Publications

2020
Kokel, H., & Odom, P., & Yang, S., & Natarajan, S., A Unified Framework for Knowledge Intensive Gradient Boosting: Leveraging Human Experts for Noisy Sparse Domain, Thirty Fourth AAAI Conference on Artificial Intelligence (AAAI) 2020.
Kaur, N., & Kunapuli, G., & Natarajan, S., Non-Parametric Learning of Lifted Restricted Boltzmann Machines, International Journal of Approximate Reasoning 2020.
Chen, Y., & Yang, Y., & Natarajan, S., & Ruozzi, N., Lifted Hybrid Variational Inference, Workshop on Statistical Relational AI (StarAI) 2020.
Skinner, M.A., & Raman, L., & Shah, N., & Farhat, A., & Natarajan, S., A preliminary approach for learning relationalpolicies for the management of critically ill children, Workshop on Statistical Relational AI (StarAI) 2020.
Hayes, A.L., srlearn: A Python Library for Gradient-Boosted Statistical Relational Models, Workshop on Statistical Relational AI (StarAI) 2020.
Kaur, N., & Kunapuli, G., & Natarajan, S., Non-Parametric Learning of Lifted Restricted Boltzmann Machines, Workshop on Statistical Relational AI (StarAI) 2020.
Dhami, D.S., & Yan, S., & Kunapuli, G., & Natarajan, S., Non-Parametric Learning of Gaifman Models, Workshop on Statistical Relational AI (StarAI) 2020.
Das, M., & Ramanan, N., & Doppa, J.R., & Natarajan, S., One-Shot Induction of Generalized Logical Concepts via Human Guidance, Workshop on Statistical Relational AI (StarAI) 2020.

2019
Dhami, D.S., & Kunapuli, G., & Natarajan, S., Efficient Learning of Relational Gaifman Models using Probabilistic Logic, Workshop on Probabilistic Logic Programming (PLP) 2019.
Skinner, M.A., & Raman, L., & Shah, N., & Farhat, A., & Natarajan, S., Elicitation of probabilistic logic rules from records: A preliminary study in learning policies for the of critically ill children, Workshop on Probabilistic Logic Programming (PLP) 2019.
Dhami, D.S., & Kunapuli, G., & Page, D., & Natarajan, S., Predicting Drug-Drug Interactions from Molecular Structure Images, AAAI Fall symposium - AI for Social Good 2019.
Kaur, N., & Kunapuli, G., & Joshi, S., & Kersting, K., & Natarajan, S., Neural Network for Relational Data, 29th International Conference on Inductive Logic Programming (ILP) 2019.
Chen, Y., & Ruozzi, N., & Natarajan, S., Lifted Message Passing for Hybrid Probabilistic Inference, International Joint Conference on Artificial Intelligence (IJCAI) 2019.
Das, M., & Dhami, D.S., & Yu, Y., & Kunapuli, G., & Natarajan, S., Knowledge-augmented Column Networks: Guiding Deep Learning with Advice, Workshop on Human In the Loop Learning (HILL) 2019.
Ramanan, N., & Das, M., & Kersting, K., & Natarajan, S., Discriminative Non-Parametric Learning of Arithmetic Circuits, The Third Workshop On Tractable Probabilistic Modeling (TPM) 2019.
Ramanan, N., & Natarajan, S., Work-In-Progress: Ensemble Causal Learning for Modeling Post-Partum Depression, AAAI Spring Symposium on Beyond Curve Fitting — Causation, Counterfactuals and Imagination-Based AI 2019.
Das, M., & Dhami, D.S., & Kunapuli, G., & Kersting, K., & Natarajan, S., Fast Relational Probabilistic Inference and Learning Approximate Counting via Hypergraphs, 33rd AAAI Conference on Artificial Intelligence (AAAI) 2019.

