Sriraam Natarajan

217 papers A* 34A 11B 22C 8Misc 1Journal 77Unranked 57

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Context-specific Credibility-aware Multimodal Fusion with Conditional Probabilistic Circuits. CoRR Pranuthi Tenali, Sahil Sidheekh, Saurabh Mathur, Erik Blasch, Kristian Kersting, Sriraam Natarajan
2026	J	`jnl`	EHR Sampling Interval Bias Detection and Burden of Blood Pressure Excursions: Implications for Clinical Decision Support and Model Validity in Pediatric ECMO. Inf. Neel Shah, Ethan Sanford, David R. Busch, Ranveer Singh, Saurabh Mathur, Jayesh Sharma, Philip Reeder, Sriraam Natarajan, Lakshmi Raman
2026	J	`jnl`	Geometry-Aware Probabilistic Circuits via Voronoi Tessellations. CoRR Sahil Sidheekh, Sriraam Natarajan
2026	J	`jnl`	Recursive Inference Machines for Neural Reasoning. CoRR Mieszko Komisarczyk, Saurabh Mathur, Maurice Kraus, Sriraam Natarajan, Kristian Kersting
2026	A*	`conf`	Tractable Sharpness-Aware Learning of Probabilistic Circuits. AAAI Hrithik Suresh, Sahil Sidheekh, Vishnu Shreeram M. P, Sriraam Natarajan, Narayanan Chatapuram Krishnan
2025	A*	`conf`	A Unified Framework for Human-Allied Learning of Probabilistic Circuits. AAAI Athresh Karanam, Saurabh Mathur, Sahil Sidheekh, Sriraam Natarajan
2025	A	`conf`	Combining Planning and Reinforcement Learning for Solving Relational Multiagent Domains. AAMAS Nikhilesh Prabhakar, Ranveer Singh, Harsha Kokel, Sriraam Natarajan, Prasad Tadepalli
2025	J	`jnl`	Combining Planning and Reinforcement Learning for Solving Relational Multiagent Domains. CoRR Nikhilesh Prabhakar, Ranveer Singh, Harsha Kokel, Sriraam Natarajan, Prasad Tadepalli
2025	A	`conf`	Credibility-Aware Multimodal Fusion Using Probabilistic Circuits. AISTATS Sahil Sidheekh, Pranuthi Tenali, Saurabh Mathur, Erik Blasch, Kristian Kersting, Sriraam Natarajan
2025	J	`jnl`	Human-Allied Relational Reinforcement Learning. CoRR Fateme Golivand Darvishvand, Hikaru Shindo, Sahil Sidheekh, Kristian Kersting, Sriraam Natarajan
2025	A*	`conf`	Human-in-the-loop or AI-in-the-loop? Automate or Collaborate? AAAI Sriraam Natarajan, Saurabh Mathur, Sahil Sidheekh, Wolfgang Stammer, Kristian Kersting
2025	J	`jnl`	IndiCASA: A Dataset and Bias Evaluation Framework in LLMs Using Contrastive Embedding Similarity in the Indian Context. CoRR Santhosh G. S, Akshay Govind S, Gokul S. Krishnan, Balaraman Ravindran, Sriraam Natarajan
2025	—	`conf`	LLM-Guided Causal Bayesian Network Construction for Pediatric Patients on ECMO. AIME (2) Saurabh Mathur, Ranveer Singh, Michael A. Skinner, Ethan Sanford, Neel Shah, Phillip Reeder, Lakshmi Raman, Sriraam Natarajan
2025	—	`conf`	LLMs for Causal Reasoning in Medicine? A Call for Caution. CODS Saurabh Mathur, Ranveer Singh, Michael A. Skinner, Predrag Radivojac, David M. Haas, Lakshmi Raman, Sriraam Natarajan
2025	—	`conf`	Representing Multi-Relational Data via Statistical Relational Learning and Graph Convolutional Networks. CODS Devendra Singh Dhami, Saurabh Mathur, Siwen Yan, Sriraam Natarajan
