Journal Papers
Sriraam Natarajan, Vishal Bangera, Tushar Khot, Jose Picado, Anurag Wazalwar, Vitor Santos Costa, David Page, Michael Caldwell, Markov Logic Networks for Adverse Drug Event Extraction from Text, Knowledge and Information Systems (KAIS), 2016.
Kristian Kersting and Sriraam Natarajan, Statistical Relational Artificial Intelligence: From Distributions through Actions to Optimization, Kunstliche Intelligenz, Special Issue on Advances in Autonomous Learning, Springer 2015.
Fabian Hadiji, Alejandro Molina, Sriraam Natarajan and Kristian Kersting, Poisson Dependency Networks - Gradient Boosted Models for Multivariate Count Data, ECML-PKDD Journal Track, 2015.
Tushar Khot, Sriraam Natarajan, Kristian Kersting, Bernd Gutmann and Jude Shavlik, Gradient-based Boosting for Statistical Relational Learning: The Markov Logic Network and Missing Data Cases, Machine Learning Journal, 2015.
Alan Fern, Sriraam Natarajan, Kshitij Judah and Prasad Tadepalli, A Decision-Theoretic Model of Assistance, Journal Of Artificial Intelligence Research (JAIR), 2014.
Babak Ahmadi, Kristian Kersting, Martin Mladenov and Sriraam Natarajan, Exploiting Symmetries for Scaling Loopy Belief Propagation and Relational Training, Machine Learning Journal, 2013.
Sriraam Natarajan, Baidya N. Saha, Saket Joshi, Adam Edwards, Elizabeth Moody, Tushar Khot, Kristian Kersting, Christopher T. Whitlow and Joseph A. Maldjian. Relational Learning helps in Three-way Classification of Alzheimer Patients from Structural Magnetic Resonance Images of the Brain (draft). International Journal of Machine Learning and Cybernetics, Springer 2013.
Sriraam Natarajan, Tushar Khot, Kristian Kersting, Bernd Gutmann and Jude Shavlik. Gradient-based Boosting for Statistical Relational Learning: The Relational Dependency Network Case, Special issue of Machine Learning Journal (MLJ), Volume 86, Number 1, 25-56, 2012.
Sriraam Natarajan, Prasad Tadepalli and Alan Fern, A Relational Hierarchical Model of Decision-Theoretic Assistance Knowledge and Information Systems(KAIS) 2011.
Jeremy Weiss, Sriraam Natarajan, Peggy Peissig, Catherine McCarty and David Page, Statistical Relational Learning to Predict Primary Myocardial Infarction from Electronic Health Records, AI Magazine 2012.
Sriraam Natarajan,Prasad Tadepalli, Thomas G. Dietterich and Alan Fern. Learning First-Order Probabilistic Models with Combining Rules . Annals of Mathematics and AI, Special Issue on Probabilistic Relational Learning 2009.
Neville Mehta, Sriraam Natarajan, Prasad Tadepalli and Alan Fern. Transfer in Variable Reward Hierarchical Reinforcement Learning. Special issue on Inductive transfer in Machine Learning, Machine Learning Journal, 2008.
Books
Luc De Raedt, Kristian Kersting, Sriraam Natarajan and David Poole, Statistical Relational Artificial Intelligence
Logic, Probability, and Computation. Morgan & Claypool Publishers, Synthesis Lectures on Artificial Intelligence and Machine Learning, ISBN: 9781627058414, 2016. 
Sriraam Natarajan, Tushar Khot, Kristian Kersting and Jude Shavlik, Boosted Statistical Relational Learners: From Benchmarks to Data-Driven Medicine
. SpringerBriefs in Computer Science, ISBN: 978-3-319-13643-1, 2015.
Sriraam Natarajan, Intelligent Assistants - A Decision-Theoretic Model: Effective Decision-Theoretic Assistance Through Relational Hierarchical Models. VDM-Verlag 2009
Conference Papers
Sriraam Natarajan, Annu Prabhakar, Nandini Ramanan, Anna Bagilone, Katie Siek, and Kay Connelly, Boosting for Post Partum Depression Prediction, IEEE Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE), 2017.
Devendra Singh Dhami, Ameet Soni, David Page, Sriraam Natarajan, Identifying Parkinson's Patients : A Functional Gradient Boosting Approach, Artificial Intelligence in Medicine (AIME) (2017).