2018
Das, M., & Odom, P., & Islam, M.R., & Doppa, J., & Roth, D., & Natarajan, S., Planning with actively eliciting preferences, Knowledge-Based Systems 2018.
Das, M., & Dhami, D.S., & Kunapuli, G., & Kersting, K., & Natarajan, S., Approximate Counting for Fast Inference and Learning in Probabilistic Programming, Proceedings of the Inaugural International Conference on Probabilistic Programming (ProbProg) 2018.
Natarajan, S., & Odom, P., & Khot, T., & Kersting, K., & Shavlik, J., Human-in-the-loop Learning for Probabilistic Programming, Proceedings of the Inaugural International Conference on Probabilistic Programming (ProbProg) 2018.
Ramanan, N., & Kunapuli, G., & Khot, T., & Fatemi, B., & Kazemi, S.M., & Poole, D., & Kersting, K., & Natarajan, S., Structure Learning for Relational Logistic Regression: An Ensemble Approach, 16th International Conference on Principles of Knowledge Representation and Reasoning (KR) 2018.
Ramanan, N., & Kunapuli, G., & Khot, T., & Fatemi, B., & Kazemi, S.M., & Poole, D., & Kersting, K., & Natarajan, S., Structure Learning for Relational Logistic Regression: An Ensemble Approach, Hybrid reasoning and Learning (KR) 2018.
Dhami, D.S., & Kunapuli, G., & Das, M., & Page, D., & Natarajan, S., Drug-Drug Interaction Discovery: Kernel Learning from Heterogeneous Similarities, IEEE Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE) 2018.
Natarajan, S., & Das, S., & Ramaman, N., & Kunapuli, G., & Radivojac, P., Whom Should I Perform the Lab Test on Next? An Active Feature Elicitation Approach, International Joint Conference on Artificial Intelligence (IJCAI) 2018.
Odom, P., & Natarajan, S., Human-Guided Learning for Probabilistic Logic Models, Frontiers in Robotics and AI (Front. Robot. AI) 2018.
Das, M., & Odom, P., & Islam, M.R., & Doppa, J., & Roth, D., & Natarajan, S., Preference- Guided Planning: An Active Elicitation Approach, International Conference on Autonomous Agents and Multiagent Systems (AAMAS) 2018.
Molina, A., & Vergari, A., & Mauro, N.D., & Esposito, F., & Natarajan, S., & Kersting, K., Mixed Sum-Product Networks: A Deep Architecture for Hybrid Domains, In Proceedings of the Conference on Artificial Intelligence (AAAI) 2018.

2017
Yang, S., & Hadiji, F., & Kersting, K., & Grannis, S., & Natarajan, S., Modeling Heart Procedures from EHRs: An Application of Exponential Families, IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM) 2017.
Ramanan, N., & Yang, S., & Grannis, S., & Natarajan, S., Discriminative Boosted Bayes Networks for Learning Multiple Cardiovascular Procedures, IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM) 2017.
Hayes, A.L., & Das, M., & Odom, P., & Natarajan, S., User Friendly Automatic Construction of Background Knowledge: Mode Construction from ER Diagrams, Knowledge Capture Conference 2017.
Natarajan, S., & Prabhakar, A., & Ramanan, N., & Bagilone, A., & Siek, K., & Connelly, K., Boosting for Post Partum Depression Prediction, IEEE Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE) 2017.
Kaur, N., & Kunapuli, G., & Khot, T., & Kersting, K., & Cohen, W., & Natarajan, S., Relational Restricted Boltzmann Machines: A Probabilistic Logic Learning Approach, International Conference on Inductive Logic Programming (ILP) 2017.
Yang, S., & Korayem, M., & Aljadda, K., & Grainger, T., & Natarajan, S., Combining content-based and collaborative filtering for job recommendation system: A cost-sensitive Statistical Relational Learning approach, Knowledge-Based Systems 2017.
Dhami, D.S., & Soni, A., & Page, D., & Natarajan, S., Identifying Parkinson's Patients : A Functional Gradient Boosting Approach, Artificial Intelligence in Medicine (AIME) 2017.
Molina, A., & Natarajan, S., & Kersting, K., Poisson Sum-Product Networks: A Deep Architecture for Tractable Multivariate Poisson Distributions, Thirty First AAAI Conference on Artificial Intelligence (AAAI) 2017.
Dhami, D.S., & Leake, D., & Natarajan, S., Knowledge-based Morphological Classification of Galaxies from Vision Features, Knowledge-Based Techniques for Problem Solving and Reasoning (AAAI) 2017.
Das, M., & Islam, M.R., & Doppa, J.R., & Roth, D., & Natarajan, S., Active Preference Elicitation for Planning, Human-Machine Collaborative Learning (AAAI) 2017.