2025	—	`conf`	Scalable Knowledge Graph Construction from Unstructured Text: A Case Study on Artisanal and Small-Scale Gold Mining. PAKDD (2) Debashis Gupta, Aditi Golder, Sahil Sidheekh, Sakib Imtiaz, Sarra Alaqahtani, Fan Yang, Gregory D. Larsen, Miles R. Silman, Luis Fernendez, Robert J. Plemmons, Sriraam Natarajan, V. Paul Pauca
2025	J	`jnl`	Tractable Representation Learning with Probabilistic Circuits. CoRR Steven Braun, Sahil Sidheekh, Antonio Vergari, Martin Mundt, Sriraam Natarajan, Kristian Kersting
2025	J	`jnl`	Tractable Representation Learning with Probabilistic Circuits. Trans. Mach. Learn. Res. Steven Braun, Sahil Sidheekh, Antonio Vergari, Martin Mundt, Sriraam Natarajan, Kristian Kersting
2025	J	`jnl`	Tractable Sharpness-Aware Learning of Probabilistic Circuits. CoRR Hrithik Suresh, Sahil Sidheekh, Vishnu Shreeram M. P, Sriraam Natarajan, Narayanan C. Krishnan
2025	J	`jnl`	mFARM: Towards Multi-Faceted Fairness Assessment based on HARMs in Clinical Decision Support. CoRR Shreyash Adappanavar, Krithi Shailya, Gokul S. Krishnan, Sriraam Natarajan, Balaraman Ravindran
2024	J	`jnl`	A Unified Framework for Human-Allied Learning of Probabilistic Circuits. CoRR Athresh Karanam, Saurabh Mathur, Sahil Sidheekh, Sriraam Natarajan
2024	J	`jnl`	AI-assisted research collaboration with open data for fair and effective response to call for proposals. AI Mag. Siva Likitha Valluru, Michael Widener, Biplav Srivastava, Sriraam Natarajan, Sugata Gangopadhyay
2024	A*	`conf`	Building Expressive and Tractable Probabilistic Generative Models: A Review. IJCAI Sahil Sidheekh, Sriraam Natarajan
2024	J	`jnl`	Building Expressive and Tractable Probabilistic Generative Models: A Review. CoRR Sahil Sidheekh, Sriraam Natarajan
2024	J	`jnl`	Credibility-Aware Multi-Modal Fusion Using Probabilistic Circuits. CoRR Sahil Sidheekh, Pranuthi Tenali, Saurabh Mathur, Erik Blasch, Kristian Kersting, Sriraam Natarajan
2024	—	`conf`	Deep Tractable Probabilistic Models. COMAD/CODS Sahil Sidheekh, Saurabh Mathur, Athresh Karanam, Sriraam Natarajan
2024	J	`jnl`	Explainable models via compression of tree ensembles. Mach. Learn. Siwen Yan, Sriraam Natarajan, Saket Joshi, Roni Khardon, Prasad Tadepalli
2024	J	`jnl`	Human-in-the-loop or AI-in-the-loop? Automate or Collaborate? CoRR Sriraam Natarajan, Saurabh Mathur, Sahil Sidheekh, Wolfgang Stammer, Kristian Kersting
2024	A	`conf`	Knowledge Intensive Learning of Credal Networks. UAI Saurabh Mathur, Alessandro Antonucci, Sriraam Natarajan
2024	—	`conf`	Modeling Multiple Adverse Pregnancy Outcomes: Learning from Diverse Data Sources. AIME (1) Saurabh Mathur, Veerendra P. Gadekar, Rashika Ramola, Peixin Wang, Ramachandran Thiruvengadam, David M. Haas, Shinjini Bhatnagar, Nitya Wadhwa, Garbhini Study Group, Predrag Radivojac, Himanshu Sinha, Kristian Kersting, Sriraam Natarajan
2024	C	`conf`	On the Robustness and Reliability of Late Multi-Modal Fusion using Probabilistic Circuits. FUSION Sahil Sidheekh, Pranuthi Tenali, Saurabh Mathur, Erik Blasch, Sriraam Natarajan
2024	—	`ed.`	Proceedings of the 7th Joint International Conference on Data Science & Management of Data (11th ACM IKDD CODS and 29th COMAD), Bangalore, India, January 4-7, 2024 COMAD/CODS Sriraam Natarajan, Indrajit Bhattacharya, Richa Singh, Arun Kumar, Sayan Ranu, Kalika Bali, Abinaya K.
