Publications

All Journal Papers Conference Papers Workshop Papers Books Preprints

2026

Neel Shah, Ethan Sanford, David R. Busch, Ranveer Singh, Saurabh Mathur, Jayesh Sharma, Phillip Reeder, Sriraam Natarajan, Lakshmi Raman, EHR Sampling Interval Bias Detection and Burden of Blood Pressure Excursions: Implications for Clinical Decision Support and Model Validity in Pediatric ECMO , Information 2026.

Hrithik Suresh, Sahil Sidheekh, Vishnu Shreeram M.P, Sriraam Natarajan, Narayanan C. Krishnan, Tractable Sharpness-Aware Learning of Probabilistic Circuits, The 40th Annual AAAI Conference on Artificial Intelligence (AAAI) 2026.

2025

Santhosh G S, Akshay Govind S, Gokul S Krishnan, Balaraman Ravindran, Sriraam Natarajan, IndiCASA: A Dataset and Bias Evaluation Framework for LLMs Using Contrastive Embedding Similarity in the Indian Context, 8th AAAI/ACM Conference on AI, Ethics, and Society (AIES) 2025.

Steven Braun, Sahil Sidheekh, Antonio Vergari, Martin Mundt, Sriraam Natarajan, Kristian Kersting, Tractable Representation Learning with Probabilistic Circuits, Transactions on Machine Learning Research (TMLR) 2025.

Devendra Singh Dhami, Saurabh Mathur, Siwen Yan, Sriraam Natarajan, Representing Multi-Relational Data via Statistical Relational Learning and Graph Convolutional Networks, 13th ACM IKDD International Conference on Data Science (CODS) 2025.

Saurabh Mathur, Ranveer Singh, Michael Skinner, Predrag Radivojac, David M. Haas, Lakshmi Raman, Sriraam Natarajan, LLMs for Causal Reasoning in Medicine? A Call for Caution, 13th ACM IKDD International Conference on Data Science (CODS) 2025.

Fateme Golivand, Hikaru Shindo, Sahil Sidheekh, Kristian Kersting, Sriraam Natarajan, Human-Allied Relational Reinforcement Learning, 12th Annual Conference on Advances in Cognitive Systems (ACS) 2025.

Saurabh Mathur, Ranveer Singh, Michael Skinner, Predrag Radivojac, David M. Haas, Lakshmi Raman, Sriraam Natarajan, LLMs for Causal Reasoning in Medicine? A Call for Caution, IJCAI 2025 Workshop on User-Aligned Assessment of Adaptive AI Systems (AIA) 2025.

Shreyash Adappanavar, Krithi Shailya, Gokul S Krishnan, Sriraam Natarajan, Balaraman Ravindran, mFARM: Towards Multi-Faceted Fairness Assessment based on HARMs in Clinical Decision Support, 2025.

Saurabh Mathur, Ranveer Singh, Michael Skinner, Ethan Sanford, Neel Shah, Phillip Reeder, Lakshmi Raman, Sriraam Natarajan, LLM-guided Causal Bayesian Network construction for pediatric patients on ECMO, 23rd International Conference on Artificial Intelligence in Medicine (AIME) 2025.

Debashis Gupta, Aditi Golder, Sahil Sidheekh, Sakib Imtiaz, Luis Fernendez, Miles Silman, Greg Lersen, Fan Yang, Bob Plemmons, Sarra Alqahtani, Sriraam Natarajan, Paul Victor Pauca, Scalable Knowledge Graph Construction from Unstructured Text: A Case Study on Artisanal and Small-Scale Gold Mining, The 29th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) 2025.

Sahil Sidheekh, Pranuthi Tenali, Saurabh Mathur, Erik Blasch, Kristian Kersting, Sriraam Natarajan, Credibility-Aware Multi-Modal Fusion Using Probabilistic Circuits, The 28th International Conference on Artificial Intelligence and Statistics (AISTATS) 2025.

Nikhilesh Prabhakar, Ranveer Singh, Harsha Kokel, Sriraam Natarajan, Prasad Tadepalli, Combining Planning and Reinforcement Learning for Solving Relational Multiagent Domains, The 24th International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS) 2025.

Athresh Karanam, Saurabh Mathur, Sahil Sidheekh, Sriraam Natarajan, A Unified Framework for Human-Allied Learning of Probabilistic Circuits, The 39th Annual AAAI Conference on Artificial Intelligence (AAAI) 2025.

Sriraam Natarajan, Saurabh Mathur, Sahil Sidheekh, Wolfgang Stammer, Kristian Kersting, Human-in-the-loop or AI-in-the-loop? Automate or Collaborate?, The 39th Annual AAAI Conference on Artificial Intelligence (AAAI) 2025.

2024

Siva Likitha Valluru, Michael Widener, Biplav Srivastava, Sriraam Natarajan, Sugata Gangopadhyay, AI-assisted research collaboration with open data for fair and effective response to call for proposals, AI Magazine 2024.

Sahil Sidheekh, Pranuthi Tenali, Saurabh Mathur, Erik Blasch, Sriraam Natarajan, On the Robustness and Reliability of Late Multi-Modal Fusion using Probabilistic Circuits, The 27th International Conference on Information Fusion (FUSION) 2024.

Saurabh Mathur, Alessandro Antonucci, Sriraam Natarajan, Knowledge Intensive Learning of Credal Networks, The 40th Conference on Uncertainty in Artificial Intelligence (UAI) 2024.