Aljenadro Molina, and Sriraam Natarajan, and Kristian Kersting, Poisson Sum-Product Networks: A Deep Architecture for Tractable Multivariate Poisson Distributions, Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI), AAAI Press, 2017.
Phillip Odom, and Sriraam Natarajan, Actively Interacting with Experts: A Probabilistic Logic Approach, European Conference on Machine Learning and Principles of Knowledge Discovery in Databases (ECMLPKDD) 2016.
Marcin Malec, Tushar Khot, James Nagy, Erik Blasch, and Sriraam Natarajan, Inductive Logic Programming meets Relational Databases: An Application to Statistical Relational Learning, International Conference on Inductive Logic Programming (ILP), 2016. (Best Student Paper)
Ameet Soni, Dileep Viswanathan, Jude Shavlik, and Sriraam Natarajan, Learning Relational Dependency Networks for Relation Extraction, International Conference on Inductive Logic Programming (ILP), 2016.
Phillip Odom, Raksha Kumaraswamy, Kristian Kersting, and Sriraam Natarajan,Learning through Advice-Seeking via Transfer, International Conference on Inductive Logic Programming (ILP), 2016.
Haley MacLeod, Shuo Yang, Kim Oakes, Kay Connelly and Sriraam Natarajan, Identifying Rare Diseases from Behavioural Data:A Machine Learning Approach, First IEEE Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE), 2016.
Phillip Odom and Sriraam Natarajan, Active Advice Seeking for Inverse Reinforcement Learning , International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2016.
Mayukh Das, Yuqing Wu, Tushar Khot, Kristian Kersting and Sriraam Natarajan, Scaling Lifted Probabilistic Inference and Learning Via Graph Databases, SIAM International Conference on Data Mining (SDM), 2016.
Shuo Yang, Tushar Khot, Kristian Kersting and Sriraam Natarajan, Learning Continuous-Time Bayesian Networks in Relational Domains: A Non-Parametric Approach, 30th AAAI Conference on Artificial Intelligence (AAAI), 2016.
Raksha Kumaraswamy, Phillip Odom, Kristian Kersting, David Leake, and Sriraam Natarajan, Transfer Learning via Relational Type Matching, International Conference on Data Mining (ICDM), 2015.
Shuo Yang, Kristian Kersting, Greg Terry, Jeffrey Carr and Sriraam Natarajan, Modeling Coronary Artery Calcification Levels From Behavioral Data in a Clinical Study, Artificial Intelligence in Medicine (AIME), 2015.
Phillip Odom, Vishal Bangera, Tushar Khot, David Page and Sriraam Natarajan, Extracting Adverse Drug Events from Text using Human Advice, Artificial Intelligence in Medicine (AIME), 2015.
Phillip Odom, Tushar Khot, Reid Porter, and Sriraam Natarajan, Knowledge-Based Probabilistic Logic Learning, Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI), 2015.
Jeremy Weiss, Sriraam Natarajan, and David Page, Learning To Reject Sequential Importance Steps for Continuous-Time Bayesian Networks, Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI), 2015.
Shuo Yang, Tushar Khot, Kristian Kersting, Gautam Kunapuli, Kris Hauser and Sriraam Natarajan, Learning from Imbalanced Data in Relational Domains: A Soft Margin Approach, International Conference on Data Mining (ICDM), 2014.
Tushar Khot, Sriraam Natarajan and Jude Shavlik, Relational One-Class Classification: A Non-Parametric Approach, Twenty-Eighth AAAI Conference on Artificial Intelligence (AAAI), 2014.
Sriraam Natarajan, Jose Manuel Picado Leiva, Tushar Khot, Kristian Kersting, Christopher Re and Jude Shavlik, Effectively creating weakly labeled training examples via approximate domain knowledge, International Conference on Inductive Logic Programming, (ILP), 2014.
David Poole, David Buchman, Seyed Mehran Kazemi, Kristian Kersting and Sriraam Natarajan,Population Size Extrapolation in Relational Probabilistic Modelling, Scalable Uncertainty Management (SUM) 2014.
Seyed Mehran Kazemi, David Buchman, Kristian Kerstin, Sriraam Natarajan and David Poole, Relational Logistic Regression, International Conference on Principles of Knowledge Representation and Reasoning (KR), 2014.
Chris Magnano, Ameet Soni, Sriraam Natarajan and Gautam Kunapuli, A graphical model approach to ATLAS-free mining of MRI images, SIAM International Conference on Data Mining, 2014.