2024	A*	`conf`	Promoting Research Collaboration with Open Data Driven Team Recommendation in Response to Call for Proposals. AAAI Siva Likitha Valluru, Biplav Srivastava, Sai Teja Paladi, Siwen Yan, Sriraam Natarajan
2024	A*	`ed.`	Thirty-Eighth AAAI Conference on Artificial Intelligence, AAAI 2024, Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence, IAAI 2024, Fourteenth Symposium on Educational Advances in Artificial Intelligence, EAAI 2014, February 20-27, 2024, Vancouver, Canada AAAI Michael J. Wooldridge, Jennifer G. Dy, Sriraam Natarajan
2024	C	`conf`	Utilizing Threat Partitioning for More Practical Network Anomaly Detection. SACMAT Brian Ricks, Patrick Tague, Bhavani Thuraisingham, Sriraam Natarajan
2023	—	`conf`	Active Feature Acquisition via Human Interaction in Relational domains. COMAD/CODS Nandini Ramanan, Phillip Odom, Kristian Kersting, Sriraam Natarajan
2023	J	`jnl`	Active feature elicitation: An unified framework. Frontiers Artif. Intell. Srijita Das, Nandini Ramanan, Gautam Kunapuli, Predrag Radivojac, Sriraam Natarajan
2023	A	`conf`	Knowledge Intensive Learning of Cutset Networks. UAI Saurabh Mathur, Vibhav Gogate, Sriraam Natarajan
2023	J	`jnl`	Knowledge-based Refinement of Scientific Publication Knowledge Graphs. CoRR Siwen Yan, Phillip Odom, Sriraam Natarajan
2023	J	`jnl`	Learning with privileged and sensitive information: a gradient-boosting approach. Frontiers Artif. Intell. Siwen Yan, Phillip Odom, Rahul Pasunuri, Kristian Kersting, Sriraam Natarajan
2023	J	`jnl`	MACOptions: Multi-Agent Learning with Centralized Controller and Options Framework. CoRR Alakh Aggarwal, Rishita Bansal, Parth Padalkar, Sriraam Natarajan
2023	—	`conf`	Never Ending Reasoning and Learning: Opportunities and Challenges. AAAI Bridge Program Sriraam Natarajan, Kristian Kersting
2023	A	`conf`	Probabilistic Flow Circuits: Towards Unified Deep Models for Tractable Probabilistic Inference. UAI Sahil Sidheekh, Kristian Kersting, Sriraam Natarajan
2023	J	`jnl`	Promoting Research Collaboration with Open Data Driven Team Recommendation in Response to Call for Proposals. CoRR Siva Likitha Valluru, Biplav Srivastava, Sai Teja Paladi, Siwen Yan, Sriraam Natarajan
2023	J	`jnl`	RePReL: a unified framework for integrating relational planning and reinforcement learning for effective abstraction in discrete and continuous domains. Neural Comput. Appl. Harsha Kokel, Sriraam Natarajan, Balaraman Ravindran, Prasad Tadepalli
2022	B	`conf`	An Anytime Querying Algorithm for Predicting Cardiac Arrest in Children: Work-in-Progress. AIME Michael A. Skinner, Priscilla Yu, Lakshmi Raman, Sriraam Natarajan
2022	J	`jnl`	Explainable Models via Compression of Tree Ensembles. CoRR Siwen Yan, Sriraam Natarajan, Saket Joshi, Roni Khardon, Prasad Tadepalli
2022	—	`conf`	Explaining Deep Tractable Probabilistic Models: The sum-product network case. PGM Athresh Karanam, Saurabh Mathur, Predrag Radivojac, David M. Haas, Kristian Kersting, Sriraam Natarajan
2022	J	`jnl`	Human-guided Collaborative Problem Solving: A Natural Language based Framework. CoRR Harsha Kokel, Mayukh Das, Md. Rakibul Islam, Julia Bonn, Jon Z. Cai, Soham Dan, Anjali Narayan-Chen, Prashant Jayannavar, Janardhan Rao Doppa, Julia Hockenmaier, Sriraam Natarajan, Martha Palmer, Dan Roth
2022	C	`conf`	Hybrid Deep RePReL: Integrating Relational Planning and Reinforcement Learning for Information Fusion. FUSION Harsha Kokel, Nikhilesh Prabhakar, Balaraman Ravindran, Erik Blasch, Prasad Tadepalli, Sriraam Natarajan
2022	J	`jnl`	Knowing what it knows and asking the right questions?: an effective approach to human-allied AI: keynote. J. Comput. Sci. Coll. Sriraam Natarajan
2022	—	`conf`	Relational Active Feature Elicitation for DDDAS. DDDAS Nandini Ramanan, Phillip Odom, Erik Blasch, Kristian Kersting, Sriraam Natarajan
2022	A	`conf`	Relational Neural Markov Random Fields. AISTATS Yuqiao Chen, Sriraam Natarajan, Nicholas Ruozzi