2016
Malec, M., & Khot, T., & Nagy, J., & Blasch, E., & Natarajan, S., Inductive Logic Programming meets Relational Databases: An Application to Statistical Relational Learning, International Conference on Inductive Logic Programming (ILP) 2016.
Soni, A., & Viswanathan, D., & Shavlik, J., & Natarajan, S., Learning Relational Dependency Networks for Relation Extraction, International Conference on Inductive Logic Programming (ILP) 2016.
Odom, P., & Kumaraswamy, R., & Kersting, K., & Natarajan, S., Learning through Advice-Seeking via Transfer, International Conference on Inductive Logic Programming (ILP) 2016.
Odom, P., & Natarajan, S., Actively Interacting with Experts: A Probabilistic Logic Approach, European Conference on Machine Learning and Principles of Knowledge Discovery in Databases (ECMLPKDD) 2016.
MacLeod, H., & Yang, S., & Oakes, K., & Connelly, K., & Natarajan, S., Identifying Rare Diseases from Behavioural Data: A Machine Learning Approach, IEEE Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE) 2016.
Odom, P., & Natarajan, S., Active Advice Seeking for Inverse Reinforcement Learning, International Conference on Autonomous Agents and Multiagent Systems (AAMAS) 2016.
Das, M., & Wu, Y., & Khot, T., & Kersting, K., & Natarajan, S., Scaling Lifted Probabilistic Inference and Learning Via Graph Databases, SIAM International Conference on Data Mining (SDM) 2016.
Yang, S., & Khot, T., & Kersting, K., & Natarajan, S., Learning Continuous-Time Bayesian Networks in Relational Domains: A Non-Parametric Approach, Thirtieth AAAI Conference on Artificial Intelligence (AAAI) 2016.

2015
Kumaraswamy, R., & Odom, P., & Kersting, K., & Leake, D., & Natarajan, S., Transfer Learning via Relational Type Matching, International Conference on Data Mining (ICDM) 2015.
Das, M., & Wu, Y., & Khot, T., & Kersting, K., & Natarajan, S., Graph-based Approximate Counting for Relational Probabilistic Models, International Workshop on Statistical Relational AI (StarAI) 2015.
Kumaraswamy, R., & Odom, P., & Kersting, K., & Leake, D., & Natarajan, S., Transfer Learning Across Relational and Uncertain Domains: A Language-Bias Approach, International Workshop on Statistical Relational AI (StarAI) 2015.
Yang, S., & Kersting, K., & Terry, G., & Carr, J., & Natarajan, S., Modeling Coronary Artery Calcification Levels From Behavioral Data in a Clinical Study, Artificial Intelligence in Medicine (AIME) 2015.
Odom, P., & Bangera, V., & Khot, T., & Page, D., & Natarajan, S., Extracting Adverse Drug Events from Text using Human Advice, Artificial Intelligence in Medicine (AIME) 2015.
Odom, P., & Khot, T., & Porter, R., & Natarajan, S., Knowledge-Based Probabilistic Logic Learning, Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI) 2015.
Weiss, J., & Natarajan, S., & Page, D., Learning To Reject Sequential Importance Steps for Continuous-Time Bayesian Networks, Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI) 2015.
Odom, P., & Khot, T., & Natarajan, S., Learning Probabilistic Logic Models with Human Advice, AAAI Spring Symposium on Knowledge Representation and Reasoning 2015.
Odom, P., & Natarajan, S., Active Advice Seeking for Inverse Reinforcement Learning, AAAI Student Abstract and Poster Program (AAAI) 2015.
Khot, T., & Natarajan, S., & Kersting, K., & Shavlik, J., Gradient-based boosting for statistical relational learning: the Markov logic network and missing data cases, Machine Learning Journal (MLJ) 2015.

2014
Poyrekar, S., & Natarajan, S., & Kersting, K., A Deeper Empirical Analysis of CBP algorithm: Grounding is the Bottleneck, International Workshop on Statistical Relational AI (StarAI) 2014.
Yang, S., & Khot, T., & Kersting, K., & Kunapuli, G., & Hauser, K., & Natarajan, S., Learning from Imbalanced Data in Relational Domains: A Soft Margin Approach, International Conference on Data Mining (ICDM) 2014.
Khot, T., & Natarajan, S., & Shavlik, J., Relational One-Class Classification: A non-parametric approach, Twenty-Eighth AAAI Conference on Artificial Intelligence (AAAI) 2014.
Natarajan, S., & Leiva, J.M.P., & Khot, T., & Kersting, K., & Re, C., & Shavlik, J., Effectively creating weakly labeled training examples via approximate domain knowledge, International Conference in Inductive Logic Programming (ILP) 2014.
Poole, D., & Buchman, D., & Kazemi, S.M., & Kersting, K., & Natarajan, S., Population Size Extrapolation in Relational Probabilistic Modelling, Scalable Uncertainty Management (SUM) 2014.
Fern, A., & Natarajan, S., & Judah, K., & Tadepalli, P., A Decision-Theoretic Model of Assistance, Journal Of Artificial Intelligence Research (JAIR) 2014.
Kazemi, S.M., & Buchman, D., & Kersting, K., & Natarajan, S., & Poole, D., Relational Logistic Regression, International Conference on Principles of Knowledge Representation and Reasoning (KR) 2014.
Magnano, C., & Soni, A., & Natarajan, S., & Kunapuli, G., A graphical model approach to ATLAS-free mining of MRI images, SIAM International Conference on Data Mining (SDM) 2014.