Saurabh Mathur, Veerendra Gadekar, Rashika Ramola, Avery Wang, Ramachandran Thiruvengadam, David Haas, Shinjini Bhatnagar, Nitya Wadhwa, Garbhini Study Group, Predrag Radivojac, Himanshu Sinha, Kristian Kersting, Sriraam Natarajan, Modeling multiple adverse pregnancy outcomes: Learning from diverse data sources, The 22nd International Conference in Artificial Intelligence in Medicine (AIME) 2024.

Brian Ricks, Patrick Tague, Bhavani Thuraisingham, Sriraam Natarajan, Utilizing Threat Partitioning for More Practical Network Anomaly Detection, In Proceedings of the 29th ACM Symposium on Access Control Models and Technologies (SACMAT) 2024.

Siva Likitha Valluru, Biplav Srivastava, Sai Teja Paladi, Siwen Yan, Sriraam Natarajan, Promoting Research Collaboration with Open Data Driven Team Recommendation in Response to Call for Proposals, The Thirty-Sixth Annual Conference on Innovative Applications of Artificial Intelligence (IAAI/AAAI-24) (Best Application paper) 2024.

Ranveer Singh, Nikhilesh Prabhakar, Sriraam Natarajan, Prasad Tadepalli, Exploiting Relational Planning and Task-Specific Abstractions for Multiagent Reinforcement Learning in Relational Domains, Cooperative Multi-Agent Systems Decision-Making and Learning 2024.

Sahil Sidheekh, Sriraam Natarajan, Building Expressive and Tractable Probabilistic Generative Models: A Review, The 33rd International Joint Conference on Artificial Intelligence (IJCAI) 2024.

Saurabh Mathur, Alessandro Antonucci, Sriraam Natarajan, Learning Credal Conditional Probability Tables with Qualitative Knowledge, The 2nd Workshop of Deployable AI (DAI) 2024.

Saurabh Mathur, Sahil Sidheekh, Pranuthi Tenali, Erik Blasch, Kristian Kersting, Sriraam Natarajan, Credibility-aware Reliable Multi-Modal Fusion Using Probabilistic Circuits, The 2nd Workshop of Deployable AI (DAI) 2024.

2023

Neel Shah, Saurabh Mathur, Prashanth Shanmugham, Xilong Li, Ravi R Thiagarajan, Sriraam Natarajan, Lakshmi Raman, Neurologic Statistical Prognostication and Risk Assessment for Kids on Extracorporeal Membrane Oxygenation—Neuro SPARK, ASAIO Journal 2023.

Siwen Yan, Sriraam Natarajan, Saket Joshi, Roni Khardon, Prasad Tadepalli, Explainable Models via Compression of Tree Ensembles, 2023.

Siwen Yan, Phillip Odom, Rahul Pasunuri, Kristian Kersting, Sriraam Natarajan, Learning with privileged and sensitive information: a gradient-boosting approach, 2023.

Saurabh Mathur, Vibhav Gogate, Sriraam Natarajan, Knowledge Intensive Learning of Cutset Networks, The 39th Conference on Uncertainty in Artificial Intelligence (UAI) 2023.

Sahil Sidheekh, Kristian Kersting, Sriraam Natarajan, Probabilistic Flow Circuits: Towards Unified Deep Models for Tractable Probabilistic Inference, The 39th Conference on Uncertainty in Artificial Intelligence (UAI) 2023.

Saurabh Mathur, Vibhav Gogate, Sriraam Natarajan, Knowledge Intensive Learning of Cutset Networks, The Sixth Workshop On Tractable Probabilistic Modeling (TPM) 2023.

Athresh Karanam, Saurabh Mathur, Sahil Sidheekh, Sriraam Natarajan, Bayesian Learning of Probabilistic Circuits with Domain Constraints, The Sixth Workshop On Tractable Probabilistic Modeling (TPM) 2023.

Athresh Karanam, Sriraam Natarajan, Test-time active feature selection through tractable acquisition functions, The Sixth Workshop On Tractable Probabilistic Modeling (TPM) 2023.

Priscilla Yu, Michael Skinner, Ivie Esangbedo, Javier J. Lasa, Xilong Li, Sriraam Natarajan, Lakshmi Raman, Predicting Cardiac Arrest in Children with Heart Disease: A Novel Machine Learning Algorithm, Journal of Clinical Medicine 2023.

Srijita Das, Nandini Ramanan, Gautam Kunapuli, Predrag Radivojac, Sriraam Natarajan, Active feature elicitation: An unified framework, Frontiers in Artificial Intelligence 2023.

Sriraam Natarajan, Kristian Kersting, Never Ending Reasoning and Learning: Opportunities and Challenges, Continual Causality Bridge Program at AAAI 2023.

Hoyin Chu, Rashika Ramola, Shantanu Jain, David M. Haas, Sriraam Natarajan, Predrag Radivojac, Using Association Rules to Understand the Risk of Adverse Pregnancy Outcomes in a Diverse Population, Pacific Symposium on Biocomputing 2023.

Saurabh Mathur, Athresh Karanam, Predrag Radivojac, David M. Haas, Kristian Kersting, Sriraam Natarajan, Exploiting Domain Knowledge as Causal Independencies in Modeling Gestational Diabetes, Pacific Symposium on Biocomputing 2023.

Nandini Ramanan, Phillip Odom, Kristian Kersting, Sriraam Natarajan, Active Feature Acquisition via Human Interaction in Relational domains, 6th Joint International Conference on Data Science & Management of Data (CODS-COMAD) 2023.

2022

Athresh Karanam, Krishnateja Killamsetty, Harsha Kokel, Rishabh K Iyer, Orient: Submodular Mutual Information Measures for Data Subset Selection under Distribution Shift, 36th Conference on Neural Information Processing Systems (NeurIPS) 2022.