Gautam Kunapuli, Phillip Odom, Jude Shavlik and Sriraam Natarajan, Guiding Autonomous Agents to Better Behaviors through Human Advice, IEEE International Conference on Data Mining (ICDM) 2013.
Sriraam Natarajan, Kristian Kersting, Edward Ip, David Jacobs and Jeff Carr, Early Prediction of Coronary Artery Calcification Levels Using Machine Learning, AAAI conference on Innovative Applications in AI (IAAI) 2013.
Shuo Yang and Sriraam Natarajan, Knowledge Intensive Learning: Combining Qualitative Constraints with Causal Independence for Parameter Learning in Probabilistic Models, European Conference on Machine Learning, (ECMLPKDD) 2013.
Baidya Saha, Gautam Kunapuli, Nilanjan Ray, Joseph Maldjian and Sriraam Natarajan, AR-Boost: Reducing Overfitting by a Robust Data-Driven Regularization Strategy, European Conference on Machine Learning, (ECMLPKDD) 2013.
Sriraam Natarajan, Phillip Odom, Saket Joshi, Tushar Khot, Kristian Kersting and Prasad Tadepalli, Accelarating Imitation Learning in Relational Domains via Transfer by Initialization, International Conference on Inductive Logic Programming (ILP), 2013.
Tushar Khot, Sriraam Natarajan, Kristian Kersting and Jude Shavlik, Learning Relational Probabilistic Models from Partially Observed Data - Opening the Closed-World Assumption, International Conference on Inductive Logic Programming (ILP), 2013.
Jeremy Weiss, Sriraam Natarajan and David Page, Learning When to Reject an Importance Sample, Late-Breaking Paper, AAAI, 2013.
Jeremy Weiss, Sriraam Natarajan and David Page, Multiplicative Forests for Continuous-Time Processes, Neural Information Processing Systems (NIPS) 2012.
Babak Ahmadi, Kristian Kersting and Sriraam Natarajan, Lifted Online Training of Relational Models with Stochastic Gradient Methods, ECML-PKDD, 2012.
Jeremy Weiss, Sriraam Natarajan, Peggy Peissig, Catherine McCarty and David Page. Statistical Relational Learning to Predict Primary Myocardial Infarction from Electronic Health Records, AAAI conference on Innovative Applications in AI (IAAI) 2012.
David Page, Vitor Santos Costa, Sriraam Natarajan, Peggy Peissig, Aubrey Barnard, and Michael Caldwell. Identifying Adverse Drug Events from Multi-Relational Healthcare Data. Twenty-Sixth Conference on Artificial Intelligence, AAAI-12.
Sriraam Natarajan, Saket Joshi, Baidya N. Saha, Adam Edwards, Elizabeth Moody, Tushar Khot, Kristian Kersting, Christopher T. Whitlow and Joseph A. Maldjian. A Machine Learning Pipeline for Three-way Classification of Alzheimer Patients from Structural Magnetic Resonance Images of the Brain. IEEE Conference on Machine Learning and Applications (ICMLA), 2012.
Baidya N. Saha, Sriraam Natarajan, Gopi Kota, Christopher T. Whitlow, Donald W. Bowden, Jasmin Divers, Barry I. Freedman and Joseph A. Maldjian. A Novel Hierarchical Level Set with AR-Boost for White Matter Lesion Segmentation in Diabetes. IEEE Conference on Machine Learning and Applications (ICMLA), 2012.
Sriraam Natarajan, Saket Joshi, Prasad Tadepalli, Kristian Kersting, and Jude Shavlik. Imitation Learning in Relational Domains: A Functional-Gradient Boosting Approach , International Joint Conference in AI (IJCAI) 2011.
Tushar Khot, Sriraam Natarajan, Kristian Kersting, and Jude Shavlik. Learning Markov Logic Networks via Functional Gradient Boosting, International Conference in Data Mining (ICDM) 2011.
Sabareesh Subramaniam, Sriraam Natarajan, and Alessandro Senes. A Machine Learning based Approach to Improve Protein Sidechain Optimization, ACM Conference on Bioinformatics, Computational Biology and Biomedicine (ACM BCB) 2011.
Sriraam Natarajan, Tushar Khot, Daniel Lowd, Kristian Kersting, Prasad Tadepalli and Jude Shavlik. Exploiting Causal Independence in Markov Logic Networks: Combining Undirected and Directed Models , European Conference on Machine Learning (ECML) 2010.