2013
Ahmadi, B., & Kersting, K., & Mladenov, M., & Natarajan, S., Exploring Symmetries for Scaling Loopy Belief Propagation and Relational Training, Machine Learning Journal (MLJ) 2013.
Natarajan, S., & Saha, B.N., & Joshi, S., & Edwards, A., & Moody, E., & Khot, T., & Kersting, K., & Whitlow, C.T., & Maldjian, J.A., 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.
Kunapuli, G., & Odom, P., & Shavlik, J., & Natarajan, S., Guiding Autonomous Agents to Better Behaviors through Human Advice, IEEE International Conference on Data Mining (ICDM) 2013.
Natarajan, S., & Kersting, K., & Ip, E., & Jacobs, D., & Carr, J., Early Prediction of Coronary Artery Calcification Levels Using Machine Learning, AAAI conference on Innovative Applications in AI (IAAI) 2013.
Saha, B., & Kunapuli, G., & Ray, N., & Maldjian, J., & Natarajan, S., AR-Boost: Reducing Overfitting by a Robust Data-Driven Regularization Strategy, European Conference on Machine Learning, (ECMLPKDD) 2013.
Natarajan, S., & Odom, P., & Joshi, S., & Khot, T., & Kersting, K., & Tadepalli, P., Accelarating Imitation Learning in Relational Domains via Transfer by Initialization, International Conference on Inductive Logic Programming (ILP) 2013.
Khot, T., & Natarajan, S., & Kersting, K., & Shavlik, J., Learning Relational Probabilistic Models from Partially Observed Data - Opening the Closed-World Assumption, International Conference on Inductive Logic Programming (ILP) 2013.
Weiss, J., & Natarajan, S., & Page, D., Learning When to Reject an Importance Sample, Late-Breaking Paper (AAAI) 2013.