2021	—	`conf`	A Clustering based Selection Framework for Cost Aware and Test-time Feature Elicitation. COMAD/CODS Srijita Das, Rishabh K. Iyer, Sriraam Natarajan
2021	B	`conf`	A Probabilistic Approach to Extract Qualitative Knowledge for Early Prediction of Gestational Diabetes. AIME Athresh Karanam, Alexander L. Hayes, Harsha Kokel, David M. Haas, Predrag Radivojac, Sriraam Natarajan
2021	A*	`conf`	Beyond Simple Images: Human Knowledge-Guided GANs for Clinical Data Generation. KR Devendra Singh Dhami, Mayukh Das, Sriraam Natarajan
2021	J	`jnl`	Bridging Graph Neural Networks and Statistical Relational Learning: Relational One-Class GCN. CoRR Devendra Singh Dhami, Siwen Yan, Sriraam Natarajan
2021	J	`jnl`	Dynamic probabilistic logic models for effective abstractions in RL. CoRR Harsha Kokel, Arjun Manoharan, Sriraam Natarajan, Balaraman Ravindran, Prasad Tadepalli
2021	J	`jnl`	Explaining Deep Tractable Probabilistic Models: The sum-product network case. CoRR Athresh Karanam, Saurabh Mathur, Predrag Radivojac, Kristian Kersting, Sriraam Natarajan
2021	—	`conf`	Human-Guided Learning of Column Networks: Knowledge Injection for Relational Deep Learning. COMAD/CODS Mayukh Das, Devendra Singh Dhami, Yang Yu, Gautam Kunapuli, Sriraam Natarajan
2021	A*	`conf`	Interventional Sum-Product Networks: Causal Inference with Tractable Probabilistic Models. NeurIPS Matej Zecevic, Devendra Singh Dhami, Athresh Karanam, Sriraam Natarajan, Kristian Kersting
2021	J	`jnl`	Interventional Sum-Product Networks: Causal Inference with Tractable Probabilistic Models. CoRR Matej Zecevic, Devendra Singh Dhami, Athresh Karanam, Sriraam Natarajan, Kristian Kersting
2021	B	`conf`	Non-parametric Learning of Embeddings for Relational Data Using Gaifman Locality Theorem. ILP Devendra Singh Dhami, Siwen Yan, Gautam Kunapuli, Sriraam Natarajan
2021	B	`conf`	Predicting Drug-Drug Interactions from Heterogeneous Data: An Embedding Approach. AIME Devendra Singh Dhami, Siwen Yan, Gautam Kunapuli, David Page, Sriraam Natarajan
2021	J	`jnl`	Predicting Drug-Drug Interactions from Heterogeneous Data: An Embedding Approach. CoRR Devendra Singh Dhami, Siwen Yan, Gautam Kunapuli, David Page, Sriraam Natarajan
2021	A*	`conf`	RePReL: Integrating Relational Planning and Reinforcement Learning for Effective Abstraction. ICAPS Harsha Kokel, Arjun Manoharan, Sriraam Natarajan, Balaraman Ravindran, Prasad Tadepalli
2021	A*	`conf`	Relational Boosted Bandits. AAAI Ashutosh Kakadiya, Sriraam Natarajan, Balaraman Ravindran
2021	J	`jnl`	Relational Neural Markov Random Fields. CoRR Yuqiao Chen, Sriraam Natarajan, Nicholas Ruozzi
2021	J	`jnl`	Structure learning for relational logistic regression: an ensemble approach. Data Min. Knowl. Discov. Nandini Ramanan, Gautam Kunapuli, Tushar Khot, Bahare Fatemi, Seyed Mehran Kazemi, David Poole, Kristian Kersting, Sriraam Natarajan
2020	J	`jnl`	A Preliminary Approach for Learning Relational Policies for the Management of Critically Ill Children. CoRR Michael A. Skinner, Lakshmi Raman, Neel Shah, Abdelaziz Farhat, Sriraam Natarajan
2020	A*	`conf`	A Unified Framework for Knowledge Intensive Gradient Boosting: Leveraging Human Experts for Noisy Sparse Domains. AAAI Harsha Kokel, Phillip Odom, Shuo Yang, Sriraam Natarajan
2020	J	`jnl`	Causal Learning From Predictive Modeling for Observational Data. Frontiers Big Data Nandini Ramanan, Sriraam Natarajan
2020	—	`conf`	Cost Aware Feature Elicitation. KiML@KDD Srijita Das, Rishabh K. Iyer, Sriraam Natarajan
2020	—	`conf`	Discriminative Non-Parametric Learning of Arithmetic Circuits. PGM Nandini Ramanan, Mayukh Das, Kristian Kersting, Sriraam Natarajan
2020	J	`jnl`	Few-Shot Induction of Generalized Logical Concepts via Human Guidance. Frontiers Robotics AI Mayukh Das, Nandini Ramanan, Janardhan Rao Doppa, Sriraam Natarajan
2020	J	`jnl`	Fitted Q-Learning for Relational Domains. CoRR Srijita Das, Sriraam Natarajan, Kaushik Roy, Ronald Parr, Kristian Kersting
2020	J	`jnl`	Interactive Transfer Learning in Relational Domains. Künstliche Intell. Raksha Kumaraswamy, Nandini Ramanan, Phillip Odom, Sriraam Natarajan