Kymberleigh A. Pagel, Hoyin Chu, Rashika Ramola, Rafael F. Guerrero, Judith H. Chung, Samuel Parry, Uma M. Reddy, Robert M. Silver, Jonathan G. Steller, Lynn M. Yee, Ronald J. Wapner, Matthew W. Hahn, Sriraam Natarajan, David M. Haas, Predrag Radivojac, Association of Genetic Predisposition and Physical Activity With Risk of Gestational Diabetes in Nulliparous Women, JAMA Network Open 2022.

Siwen Yan, Sriraam Natarajan, Saket Joshi, Roni Khardon, Prasad Tadepalli, Explainable Models via Compression of Tree Ensembles, 2022.

Yuqiao Chen, Sriraam Natarajan, Nicholas Ruozzi, Relational Neural Markov Random Fields, International Conference on Artificial Intelligence and Statistics (AISTATS) 2022.

Harsha Kokel, Sriraam Natarajan, Balaraman Ravindran, Prasad Tadepalli, Dynamic probabilistic logic models for effective task-specific abstractions in RL, The 5th Multi-disciplinary Conference on Reinforcement Learning and Decision Making (RLDM) 2022.

Harsha Kokel, Nikhilesh Prabhakar, Balaraman Ravindran, Eric Blasch, Prasad Tadepalli, Sriraam Natarajan, Hybrid Deep RePReL: Integrating Relational Planning and Reinforcement Learning for Information Fusion, IEEE 25th International Conference on Information Fusion (FUSION) 2022.

Athresh Karanam, Saurabh Mathur, David M. Haas, Predrag Radivojac, Kristian Kersting, Sriraam Natarajan, Explaining Deep Tractable Probabilistic Models: The sum-product network case, The Fifth Workshop On Tractable Probabilistic Modeling (TPM) 2022.

Athresh Karanam, Saurabh Mathur, David M. Haas, Predrag Radivojac, Kristian Kersting, Sriraam Natarajan, Explaining Deep Tractable Probabilistic Models: The sum-product network case, The 11th International Conference on Probabilistic Graphical Models (PGM) 2022.

Michael A. Skinner, Priscilla Yu, Lakshmi Raman, Sriraam Natarajan, An Anytime Querying Algorithm for Predicting Cardiac Arrest in Children: Work-in-Progress, 20th International Conference in Artificial Intelligence in Medicine(AIME) 2022.

Harsha Kokel, Mayukh Das, Rakibul Islam, Julia Bonn, Jon Cai, Soham Dan, Anjali Narayan-Chen, Prashant Jayannavar, Janardhan Rao Doppa, Julia Hockenmaier, Sriraam Natarajan, Martha Palmer, Dan Roth, LARA -- Human-guided collaborative problem solver: Effective integration of learning, reasoning and communication, The Tenth Annual Conference on Advances in Cognitive Systems (ACS) 2022.

Harsha Kokel, Sriraam Natarajan, Balaraman Ravindran, Prasad Tadepalli, RePReL: A Unified Framework for Integrating Relational Planning and Reinforcement Learning for Effective Abstraction in Discrete and Continuous Domains, Neural Computing and Applications 2022.

2021

Guy Van den Broeck, Kristian Kersting, Sriraam Natarajan, David Poole, An Introduction to Lifted Probabilistic Inference, 2021.

Devendra Singh Dhami, Siwen Yan, Sriraam Natarajan, Bridging Graph Neural Networks and Statistical Relational Learning: Relational One-Class GCN, 2021.

Devendra Singh Dhami, Siwen Yan, Sriraam Natarajan, A Statistical Relational Approach to Learning Distance-based GCNs, Statistical Relational AI (StarAI) Workshop at IJCLR 2021.

Devendra Singh Dhami, Siwen Yan, Gautam Kunapuli, Sriraam Natarajan, Non-Parametric Learning of Embeddings for Relational Data using Gaifman Locality Theorem, International Conference on Inductive Logic Programming (ILP) 2021.

Matej Zečević, Devendra Singh Dhami, Athresh Karanam, Sriraam Natarajan, Kristian Kersting, Interventional Sum-Product Networks: Causal Inference with Tractable Probabilistic Models, 35th Conference on Neural Information Processing Systems (NeurIPS 2021) 2021.

Harsha Kokel, Arjun Manoharan, Sriraam Natarajan, Balaraman Ravindran, Prasad Tadepalli, Dynamic probabilistic logic models for effective abstractions in RL, Statistical Relational AI (StarAI) Workshop at IJCLR 2021.

Harsha Kokel, Arjun Manoharan, Sriraam Natarajan, Balaraman Ravindran, Prasad Tadepalli, Deep RePReL-Combining Planning and Deep RL for acting in relational domains, Deep RL Workshop at NeurIPS 2021.

Devendra Singh Dhami, Mayukh Das, Sriraam Natarajan, Beyond Simple Images: Human Knowledge-Guided GANs for Clinical Data Generation, 8th International Conference on Principles of Knowledge Representation and Reasoning (KR) 2021.

Devendra Singh Dhami, Siwen Yan, Gautam Kunapuli, David Page, Sriraam Natarajan, Predicting Drug-Drug Interactions from Heterogeneous Data: An Embedding Approach, 19th International Conference in Artificial Intelligence in Medicine(AIME) 2021.

Athresh Karanam, Alexander L. Hayes, Harsha Kokel, David M. Haas, Predrag Radivojac, Sriraam Natarajan, A Probabilistic Approach to Extract Qualitative Knowledge for Early Prediction of Gestational Diabetes, 19th International Conference in Artificial Intelligence in Medicine(AIME) 2021.