2012
Weiss, J., & Natarajan, S., & Peissig, P., & McCarty, C., & Page, D., Statistical Relational Learning to Predict Primary Myocardial Infarction from Electronic Health Records, AI Magazine 2012.
Weiss, J., & Natarajan, S., & Page, D., Multiplicative Forests for Continuous-Time Processes, Neural Information Processing Systems (NIPS) 2012.
Ahmadi, B., & Kersting, K., & Natarajan, S., Lifted Online Training of Relational Models with Stochastic Gradient Methods, European Conference on Machine Learning, (ECMLPKDD) 2012.
Natarajan, S., & Kersting, K., & Joshi, S., & Saldana, S., & Ip, E., & Jacobs, D., & Carr, J., Early Prediction of Coronary Artery Calcification Levels Using Statistical Relational Learning, ICML Workshop on Machine Learning for Clinical Data Analysis 2012.
Weiss, J., & Natarajan, S., & Peissig, P., & McCarty, C., & Page, D., Statistical Relational Learning to Predict Primary Myocardial Infarction from Electronic Health Records, AAAI conference on Innovative Applications in AI (IAAI) 2012.
Page, D., & Natarajan, S., & Costa, V.S., & Peissig, P., & Barnard, A., & Caldwell, M., Identifying Adverse Drug Events from Multi-Relational Healthcare Data, Twenty-Sixth Conference on Artificial Intelligence (AAAI) 2012.
Saha, B.N., & Natarajan, S., & Kota, G., & Whitlow, C.T., & Bowden, D.W., & Divers, J., & Freedman, B.I., & Maldjian, J.A., A Novel Hierarchical Level Set with AR-Boost for White Matter Lesion Segmentation in Diabetes, International Conference on Machine Learning and Applications (ICMLA) 2012.
Natarajan, S., & Joshi, S., & Saha, B.N., & Edwards, A., & Moody, E., & Khot, T., & Kersting, K., & Whitlow, C.T., & Maldjian, J.A., A Machine Learning Pipeline for Three-way Classification of Alzheimer Patients from Structural Magnetic Resonance Images of the Brain, International Conference on Machine Learning and Applications (ICMLA) 2012.
Saha, B.N., & Whitlow, C.T., & Kota, G., & Moody, E., & Natarajan, S., & Bowden, D.W., & Divers, J., & Freedman, B.I., & Maldjian, J.A., Hierarchical Level Sets with Boosting for White Matter Lesion Segmentation in Diabetes, Radiological Society of North America Annual Meeting 2012.
Maldjian, J.A., & Whitlow, C.T., & Saha, B.N., & Kota, G., & Moody, E., & Bowden, D.W., & Divers, J., & Freedman, B.I., Evaluation of Automated White Matter Lesion Segmentation in Diabetes, Radiological Society of North America Annual Meeting 2012.
Ahmadi, B., & Kersting, K., & Natarajan, S., Lifted Parameter Learning in Relational Models, ICML Workshop on Statistical Relational Learning (SRL) 2012.
Khot, T., & Natarajan, S., & Kersting, K., & Shavlik, J., Structure Learning with Hidden Data in Relational Domains, ICML Workshop on Statistical Relational Learning (SRL) 2012.
Natarajan, S., & Odom, P., & Joshi, S., & Khot, T., & Kersting, K., & Tadepalli, P., Accelarating Imitation Learning in Relational Domains via Transfer by Initialization, International Workshop on Statistical Relational AI 2012.
Freedman, R.G., & Braz, R.d.S., & Bui, H., & Natarajan, S., Initial Empirical Evaluation of Anytime Lifted Belief Propagation, International Workshop on Statistical Relational AI 2012.
Khot, T., & Srivastava, S., & Natarajan, S., & Shavlik, J., Learning Relational Structure for Temporal Relation Extraction, International Workshop on Statistical Relational AI 2012.
N, P.K.V., & Manimaran, S.S., & Ravindran, B., & Natarajan, S., Integrating Human Instructions and Reinforcement Learners: An SRL Approach, International Workshop on Statistical Relational AI 2012.
Poole, D., & Buchman, D., & Natarajan, S., & Kersting, K., Aggregation and Population Growth: The Relational Logistic Regression and Markov Logic Cases, International Workshop on Statistical Relational AI 2012.

2011
Natarajan, S., & Khot, T., & Kersting, K., & Gutmann, B., & Shavlik, J., Gradient-based Boosting for Statistical Relational Learning: The Relational Dependency Network Case, Invited contribution to special issue of Machine Learning Journal (MLJ) 2011.
Natarajan, S., & Tadepalli, P., & Fern, A., A Relational Hierarchical Model of Decision-Theoretic Assistance, Knowledge and Information Systems (KAIS) 2011.
Natarajan, S., & Joshi, S., & Tadepalli, P., & Kersting, K., & Shavlik, J., Imitation Learning in Relational Domains: A Functional-Gradient Boosting Approach, International Joint Conference in AI (IJCAI) 2011.
Khot, T., & Natarajan, S., & Kersting, K., & Shavlik, J., Learning Markov Logic Networks via Functional Gradient Boosting, International Conference in Data Mining (ICDM) 2011.
Subramaniam, S., & Natarajan, S., & Senes, A., A Machine Learning based Approach to Improve Protein Sidechain Optimization, ACM Conference on Bioinformatics, Computational Biology and Biomedicine (ACM BCB) 2011.
Natarajan, S., & Joshi, S., & Tadepalli, P., & Kersting, K., & Shavlik, J., Imitation Learning in Relational Domains Using Functional Gradient Boosting, The Learning Workshop 2011.

