov Logic Networks by Preprocessing to Reduce the Size of the Resulting Grounded Network.
Proceedings of the IJCAI 2009, 2009

Learning Parameters for Relational Probabilistic Models with Noisy-Or Combining Rule.
Proceedings of the International Conference on Machine Learning and Applications, 2009

2008
Transfer in variable-reward hierarchical reinforcement learning.
Mach. Learn., 2008

Learning first-order probabilistic models with combining rules.
Ann. Math. Artif. Intell., 2008

Logical Hierarchical Hidden Markov Models for Modeling User Activities.
Proceedings of the Inductive Logic Programming, 18th International Conference, 2008

A context-aware personal desktop assistant.
Proceedings of the 7th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2008), 2008

2007
Exploiting prior knowledge in Intelligent Assistants - Combining relational models with hierarchies.
Proceedings of the Probabilistic, Logical and Relational Learning - A Further Synthesis, 15.04., 2007

A Decision-Theoretic Model of Assistance - Evaluation, Extensions and Open Problems.
Proceedings of the Interaction Challenges for Intelligent Assistants, 2007

2005
Learning first-order probabilistic models with combining rules.
Proceedings of the Machine Learning, 2005

Dynamic preferences in multi-criteria reinforcement learning.
Proceedings of the Machine Learning, 2005


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