2020	J	`jnl`	Knowledge Graph Alignment using String Edit Distance. CoRR Navdeep Kaur, Gautam Kunapuli, Sriraam Natarajan
2020	—	`conf`	Knowledge Intensive Learning of Generative Adversarial Networks. KiML@KDD Devendra Singh Dhami, Mayukh Das, Sriraam Natarajan
2020	A*	`conf`	Lifted Hybrid Variational Inference. IJCAI Yuqiao Chen, Yibo Yang, Sriraam Natarajan, Nicholas Ruozzi
2020	J	`jnl`	Lifted Hybrid Variational Inference. CoRR Yuqiao Chen, Yibo Yang, Sriraam Natarajan, Nicholas Ruozzi
2020	J	`jnl`	Non-Parametric Learning of Gaifman Models. CoRR Devendra Singh Dhami, Siwen Yan, Gautam Kunapuli, Sriraam Natarajan
2020	J	`jnl`	Non-Parametric Learning of Lifted Restricted Boltzmann Machines. CoRR Navdeep Kaur, Gautam Kunapuli, Sriraam Natarajan
2020	J	`jnl`	Non-parametric learning of lifted Restricted Boltzmann Machines. Int. J. Approx. Reason. Navdeep Kaur, Gautam Kunapuli, Sriraam Natarajan
2020	J	`jnl`	Relational Boosted Bandits. CoRR Ashutosh Kakadiya, Sriraam Natarajan, Balaraman Ravindran
2020	J	`jnl`	The Best of SIAM Data Mining 2020. Big Data Yan Liu, Sriraam Natarajan
2020	—	`conf`	The Curious Case of Stacking Boosted Relational Dependency Networks. ICBINB@NeurIPS Siwen Yan, Devendra Singh Dhami, Sriraam Natarajan
2019	J	`jnl`	Editorial: Statistical Relational Artificial Intelligence. Frontiers Robotics AI Fabrizio Riguzzi, Kristian Kersting, Marco Lippi, Sriraam Natarajan
2019	A*	`conf`	Fast Relational Probabilistic Inference and Learning: Approximate Counting via Hypergraphs. AAAI Mayukh Das, Devendra Singh Dhami, Gautam Kunapuli, Kristian Kersting, Sriraam Natarajan
2019	J	`jnl`	Human-Guided Learning of Column Networks: Augmenting Deep Learning with Advice. CoRR Mayukh Das, Yang Yu, Devendra Singh Dhami, Gautam Kunapuli, Sriraam Natarajan
2019	J	`jnl`	Knowledge-augmented Column Networks: Guiding Deep Learning with Advice. CoRR Mayukh Das, Devendra Singh Dhami, Yang Yu, Gautam Kunapuli, Sriraam Natarajan
2019	A*	`conf`	Lifted Message Passing for Hybrid Probabilistic Inference. IJCAI Yuqiao Chen, Nicholas Ruozzi, Sriraam Natarajan
2019	B	`conf`	Neural Networks for Relational Data. ILP Navdeep Kaur, Gautam Kunapuli, Saket Joshi, Kristian Kersting, Sriraam Natarajan
2019	J	`jnl`	Neural Networks for Relational Data. CoRR Navdeep Kaur, Gautam Kunapuli, Saket Joshi, Kristian Kersting, Sriraam Natarajan
2019	J	`jnl`	One-Shot Induction of Generalized Logical Concepts via Human Guidance. CoRR Mayukh Das, Nandini Ramanan, Janardhan Rao Doppa, Sriraam Natarajan
2019	J	`jnl`	Planning with actively eliciting preferences. Knowl. Based Syst. Mayukh Das, Phillip Odom, Md. Rakibul Islam, Janardhan Rao Doppa, Dan Roth, Sriraam Natarajan
2019	J	`jnl`	Predicting Drug-Drug Interactions from Molecular Structure Images. CoRR Devendra Singh Dhami, Gautam Kunapuli, David Page, Sriraam Natarajan
2019	J	`jnl`	User Friendly Automatic Construction of Background Knowledge: Mode Construction from ER Diagrams. CoRR Alexander L. Hayes, Mayukh Das, Phillip Odom, Sriraam Natarajan
2018	C	`conf`	Control Diffusion of Information Collection for Situation Understanding Using Boosting MLNs. FUSION Erik Blasch, Robert Cruise, Sriraam Natarajan, Ali K. Raz, Tim Kelly
2018	J	`jnl`	Human-Guided Learning for Probabilistic Logic Models. Frontiers Robotics AI Phillip Odom, Sriraam Natarajan
2018	A*	`conf`	Mixed Sum-Product Networks: A Deep Architecture for Hybrid Domains. AAAI Alejandro Molina, Antonio Vergari, Nicola Di Mauro, Sriraam Natarajan, Floriana Esposito, Kristian Kersting
2018	A*	`conf`	On Whom Should I Perform this Lab Test Next? An Active Feature Elicitation Approach. IJCAI Sriraam Natarajan, Srijita Das, Nandini Ramanan, Gautam Kunapuli, Predrag Radivojac
2018	A	`conf`	Preference-Guided Planning: An Active Elicitation Approach. AAMAS Mayukh Das, Phillip Odom, Md. Rakibul Islam, Janardhan Rao Doppa, Dan Roth, Sriraam Natarajan