Sriraam Natarajan, Tushar Khot, Kristian Kersting, Bernd Gutmann and Jude Shavlik. Boosting Relational Dependency Networks, International Conference on Inductive Logic Programming (ILP) 2010.
Sriraam Natarajan, Gautam Kunapuli, Kshitij Judah, Prasad Tadepalli, Kristian Kersting and Jude Shavlik. Multi Agent Inverse Reinforcement Learning, IEEE Conference on Machine Learning and Applications (ICMLA) 2010.
Trevor Walker, Gautam Kunapuli, Sriraam Natarajan, Jude Shavlik and David Page. Automating the ILP Setup Task: Converting User Advice about Specific Examples into General Background Knowledge , International Conference on Inductive Logic Programming (ILP) 2010.
Jude Shavlik, Sriraam Natarajan. Speeding up Inference in Markov Logic Networks By Preprocessing to Reduce the Size of the Resulting Grounded Network , International Joint Conference in Artificial Intelligence (IJCAI) 2009.
Sriraam Natarajan, Prasad Tadepalli, Gautam Kunapuli, Jude Shavlik. Learning Parameters for Relational Probabilistic Models with Noisy-Or Combining Rule, IEEE Conference on Machine Learning and Applications (ICML-A) 2009
Kristian Kersting, Babak Ahmadi, Sriraam Natarajan. Counting Lifted Belief Propagation , International Conference on Uncertainty in AI (UAI) 2009
Sriraam Natarajan, Gautam Kunapuli, Ciaran O' Reilly, Rich Maclin, Trevor Walker, David Page, and Jude Shavlik. ILP for Bootstrapped Learning: A Layered Approach to Automating the ILP Setup Problem , International Conference on Inductive Logic Programming 2009.
Sriraam Natarajan, Hung H.Bui, Prasad Tadepalli, Kristian Kersting, Weng-Keen Wong. Logical Hierarchical Hidden Markov Models for User Activity Recognition , International Conference on Inductive Logic Programming 2008.
Sriraam Natarajan, Prasad Tadepalli and Alan Fern. A Relational Hierarchical Model of Decision-Theoretic Assistance. Proceedings of the International Conference on Inductive Logic Programming, (ILP 2007).
Alan Fern, Sriraam Natarajan, Kshitij Judah and Prasad Tadepalli, A Decision theoretic model of Assistance, International Joint Conference in Artificial Intelligence (IJCAI 2007).
Sriraam Natarajan, Prasad Tadepalli, Eric Altendorf, Thomas G. Dietterich, Alan Fern and Angelo Restificar. Learning First-Order Probabilistic Models with Combining Rules. The 22nd International Conference on Machine Learning (ICML 2005).
Sriraam Natarajan and Prasad Tadepalli. Dynamic Preferences in Multi-Criteria Reinforcement Learning.The 22nd International Conference on Machine Learning (ICML 2005).
Book Chapters
Sriraam Natarajan, Ameet Soni, Anurag Wazalwar, Dileep Viswanathan and Kristian Kersting. Deep Distant Supervision: Learning Statistical Relational Models for Weak Supervision in Natural Language Extraction. Morik Festschrift, LNAI 9580 2016.
Sriraam Natarajan, Peggy Peissig and David Page. Relational Learning for Sustainable Health. Computational Sustainability, Springer series "Studies in Computational Intelligence" 2015.
Sriraam Natarajan and David Page. Machine Learning for High-Throughput Biomedical Data: Lessons Learned. Machine Learning Encyclopedia, 2010.
WorkShop Papers
Sriraam Natarajan, Jose Picado, Tushar Khot, Kristian Kersting, Christopher Re and Jude Shavlik. Using Commonsense Knowledge to Automatically Create (Noisy) Training Examples from Text, International Workshop on Statistical Relational AI, 2012.
Sriraam Natarajan, Phillip Odom, Saket Joshi, Tushar Khot, Kristian Kersting and Prasad Tadepalli. Accelerating Imitation Learning in Relational Domains via Transfer by Initialization, International Workshop on Statistical Relational AI, 2012.
Richard G. Freedman, Rodrigo de Salvo Braz, Hung Bui and Sriraam Natarajan. Initial Empirical Evaluation of Anytime Lifted Belief Propagation, International Workshop on Statistical Relational AI, 2012.
Tushar Khot, Siddharth Srivastava, Sriraam Natarajan and Jude Shavlik. Learning Relational Structure for Temporal Relation Extraction, International Workshop on Statistical Relational AI, 2012.