Nandini Ramanan, Gautam Kunapuli, Tushar Khot, Bahare Fatemi, Seyed Mehran Kazemi, David Poole, Kristian Kersting, Sriraam Natarajan, Structure Learning for Relational Logistic Regression: An Ensemble Approach, Data Mining and Knowledge Discovery (DMKD) Journal 2021.

Ashutosh Kakadiya, Sriraam Natarajan, Balaraman Ravindran, Relational Boosted Bandits, Thirty Fifth AAAI Conference on Artificial Intelligence (AAAI) 2021.

Srijita Das, Rishabh Iyer, Sriraam Natarajan, A Clustering based Selection Framework for Cost Aware and Test-time Feature Elicitation, ACM India Joint International Conference on Data Science & Management of Data (CODS-COMAD) 2021.

Mayukh Das, Devendra Singh Dhami, Yang Yu, Gautam Kunapuli, Sriraam Natarajan, Human-Guided Learning of Column Networks: Knowledge Injection for Relational Deep Learning, ACM India Joint International Conference on Data Science & Management of Data (CODS-COMAD) 2021.

Harsha Kokel, Arjun Manoharan, Sriraam Natarajan, Balaraman Ravindran, Prasad Tadepalli, RePReL: Integrating Relational Planning and Reinforcement Learning for Effective Abstraction, Thirty First International Conference on Automated Planning and Scheduling (ICAPS) 2021.

Harsha Kokel, Mayukh Das, Rakibul Islam, Julia Bonn, Jon Cai, Soham Dan, Anjali Narayan-Chen, Prashant Jayannavar, Janardhan Rao Doppa, Julia Hockenmaier, Sriraam Natarajan, Martha Palmer, Dan Roth, Human-guided Collaborative Problem Solving: A Natural Language based Framework, Thirty First International Conference on Automated Planning and Scheduling (ICAPS) 2021.

Harsha Kokel, Arjun Manoharan, Sriraam Natarajan, Balaraman Ravindran, Prasad Tadepalli, RePReL: Integrating Relational Planning and Reinforcement Learning for Effective Abstraction (Extended Abstract), Planning and Reinforcement Learning (PRL) Workshop at ICAPS 2021.

2020

Siwen Yan, Devendra Singh Dhami, Sriraam Natarajan, The Curious Case of Stacking Boosted Relational Dependency Networks, Proceedings on "I Can't Believe It's Not Better!" at NeurIPS Workshops, PMLR 2020.

Devendra Singh Dhami, Siwen Yan, Gautam Kunapuli, David Page, Sriraam Natarajan, Beyond Textual Data: Predicting Drug-Drug Interactions from Molecular Structure Images using Siamese Neural Networks, 2020.

Srijita Das, Rishabh Iyer, Sriraam Natarajan, Cost Aware Feature Elicitation, International Workshop on Knowledge-infused Mining and Learning (KIML) organized in conjunction with KDD 2020.

Devendra Singh Dhami, Mayukh Das, Sriraam Natarajan, Knowledge Intensive Learning of Generative Adversarial Networks, International Workshop on Knowledge-infused Mining and Learning (KIML) organized in conjunction with KDD (Best student paper award) 2020.

Nandini Ramanan, Sriraam Natarajan, Causal Learning from Predictive Modeling for Observational Data, Machine Learning and Artificial Intelligence, Frontiers in Big Data 2020.

Mayukh Das, Nandini Ramanan, Janardhan Rao Doppa, Sriraam Natarajan, Few-Shot Induction of Generalized Logical Concepts via Human Guidance, Computational Intelligence in Robotics, Frontiers in Robotics and AI 2020.

Nandini Ramanan, Mayukh Das, Kristian Kersting, Sriraam Natarajan, Discriminative Non-Parametric Learning of Arithmetic Circuits, International Conference on Probabilistic Graphical Models (PGM) 2020.

Srijita Das, Sriraam Natarajan, Kaushik Roy, Ronald Parr, Kristian Kersting, Fitted Q-Learning for Relational Domains, International Conference on Principles of Knowledge Representation and Reasoning (KR) (accepted as poster paper) 2020.

Yuqiao Chen, Yibo Yang, Sriraam Natarajan, Nicholas Ruozzi, Lifted Hybrid Variational Inference, International Joint Conference on Artificial Intelligence (IJCAI) 2020.

Raksha Kumaraswamy, Nandini Ramanan, Phillip Odom, Sriraam Natarajan, Interactive Transfer Learning in Relational Domains, KUIN Springer Journal 2020.

Harsha Kokel, Phillip Odom, Shuo Yang, Sriraam Natarajan, A Unified Framework for Knowledge Intensive Gradient Boosting: Leveraging Human Experts for Noisy Sparse Domain, Thirty Fourth AAAI Conference on Artificial Intelligence (AAAI) 2020.

Navdeep Kaur, Gautam Kunapuli, Sriraam Natarajan, Non-Parametric Learning of Lifted Restricted Boltzmann Machines, International Journal of Approximate Reasoning 2020.

Yuqiao Chen, Yibo Yang, Sriraam Natarajan, Nicholas Ruozzi, Lifted Hybrid Variational Inference, Workshop on Statistical Relational AI (StarAI) 2020.

Michael A. Skinner, Lakshmi Raman, Neel Shah, Abdelaziz Farhat, Sriraam Natarajan, A preliminary approach for learning relationalpolicies for the management of critically ill children, Workshop on Statistical Relational AI (StarAI) 2020.