2018	J	`jnl`	Preference-Guided Planning: An Active Elicitation Approach. CoRR Mayukh Das, Phillip Odom, Md. Rakibul Islam, Janardhan Rao Doppa, Dan Roth, Sriraam Natarajan
2018	A*	`conf`	Structure Learning for Relational Logistic Regression: An Ensemble Approach. KR Nandini Ramanan, Gautam Kunapuli, Tushar Khot, Bahare Fatemi, Seyed Mehran Kazemi, David Poole, Kristian Kersting, Sriraam Natarajan
2018	J	`jnl`	Structure Learning for Relational Logistic Regression: An Ensemble Approach. CoRR Nandini Ramanan, Gautam Kunapuli, Tushar Khot, Bahare Fatemi, Seyed Mehran Kazemi, David Poole, Kristian Kersting, Sriraam Natarajan
2017	—	`conf`	Active Preference Elicitation for Planning. AAAI Workshops Mayukh Das, Md. Rakibul Islam, Janardhan Rao Doppa, Dan Roth, Sriraam Natarajan
2017	—	`ch.`	Biomedical Informatics. Encyclopedia of Machine Learning and Data Mining C. David Page, Sriraam Natarajan
2017	B	`conf`	Boosting for Postpartum Depression Prediction. CHASE Sriraam Natarajan, Annu Prabhakar, Nandini Ramanan, Anna N. Baglione, Katie A. Siek, Kay Connelly
2017	J	`jnl`	Combining content-based and collaborative filtering for job recommendation system: A cost-sensitive Statistical Relational Learning approach. Knowl. Based Syst. Shuo Yang, Mohammed Korayem, Khalifeh AlJadda, Trey Grainger, Sriraam Natarajan
2017	—	`conf`	Discriminative boosted Bayes networks for learning multiple cardiovascular procedures. BIBM Nandini Ramanan, Shuo Yang, Shaun J. Grannis, Sriraam Natarajan
2017	B	`conf`	Does Race Play a Role in Invasive Procedure Treatments? An Initial Analysis. CHASE Noah Hammarlund, Sriraam Natarajan
2017	B	`conf`	Identifying Parkinson's Patients: A Functional Gradient Boosting Approach. AIME Devendra Singh Dhami, Ameet Soni, David Page, Sriraam Natarajan
2017	—	`conf`	Knowledge-Based Morphological Classification of Galaxies from Vision Features. AAAI Workshops Devendra Singh Dhami, David Leake, Sriraam Natarajan
2017	J	`jnl`	Markov logic networks for adverse drug event extraction from text. Knowl. Inf. Syst. Sriraam Natarajan, Vishal Bangera, Tushar Khot, Jose Picado, Anurag Wazalwar, Vítor Santos Costa, David Page, Michael Caldwell
2017	—	`conf`	Modeling heart procedures from EHRs: An application of exponential families. BIBM Shuo Yang, Fabian Hadiji, Kristian Kersting, Shaun J. Grannis, Sriraam Natarajan
2017	A*	`conf`	Poisson Sum-Product Networks: A Deep Architecture for Tractable Multivariate Poisson Distributions. AAAI Alejandro Molina, Sriraam Natarajan, Kristian Kersting
2017	B	`conf`	Relational Restricted Boltzmann Machines: A Probabilistic Logic Learning Approach. ILP Navdeep Kaur, Gautam Kunapuli, Tushar Khot, Kristian Kersting, William Cohen, Sriraam Natarajan
2017	J	`jnl`	Sum-Product Networks for Hybrid Domains. CoRR Alejandro Molina, Antonio Vergari, Nicola Di Mauro, Sriraam Natarajan, Floriana Esposito, Kristian Kersting
2017	—	`conf`	Towards Problem Solving Agents that Communicate and Learn. RoboNLP@ACL Anjali Narayan-Chen, Colin Graber, Mayukh Das, Md. Rakibul Islam, Soham Dan, Sriraam Natarajan, Janardhan Rao Doppa, Julia Hockenmaier, Martha Palmer, Dan Roth
2017	B	`conf`	User Friendly Automatic Construction of Background Knowledge: Mode Construction from ER Diagrams. K-CAP Alexander L. Hayes, Mayukh Das, Phillip Odom, Sriraam Natarajan
2016	—	`conf`	A Comparison of Weak Supervision methods for Knowledge Base Construction. AKBC@NAACL-HLT Ameet Soni, Dileep Viswanathan, Niranjan Pachaiyappan, Sriraam Natarajan
2016	A	`conf`	Active Advice Seeking for Inverse Reinforcement Learning. AAMAS Phillip Odom, Sriraam Natarajan
2016	—	`conf`	Actively Interacting with Experts: A Probabilistic Logic Approach. ECML/PKDD (2) Phillip Odom, Sriraam Natarajan
2016	J	`jnl`	Application of Statistical Relational Learning to Hybrid Recommendation Systems. CoRR Shuo Yang, Mohammed Korayem, Khalifeh AlJadda, Trey Grainger, Sriraam Natarajan
2016	—	`conf`	Deep Distant Supervision: Learning Statistical Relational Models for Weak Supervision in Natural Language Extraction. Solving Large Scale Learning Tasks Sriraam Natarajan, Ameet Soni, Anurag Wazalwar, Dileep Viswanathan, Kristian Kersting