2010
Natarajan, S., & Page, D., Machine Learning for High-Throughput Biomedical Data: Lessons Learned, Machine Learning Encyclopedia 2010.
Natarajan, S., & Khot, T., & Lowd, D., & Kersting, K., & Tadepalli, P., & Shavlik, J., Exploiting Causal Independence in Markov Logic Networks: Combining Undirected and Directed Models, European Conference on Machine Learning (ECML) 2010.
Natarajan, S., & Khot, T., & Kersting, K., & Gutmann, B., & Shavlik, J., Boosting Relational Dependency Networks, International Conference on Inductive Logic Programming (ILP) 2010.
Natarajan, S., & Kunapuli, G., & Judah, K., & Tadepalli, P., & Kersting, K., & Shavlik, J., Multi Agent Inverse Reinforcement Learning, IEEE Conference on Machine Learning and Applications (ICMLA) 2010.
Walker, T., & Kunapuli, G., & Natarajan, S., & Shavlik, J., & Page, D., Automating the ILP Setup Task: Converting User Advice about Specific Examples into General Background Knowledge, International Conference on Inductive Logic Programming (ILP) 2010.
Natarajan, S., & Kunapuli, G., & Judah, K., & Tadepalli, P., & Kersting, K., & Shavlik, J., Multi-Agent Inverse Reinforcement Learning, The Learning Worshop 2010.

2009
Natarajan, S., & Tadepalli, P., & Dietterich, T.G., & Fern, A., Learning First-Order Probabilistic Models with Combining Rules, Annals of Mathematics and AI, Special Issue on Probabilistic Relational Learning 2009.
Shavlik, J., & Natarajan, S., 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.
Natarajan, S., & Tadepalli, P., & Kunapuli, G., & Shavlik, J., Learning Parameters for Relational Probabilistic Models with Noisy-Or Combining Rule, IEEE Conference on Machine Learning and Applications (ICML-A) 2009.
Kersting, K., & Ahmadi, B., & Natarajan, S., Counting Lifted Belief Propagation, International Conference on Uncertainty in AI (UAI) 2009.
Natarajan, S., & Kunapuli, G., & Reilly, C.O., & Maclin, R., & Walker, T., & Page, D., & Shavlik, J., ILP for Bootstrapped Learning: A Layered Approach to Automating the ILP Setup Problem, International Conference on Inductive Logic Programming 2009.
Natarajan, S., & Tadepalli, P., & Kunapuli, G., & Shavlik, J., Knowledge Intensive Learning: Directed vs. Undirected SRL Models, International Workshop in SRL 2009.
Braz, R.d.S., & Natarajan, S., & Bui, H., & Shavlik, J., & Russell, S., Anytime Lifted Belief Propagation, International Workshop in SRL 2009.

2008
Mehta, N., & Natarajan, S., & Tadepalli, P., & Fern, A., Transfer in Variable Reward Hierarchical Reinforcement Learning, Invited contribution to Inductive transfer in Machine Learning 2008.
Natarajan, S., & H.Bui, H., & Tadepalli, P., & Kersting, K., & Wong, W., Logical Hierarchical Hidden Markov Models for User Activity Recognition, International Conference on Inductive Logic Programming 2008.

2007
Natarajan, S., & Tadepalli, P., & Fern, A., A Relational Hierarchical Model of Decision-Theoretic Assistance, Proceedings of the International Conference on Inductive Logic Programming 2007.
Fern, A., & Natarajan, S., & Judah, K., & Tadepalli, P., A Decision theoretic model of Assistance, International Joint Conference in Artificial Intelligence (IJCAI) 2007.
Natarajan, S., & Judah, K., & Tadepalli, P., & Fern, A., A Decision-Theoretic Model of Assistance - Evaluation, Extensions and Open Problems, AAAI Spring Symposium on Interaction Challenges for Intelligent Assistants 2007.
Natarajan, S., & Tadepalli, P., & Fern, A., Exploiting prior knowledge in Intelligent Assistants - Combining relational models with hierarchies - Extended Abstract, Proceedings of the Dagstuhl Seminar on Probabilistic, Logical and Relational Learning 2007.

2006
Fern, A., & Natarajan, S., & Judah, K., & Tadepalli, P., A Decision theoretic model of Assistance, Modeling Others from Observations workshop in AAAI 2006.
Natarajan, S., & Wong, W., & Tadepalli, P., Structure Refinement in First Order Conditional Influence Language, Open Problems in Statistical Relational Learning, ICML 2006.

2005
Natarajan, S., & Tadepalli, P., & Altendorf, E., & Dietterich, T.G., & Fern, A., & Restificar, A., Learning First-Order Probabilistic Models with Combining Rules, 22nd International Conference on Machine Learning (ICML) 2005.
Natarajan, S., & Tadepalli, P., Dynamic Preferences in Multi-Criteria Reinforcement Learning, 22nd International Conference on Machine Learning (ICML) 2005.