Alexander L. Hayes, srlearn: A Python Library for Gradient-Boosted Statistical Relational Models, Workshop on Statistical Relational AI (StarAI) 2020.

Navdeep Kaur, Gautam Kunapuli, Sriraam Natarajan, Non-Parametric Learning of Lifted Restricted Boltzmann Machines, Workshop on Statistical Relational AI (StarAI) 2020.

Devendra Singh Dhami, Siwen Yan, Gautam Kunapuli, Sriraam Natarajan, Non-Parametric Learning of Gaifman Models, Workshop on Statistical Relational AI (StarAI) 2020.

Mayukh Das, Nandini Ramanan, Janardhan Rao Doppa, Sriraam Natarajan, One-Shot Induction of Generalized Logical Concepts via Human Guidance, Workshop on Statistical Relational AI (StarAI) 2020.

2019

Robert Cruise, Erik Blasch, Sriraam Natarajan, Ali Raz, Cyber-Physical Command-Guided Swarm, Defense Systems Information Analysis Center(DCIAC) 2019.

Devendra Singh Dhami, Gautam Kunapuli, Sriraam Natarajan, Efficient Learning of Relational Gaifman Models using Probabilistic Logic, Workshop on Probabilistic Logic Programming (PLP) 2019.

Michael A. Skinner, Lakshmi Raman, Neel Shah, Abdelaziz Farhat, Sriraam Natarajan, 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.

Devendra Singh Dhami, Gautam Kunapuli, David Page, Sriraam Natarajan, Predicting Drug-Drug Interactions from Molecular Structure Images, AAAI Fall symposium - AI for Social Good 2019.

Navdeep Kaur, Gautam Kunapuli, Saket Joshi, Kristian Kersting, Sriraam Natarajan, Neural Network for Relational Data, 29th International Conference on Inductive Logic Programming (ILP) 2019.

Yuqiao Chen, Nicholas Ruozzi, Sriraam Natarajan, Lifted Message Passing for Hybrid Probabilistic Inference, International Joint Conference on Artificial Intelligence (IJCAI) 2019.

Mayukh Das, Devendra Singh Dhami, Yang Yu, Gautam Kunapuli, Sriraam Natarajan, Knowledge-augmented Column Networks: Guiding Deep Learning with Advice, Workshop on Human In the Loop Learning (HILL) 2019.

Nandini Ramanan, Mayukh Das, Kristian Kersting, Sriraam Natarajan, Discriminative Non-Parametric Learning of Arithmetic Circuits, The Third Workshop On Tractable Probabilistic Modeling (TPM) 2019.

Nandini Ramanan, Sriraam Natarajan, 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.

Mayukh Das, Phillip Odom, Md. Rakibul Islam, Jana Doppa, Dan Roth, Sriraam Natarajan, Planning with actively eliciting preferences, Knowledge-Based Systems 2019.

Mayukh Das, Devendra Singh Dhami, Gautam Kunapuli, Kristian Kersting, Sriraam Natarajan, Fast Relational Probabilistic Inference and Learning Approximate Counting via Hypergraphs, 33rd AAAI Conference on Artificial Intelligence (AAAI) 2019.

2018

Erik Blasch, Robert Cruise, Sriraam Natarajan, Ali Raz, Control Diffusion of Information Collection for Situation Understanding Using Boosting MLNs, International Conference on Information Fusion (FUSION) 2018.

Mayukh Das, Devendra Singh Dhami, Gautam Kunapuli, Kristian Kersting, Sriraam Natarajan, Approximate Counting for Fast Inference and Learning in Probabilistic Programming, Proceedings of the Inaugural International Conference on Probabilistic Programming (ProbProg) 2018.

Sriraam Natarajan, Phillip Odom, Tushar Khot, Kristian Kersting, Jude Shavlik, Human-in-the-loop Learning for Probabilistic Programming, Proceedings of the Inaugural International Conference on Probabilistic Programming (ProbProg) 2018.

Nandini Ramanan, Gautam Kunapuli, Tushar Khot, Bahere Fatemi, Seyed Mehran Kazemi, David Poole, Kristian Kersting, Sriraam Natarajan, Structure Learning for Relational Logistic Regression: An Ensemble Approach, 16th International Conference on Principles of Knowledge Representation and Reasoning (KR) 2018.

Devendra Singh Dhami, Gautam Kunapuli, Mayukh Das, David Page, Sriraam Natarajan, Drug-Drug Interaction Discovery: Kernel Learning from Heterogeneous Similarities, IEEE Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE) 2018.

Sriraam Natarajan, Srijita Das, Nandini Ramaman, Gautam Kunapuli, Predrag Radivojac, Whom Should I Perform the Lab Test on Next? An Active Feature Elicitation Approach, International Joint Conference on Artificial Intelligence (IJCAI) 2018.

Phillip Odom, Sriraam Natarajan, Human-Guided Learning for Probabilistic Logic Models, Frontiers in Robotics and AI (Front. Robot. AI) 2018.

Mayukh Das, Phillip Odom, Md. Rakibul Islam, Jana Doppa, Dan Roth, Sriraam Natarajan, Preference- Guided Planning: An Active Elicitation Approach, International Conference on Autonomous Agents and Multiagent Systems (AAMAS) 2018.

Alejandro Molina, Antonio Vergari, Nicola Di Mauro, Floriana Esposito, Sriraam Natarajan, Kristian Kersting, Mixed Sum-Product Networks: A Deep Architecture for Hybrid Domains, In Proceedings of the Conference on Artificial Intelligence (AAAI) 2018.

2017

Shuo Yang, Fabian Hadiji, Kristian Kersting, Shaun Grannis, Sriraam Natarajan, Modeling Heart Procedures from EHRs: An Application of Exponential Families, IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM) 2017.