2016	B	`conf`	Identifying Rare Diseases from Behavioural Data: A Machine Learning Approach. CHASE Haley MacLeod, Shuo Yang, Kim Oakes, Kay Connelly, Sriraam Natarajan
2016	B	`conf`	Inductive Logic Programming Meets Relational Databases: Efficient Learning of Markov Logic Networks. ILP Marcin Malec, Tushar Khot, James G. Nagy, Erik Blask, Sriraam Natarajan
2016	A*	`conf`	Learning Continuous-Time Bayesian Networks in Relational Domains: A Non-Parametric Approach. AAAI Shuo Yang, Tushar Khot, Kristian Kersting, Sriraam Natarajan
2016	B	`conf`	Learning Relational Dependency Networks for Relation Extraction. ILP Ameet Soni, Dileep Viswanathan, Jude W. Shavlik, Sriraam Natarajan
2016	J	`jnl`	Learning Relational Dependency Networks for Relation Extraction. CoRR Dileep Viswanathan, Ameet Soni, Jude W. Shavlik, Sriraam Natarajan
2016	B	`conf`	Learning Through Advice-Seeking via Transfer. ILP Phillip Odom, Raksha Kumaraswamy, Kristian Kersting, Sriraam Natarajan
2016	—	`ch.`	Relational Learning for Sustainable Health. Computational Sustainability Sriraam Natarajan, Peggy L. Peissig, David Page
2016	A	`conf`	Scaling Lifted Probabilistic Inference and Learning Via Graph Databases. SDM Mayukh Das, Yuqing Wu, Tushar Khot, Kristian Kersting, Sriraam Natarajan
2016	—	`book`	Statistical Relational Artificial Intelligence: Logic, Probability, and Computation Statistical Relational Artificial Intelligence Luc De Raedt, Kristian Kersting, Sriraam Natarajan, David Poole
2015	J	`jnl`	A Summary of the Twenty-Ninth AAAI Conference on Artificial Intelligence. AI Mag. Robert Morris, Blai Bonet, Marc Cavazza, Marie desJardins, Ariel Felner, Nick Hawes, Brad Knox, Sven Koenig, George Dimitri Konidaris, Jérôme Lang, Carlos Linares López, Daniele Magazzeni, Amy McGovern, Sriraam Natarajan, Nathan R. Sturtevant, Michael Thielscher, William Yeoh, Sebastian Sardiña, Kiri Wagstaff
2015	A*	`conf`	Active Advice Seeking for Inverse Reinforcement Learning. AAAI Phillip Odom, Sriraam Natarajan
2015	Misc	`conf`	Anomaly Detection in Text: The Value of Domain Knowledge. FLAIRS Raksha Kumaraswamy, Anurag Wazalwar, Tushar Khot, Jude W. Shavlik, Sriraam Natarajan
2015	B	`conf`	Extracting Adverse Drug Events from Text Using Human Advice. AIME Phillip Odom, Vishal Bangera, Tushar Khot, David Page, Sriraam Natarajan
2015	J	`jnl`	Gradient-based boosting for statistical relational learning: the Markov logic network and missing data cases. Mach. Learn. Tushar Khot, Sriraam Natarajan, Kristian Kersting, Jude W. Shavlik
2015	A*	`conf`	Knowledge-Based Probabilistic Logic Learning. AAAI Phillip Odom, Tushar Khot, Reid B. Porter, Sriraam Natarajan
2015	—	`conf`	Learning Probabilistic Logic Models with Human Advice. AAAI Spring Symposia Phillip Odom, Tushar Khot, Sriraam Natarajan
2015	A*	`conf`	Learning to Reject Sequential Importance Steps for Continuous-Time Bayesian Networks. AAAI Jeremy C. Weiss, Sriraam Natarajan, C. David Page Jr.
2015	B	`conf`	Modeling Coronary Artery Calcification Levels from Behavioral Data in a Clinical Study. AIME Shuo Yang, Kristian Kersting, Greg Terry, Jeffrey Carr, Sriraam Natarajan
2015	J	`jnl`	Poisson Dependency Networks: Gradient Boosted Models for Multivariate Count Data. Mach. Learn. Fabian Hadiji, Alejandro Molina, Sriraam Natarajan, Kristian Kersting
2015	J	`jnl`	Reports of the AAAI 2014 Conference Workshops. AI Mag. Stefano V. Albrecht, André da Motta Salles Barreto, Darius Braziunas, David L. Buckeridge, Heriberto Cuayáhuitl, Nina Dethlefs, Markus Endres, Amir-massoud Farahmand, Mark Fox, Lutz Frommberger, Sam Ganzfried, Yolanda Gil, Sébastien Guillet, Lawrence E. Hunter, Arnav Jhala, Kristian Kersting, George Dimitri Konidaris, Freddy Lécué, Sheila A. McIlraith, Sriraam Natarajan, Zeinab Noorian, David Poole, Rémi Ronfard, Alessandro Saffiotti, Arash Shaban-Nejad, Biplav Srivastava, Gerald Tesauro, Rosario Uceda-Sosa, Guy Van den Broeck, Martijn van Otterlo, Byron C. Wallace, Paul Weng, Jenna Wiens, Jie Zhang