Pradyot Korupolu V N, S S Manimaran, Balaraman Ravindran and Sriraam Natarajan. Integrating Human Instructions and Reinforcement Learners : An SRL Approach, International Workshop on Statistical Relational AI, 2012.
David Poole, David Buchman, Sriraam Natarajan and Kristian Kersting. Aggregation and Population Growth: The Relational Logistic Regression and Markov Logic Cases, International Workshop on Statistical Relational AI, 2012.
Babak Ahmadi, Kristian Kersting and Sriraam Natarajan. Lifted Parameter Learning in Relational Models. ICML Workshop on Statistical Relational Learning (SRL), 2012.
Tushar Khot, Sriraam Natarajan, Kristian Kersting and Jude Shavlik. Structure Learning with Hidden Data in Relational Domains. ICML Workshop on Statistical Relational Learning (SRL), 2012.
Sriraam Natarajan, Kristian Kersting, Saket Joshi, Santiago Saldana, Edward Ip, David Jacobs and Jeffery Carr.Early Prediction of Coronary Artery Calcification Levels Using Statistical Relational Learning. ICML Workshop on Machine Learning for Clinical Data Analysis, 2012.
Sriraam Natarajan, Saket Joshi, Prasad Tadepalli, Kristian Kersting, and Jude Shavlik Imitation Learning in Relational Domains Using Functional Gradient Boosting, The Learning Workshop 2011.
Sriraam Natarajan, Gautam Kunapuli, David Page, Trevor Walker, Richard Maclin, Ciaran O'Reilly, Jude Shavlik. Learning from Human Teachers: Issues and Challenges for ILP in Bootstrap Learning AAMAS Workshop on Agents Learning Interactively from Human Teachers - 2010.
Sriraam Natarajan, Gautam Kunapuli, Kshitij Judah, Prasad Tadepalli, Kristian Kersting and Jude Shavlik. Multi-Agent Inverse Reinforcement Learning The Learning Worshop - 2010.
Sriraam Natarajan, Prasad Tadepalli Gautam Kunapuli and Jude Shavlik. Knowledge Intensive Learning: Directed vs. Undirected SRL Models. International Workshop in SRL 2009.
Rodrigo De Salvo Braz, Sriraam Natarajan, Hung Bui, Jude Shavlik, and Stuart Russell. Anytime Lifted Belief Propagation. International Workshop in SRL 2009.
Sriraam Natarajan, Irene Ong, David Haight, David Page, Vitor Santos Costa. Modeling Temporal Biomedical Data by SRL, ECML workshop on Bio-Medical Applications using SRL, 2008.
Sriraam Natarajan, Kshitij Judah, Prasad Tadepalli and Alan Fern. A Decision-Theoretic Model of Assistance - Evaluation, Extensions and Open Problems, AAAI 2007 Spring Symposium on Interaction Challenges for Intelligent Assistants, Stanford University, USA.
Sriraam Natarajan, Prasad Tadepalli and Alan Fern. Exploiting prior knowledge in Intelligent Assistants - Combining relational models with hierarchies, Extended Abstract in the Proceedings of the Dagstuhl Seminar on Probabilistic, Logical and Relational Learning - A Further Synthesis, (2007).
Sriraam Natarajan and Eric Altendorf. First Order Conditional Influence Language. Technical Report CS05-30-01 September 2005.
Alan Fern, Sriraam Natarajan, KshitijJudah and Prasad Tadepalli. A Decision theoretic model of Assistance, Modeling Others from Observations workshop in AAAI 2006.
Sriraam Natarajan, Weng-Keen Wong and Prasad Tadepalli, Structure Refinement in First Order Conditional Influence Language, Open Problems in Statistical Relational Learning, ICML 2006.
Neville Mehta, Sriraam Natarajan, Prasad Tadepalli and Alan Fern. Transfer in Variable Reward Hierarchical Reinforcement Learning. Inductive Transfer NIPS workshop 2005.
Lisa Torrey, Jude Shavlik, Sriraam Natarajan, Pavan Kuppilli and Trever Walker. Transfer in Reinforcement Learning via Markov Logic Networks. AAAI workshop on Transfer Learning for Complex Tasks 2008.
Hung Bui, Fedrico Cesari, Daniel Elenius, David Morley, Sriraam Natarajan, Shahin Saadati, Eric Yeh, and Neil Yorke-Smith. A Context-Aware Personal Desktop Assistant. Demonstrations track of Autonomous Agent and MultiAgent Systems, 2008.