Nandini Ramanan, Shuo Yang, Shaun Grannis, Sriraam Natarajan, Discriminative Boosted Bayes Networks for Learning Multiple Cardiovascular Procedures, IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM) 2017.

Alexander L. Hayes, Mayukh Das, Phillip Odom, Sriraam Natarajan, User Friendly Automatic Construction of Background Knowledge: Mode Construction from ER Diagrams, Knowledge Capture Conference 2017.

Sriraam Natarajan, Annu Prabhakar, Nandini Ramanan, Anna Bagilone, Katie Siek, Kay Connelly, Boosting for Post Partum Depression Prediction, IEEE Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE) 2017.

Navdeep Kaur, Gautam Kunapuli, Tushar Khot, Kristian Kersting, William Cohen, Sriraam Natarajan, Relational Restricted Boltzmann Machines: A Probabilistic Logic Learning Approach, International Conference on Inductive Logic Programming (ILP) 2017.

Shuo Yang, Mohammed Korayem, Khalifeh Aljadda, Trey Grainger, Sriraam Natarajan, Combining content-based and collaborative filtering for job recommendation system: A cost-sensitive Statistical Relational Learning approach, Knowledge-Based Systems 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, Sriraam Natarajan, Kristian Kersting, Poisson Sum-Product Networks: A Deep Architecture for Tractable Multivariate Poisson Distributions, Thirty First AAAI Conference on Artificial Intelligence (AAAI) 2017.

Devendra Singh Dhami, David Leake, Sriraam Natarajan, Knowledge-based Morphological Classification of Galaxies from Vision Features, Knowledge-Based Techniques for Problem Solving and Reasoning (AAAI) 2017.

Mayukh Das, Md. Rakibul Islam, Janardhan Rao Doppa, Dan Roth, Sriraam Natarajan, Active Preference Elicitation for Planning, Human-Machine Collaborative Learning (AAAI) 2017.

Anjali Narayan-Chen, Colin Graber, Mayukh Das, Rakibul Islam, Soham Dan, Sriraam Natarajan, Janardhan Rao Doppa, Julia Hockenmaier, Martha Palmer, Dan Roth, Towards Problem Solving Agents that Communicate and Learn, RoboNLP at Association for Computational Linguistic (ACL) 2017.

2016

Luc De Raedt, Kristian Kersting, Sriraam Natarajan, David Poole, Statistical Relational Artificial Intelligence Logic, Probability, and Computation, 2016.

Marcin Malec, Tushar Khot, James Nagy, Erik Blasch, Sriraam Natarajan, Inductive Logic Programming meets Relational Databases: An Application to Statistical Relational Learning, International Conference on Inductive Logic Programming (ILP) 2016.

Ameet Soni, Dileep Viswanathan, Jude Shavlik, Sriraam Natarajan, Learning Relational Dependency Networks for Relation Extraction, International Conference on Inductive Logic Programming (ILP) 2016.

Phillip Odom, Raksha Kumaraswamy, Kristian Kersting, Sriraam Natarajan, Learning through Advice-Seeking via Transfer, International Conference on Inductive Logic Programming (ILP) 2016.

Phillip Odom, Sriraam Natarajan, Actively Interacting with Experts: A Probabilistic Logic Approach, European Conference on Machine Learning and Principles of Knowledge Discovery in Databases (ECMLPKDD) 2016.

Haley MacLeod, Shuo Yang, Kim Oakes, Kay Connelly, Sriraam Natarajan, Identifying Rare Diseases from Behavioural Data: A Machine Learning Approach, IEEE Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE) 2016.

Phillip Odom, 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, 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, Sriraam Natarajan, Learning Continuous-Time Bayesian Networks in Relational Domains: A Non-Parametric Approach, Thirtieth AAAI Conference on Artificial Intelligence (AAAI) 2016.

2015

Sriraam Natarajan, Tushar Khot, Kristian Kersting, Jude Shavlik, Boosted Statistical Relational Learners: From Benchmarks to Data-Driven Medicine, 2015.

Raksha Kumaraswamy, Phillip Odom, Kristian Kersting, David Leake, Sriraam Natarajan, Transfer Learning via Relational Type Matching, International Conference on Data Mining (ICDM) 2015.

Mayukh Das, Yuqing Wu, Tushar Khot, Kristian Kersting, Sriraam Natarajan, Graph-based Approximate Counting for Relational Probabilistic Models, International Workshop on Statistical Relational AI (StarAI) 2015.

Raksha Kumaraswamy, Phillip Odom, Kristian Kersting, David Leake, Sriraam Natarajan, Transfer Learning Across Relational and Uncertain Domains: A Language-Bias Approach, International Workshop on Statistical Relational AI (StarAI) 2015.

Shuo Yang, Kristian Kersting, Greg Terry, Jeffrey Carr, 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, Sriraam Natarajan, Extracting Adverse Drug Events from Text using Human Advice, Artificial Intelligence in Medicine (AIME) 2015.

Phillip Odom, Tushar Khot, Reid Porter, Sriraam Natarajan, Knowledge-Based Probabilistic Logic Learning, Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI) 2015.

Jeremy Weiss, Sriraam Natarajan, David Page, Learning To Reject Sequential Importance Steps for Continuous-Time Bayesian Networks, Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI) 2015.

Phillip Odom, Tushar Khot, Sriraam Natarajan, Learning Probabilistic Logic Models with Human Advice, AAAI Spring Symposium on Knowledge Representation and Reasoning 2015.

Phillip Odom, Sriraam Natarajan, Active Advice Seeking for Inverse Reinforcement Learning, AAAI Student Abstract and Poster Program (AAAI) 2015.

Tushar Khot, Sriraam Natarajan, Kristian Kersting, Jude Shavlik, Gradient-based boosting for statistical relational learning: the Markov logic network and missing data cases, Machine Learning Journal (MLJ) 2015.

2014

Shrutika Poyrekar, Sriraam Natarajan, Kristian Kersting, A Deeper Empirical Analysis of CBP algorithm: Grounding is the Bottleneck, International Workshop on Statistical Relational AI (StarAI) 2014.

Shuo Yang, Tushar Khot, Kristian Kersting, Gautam Kunapuli, Kris Hauser, Sriraam Natarajan, Learning from Imbalanced Data in Relational Domains: A Soft Margin Approach, International Conference on Data Mining (ICDM) 2014.

Tushar Khot, Sriraam Natarajan, 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, Jude Shavlik, Effectively creating weakly labeled training examples via approximate domain knowledge, International Conference in Inductive Logic Programming (ILP) 2014.

David Poole, David Buchman, Seyed Mehran Kazemi, Kristian Kersting, Sriraam Natarajan, Population Size Extrapolation in Relational Probabilistic Modelling, Scalable Uncertainty Management (SUM) 2014.

Alan Fern, Sriraam Natarajan, Kshitij Judah, Prasad Tadepalli, A Decision-Theoretic Model of Assistance, Journal Of Artificial Intelligence Research (JAIR) 2014.

Seyed Mehran Kazemi, David Buchman, Kristian Kersting, Sriraam Natarajan, David Poole, Relational Logistic Regression, International Conference on Principles of Knowledge Representation and Reasoning (KR) 2014.

Chris Magnano, Ameet Soni, Sriraam Natarajan, Gautam Kunapuli, A graphical model approach to ATLAS-free mining of MRI images, SIAM International Conference on Data Mining (SDM) 2014.

2013

Babak Ahmadi, Kristian Kersting, Martin Mladenov, Sriraam Natarajan, Exploring Symmetries for Scaling Loopy Belief Propagation and Relational Training, Machine Learning Journal (MLJ) 2013.

Sriraam Natarajan, Baidya N. Saha, Saket Joshi, Adam Edwards, Elizabeth Moody, Tushar Khot, Kristian Kersting, Christopher T. Whitlow, 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.

Gautam Kunapuli, Phillip Odom, Jude Shavlik, 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, Jeffrey Carr, Early Prediction of Coronary Artery Calcification Levels Using Machine Learning, AAAI conference on Innovative Applications in AI (IAAI) 2013.

Shuo Yang, 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, 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, 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, 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, David Page, Learning When to Reject an Importance Sample, Late-Breaking Paper (AAAI) 2013.

2012

Jeremy Weiss, Sriraam Natarajan, Peggy Peissig, Catherine McCarty, David Page, Statistical Relational Learning to Predict Primary Myocardial Infarction from Electronic Health Records, AI Magazine 2012.

Jeremy Weiss, Sriraam Natarajan, David Page, Multiplicative Forests for Continuous-Time Processes, Neural Information Processing Systems (NIPS) 2012.

Babak Ahmadi, Kristian Kersting, Sriraam Natarajan, Lifted Online Training of Relational Models with Stochastic Gradient Methods, European Conference on Machine Learning, (ECMLPKDD) 2012.

Sriraam Natarajan, Kristian Kersting, Saket Joshi, Santiago Saldana, Edward Ip, David Jacobs, Jeffrey Carr, Early Prediction of Coronary Artery Calcification Levels Using Statistical Relational Learning, ICML Workshop on Machine Learning for Clinical Data Analysis 2012.

David Page, Sriraam Natarajan, Vitor Santos Costa, Peggy Peissig, Aubrey Barnard, Michael Caldwell, Identifying Adverse Drug Events from Multi-Relational Healthcare Data, Twenty-Sixth Conference on Artificial Intelligence (AAAI) 2012.

Baidya N. Saha, Sriraam Natarajan, Gopi Kota, Christopher T. Whitlow, Donald W. Bowden, Jasmin Divers, Barry I. Freedman, Joseph A. Maldjian, A Novel Hierarchical Level Set with AR-Boost for White Matter Lesion Segmentation in Diabetes, International Conference on Machine Learning and Applications (ICMLA) 2012.

Sriraam Natarajan, Saket Joshi, Baidya N. Saha, Adam Edwards, Elizabeth Moody, Tushar Khot, Kristian Kersting, Christopher T. Whitlow, Joseph A. Maldjian, 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.

Baidya N. Saha, Christopher T. Whitlow, Gopi Kota, Elizabeth Moody, Sriraam Natarajan, Donald W. Bowden, Jasmin Divers, Barry I. Freedman, Joseph A. Maldjian, Hierarchical Level Sets with Boosting for White Matter Lesion Segmentation in Diabetes, Radiological Society of North America Annual Meeting 2012.

Joseph A. Maldjian, Christopher T. Whitlow, Baidya N. Saha, Gopi Kota, Elizabeth Moody, Donald W. Bowden, Jasmin Divers, Barry I. Freedman, Evaluation of Automated White Matter Lesion Segmentation in Diabetes, Radiological Society of North America Annual Meeting 2012.

Babak Ahmadi, Kristian Kersting, Sriraam Natarajan, Lifted Parameter Learning in Relational Models, ICML Workshop on Statistical Relational Learning (SRL) 2012.

Tushar Khot, Sriraam Natarajan, Kristian Kersting, Jude Shavlik, Structure Learning with Hidden Data in Relational Domains, ICML Workshop on Statistical Relational Learning (SRL) 2012.

Richard G. Freedman, Rodrigo de Salvo Braz, Hung Bui, Sriraam Natarajan, Initial Empirical Evaluation of Anytime Lifted Belief Propagation, International Workshop on Statistical Relational AI 2012.

Tushar Khot, Siddharth Srivastava, Sriraam Natarajan, 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, Sriraam Natarajan, Integrating Human Instructions and Reinforcement Learners: An SRL Approach, International Workshop on Statistical Relational AI 2012.

David Poole, David Buchman, Sriraam Natarajan, Kristian Kersting, Aggregation and Population Growth: The Relational Logistic Regression and Markov Logic Cases, International Workshop on Statistical Relational AI 2012.

Sriraam Natarajan, Tushar Khot, Kristian Kersting, Bernd Gutmann, Jude Shavlik, Gradient-based Boosting for Statistical Relational Learning: The Relational Dependency Network Case, Invited contribution to special issue of Machine Learning Journal (MLJ) 2012.

2011

Sriraam Natarajan, Prasad Tadepalli, Alan Fern, A Relational Hierarchical Model of Decision-Theoretic Assistance, Knowledge and Information Systems (KAIS) 2011.

Sriraam Natarajan, Saket Joshi, Prasad Tadepalli, Kristian Kersting, 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, Jude Shavlik, Learning Markov Logic Networks via Functional Gradient Boosting, International Conference in Data Mining (ICDM) 2011.

Sabareesh Subramaniam, Sriraam Natarajan, 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, Saket Joshi, Prasad Tadepalli, Kristian Kersting, Jude Shavlik, Imitation Learning in Relational Domains Using Functional Gradient Boosting, The Learning Workshop 2011.

2010

Sriraam Natarajan, David Page, Machine Learning for High-Throughput Biomedical Data: Lessons Learned, Machine Learning Encyclopedia 2010.

Sriraam Natarajan, Tushar Khot, Daniel Lowd, Kristian Kersting, Prasad Tadepalli, 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, Jude Shavlik, Boosting Relational Dependency Networks, International Conference on Inductive Logic Programming (ILP) 2010.

Sriraam Natarajan, Gautam Kunapuli, Kshitij Judah, Prasad Tadepalli, Kristian Kersting, Jude Shavlik, Multi Agent Inverse Reinforcement Learning, IEEE Conference on Machine Learning and Applications (ICMLA) 2010.

Trevor Walker, Gautam Kunapuli, Sriraam Natarajan, Jude Shavlik, 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.

Sriraam Natarajan, Gautam Kunapuli, Kshitij Judah, Prasad Tadepalli, Kristian Kersting, Jude Shavlik, Multi-Agent Inverse Reinforcement Learning, The Learning Worshop 2010.

Sriraam Natarajan, Gautam Kunapuli, Richard Maclin, David Page, Ciaran O'Reilly, Trevor Walker, Jude Shavlik, Learning from Human Teachers: Issues and Challenges for ILP in Bootstrap Learning, AAMAS Workshop on Agents Learning Interactively from Human Teachers 2010.

2009

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, Jude Shavlik, ILP for Bootstrapped Learning: A Layered Approach to Automating the ILP Setup Problem, International Conference on Inductive Logic Programming 2009.

Sriraam Natarajan, Prasad Tadepalli, Gautam Kunapuli, 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, Stuart Russell, Anytime Lifted Belief Propagation, International Workshop in SRL 2009.

2008

Sriraam Natarajan, Prasad Tadepalli, Thomas G. Dietterich, Alan Fern, Learning First-Order Probabilistic Models with Combining Rules, Annals of Mathematics and AI, Special Issue on Probabilistic Relational Learning 2008.

Neville Mehta, Sriraam Natarajan, Prasad Tadepalli, Alan Fern, Transfer in Variable Reward Hierarchical Reinforcement Learning, Invited contribution to Inductive transfer in Machine Learning 2008.

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.

2007

Sriraam Natarajan, Prasad Tadepalli, Alan Fern, A Relational Hierarchical Model of Decision-Theoretic Assistance, Proceedings of the International Conference on Inductive Logic Programming 2007.

Alan Fern, Sriraam Natarajan, Kshitij Judah, Prasad Tadepalli, A Decision theoretic model of Assistance, International Joint Conference in Artificial Intelligence (IJCAI) 2007.

Sriraam Natarajan, Kshitij Judah, Prasad Tadepalli, Alan Fern, A Decision-Theoretic Model of Assistance - Evaluation, Extensions and Open Problems, AAAI Spring Symposium on Interaction Challenges for Intelligent Assistants 2007.

Sriraam Natarajan, Prasad Tadepalli, Alan Fern, 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

Alan Fern, Sriraam Natarajan, Kshitij Judah, Prasad Tadepalli, A Decision theoretic model of Assistance, Modeling Others from Observations workshop in AAAI 2006.

Sriraam Natarajan, Weng-Keen Wong, Prasad Tadepalli, Structure Refinement in First Order Conditional Influence Language, Open Problems in Statistical Relational Learning, ICML 2006.

2005

Sriraam Natarajan, Prasad Tadepalli, Eric Altendorf, Thomas G. Dietterich, Alan Fern, Angelo Restificar, Learning First-Order Probabilistic Models with Combining Rules, 22nd International Conference on Machine Learning (ICML) 2005.

Sriraam Natarajan, Prasad Tadepalli, Dynamic Preferences in Multi-Criteria Reinforcement Learning, 22nd International Conference on Machine Learning (ICML) 2005.