2015	J	`jnl`	Statistical Relational Artificial Intelligence: From Distributions through Actions to Optimization. Künstliche Intell. Kristian Kersting, Sriraam Natarajan
2015	—	`conf`	TAC KBP 2015 : English Slot Filling Track Relational Learning with Expert Advice. TAC Dileep Viswanathan, Anurag Wazalwar, Sriraam Natarajan, Ameet Soni, Jude W. Shavlik
2015	A*	`conf`	Transfer Learning via Relational Type Matching. ICDM Raksha Kumaraswamy, Phillip Odom, Kristian Kersting, David Leake, Sriraam Natarajan
2014	J	`jnl`	A Decision-Theoretic Model of Assistance. J. Artif. Intell. Res. Alan Fern, Sriraam Natarajan, Kshitij Judah, Prasad Tadepalli
2014	—	`conf`	A Deeper Empirical Analysis of CBP Algorithm: Grounding Is the Bottleneck. StarAI@AAAI Shrutika Poyrekar, Sriraam Natarajan, Kristian Kersting
2014	A	`conf`	A graphical model approach to ATLAS-free mining of MRI images. SDM Chris S. Magnano, Ameet Soni, Sriraam Natarajan, Gautam Kunapuli
2014	—	`book`	Boosted Statistical Relational Learners - From Benchmarks to Data-Driven Medicine Sriraam Natarajan, Kristian Kersting, Tushar Khot, Jude W. Shavlik
2014	—	`conf`	Classification from One Class of Examples for Relational Domains. StarAI@AAAI Tushar Khot, Sriraam Natarajan, Jude W. Shavlik
2014	B	`conf`	Effectively Creating Weakly Labeled Training Examples via Approximate Domain Knowledge. ILP Sriraam Natarajan, Jose Picado, Tushar Khot, Kristian Kersting, Christopher Ré, Jude W. Shavlik
2014	A*	`conf`	Learning from Imbalanced Data in Relational Domains: A Soft Margin Approach. ICDM Shuo Yang, Tushar Khot, Kristian Kersting, Gautam Kunapuli, Kris Hauser, Sriraam Natarajan
2014	—	`conf`	Organizers. StarAI@AAAI Sriraam Natarajan
2014	—	`conf`	Population Size Extrapolation in Relational Probabilistic Modelling. SUM David Poole, David Buchman, Seyed Mehran Kazemi, Kristian Kersting, Sriraam Natarajan
2014	—	`conf`	Preface. StarAI@AAAI Guy Van den Broeck, Kristian Kersting, Sriraam Natarajan, David Poole
2014	A*	`conf`	Relational Logistic Regression. KR Seyed Mehran Kazemi, David Buchman, Kristian Kersting, Sriraam Natarajan, David Poole
2014	—	`conf`	Relational Logistic Regression: The Directed Analog of Markov Logic Networks. StarAI@AAAI Seyed Mehran Kazemi, David Buchman, Kristian Kersting, Sriraam Natarajan, David Poole
2014	A*	`conf`	Relational One-Class Classification: A Non-Parametric Approach. AAAI Tushar Khot, Sriraam Natarajan, Jude W. Shavlik
2014	J	`jnl`	Relational learning helps in three-way classification of Alzheimer patients from structural magnetic resonance images of the brain. Int. J. Mach. Learn. Cybern. Sriraam Natarajan, Baidya Nath Saha, Saket Joshi, Adam Edwards, Tushar Khot, Elizabeth M. Davenport, Kristian Kersting, Christopher T. Whitlow, Joseph A. Maldjian
2014	B	`conf`	Statistical Relational Learning for Handwriting Recognition. ILP Arti Shivram, Tushar Khot, Sriraam Natarajan, Venu Govindaraju
2013	—	`conf`	AR-Boost: Reducing Overfitting by a Robust Data-Driven Regularization Strategy. ECML/PKDD (3) Baidya Nath Saha, Gautam Kunapuli, Nilanjan Ray, Joseph A. Maldjian, Sriraam Natarajan
2013	B	`conf`	Accelerating Imitation Learning in Relational Domains via Transfer by Initialization. ILP Sriraam Natarajan, Phillip Odom, Saket Joshi, Tushar Khot, Kristian Kersting, Prasad Tadepalli
2013	—	`conf`	Bootstrapping Knowledge Base Acceleration. TREC Tushar Khot, Ce Zhang, Jude W. Shavlik, Sriraam Natarajan, Christopher Ré
2013	C	`conf`	Early Prediction of Coronary Artery Calcification Levels Using Machine Learning. IAAI Sriraam Natarajan, Kristian Kersting, Edward Hak-Sing Ip, David R. Jacobs Jr., Jeffrey Carr
2013	J	`jnl`	Exploiting symmetries for scaling loopy belief propagation and relational training. Mach. Learn. Babak Ahmadi, Kristian Kersting, Martin Mladenov, Sriraam Natarajan
2013	A*	`conf`	Guiding Autonomous Agents to Better Behaviors through Human Advice. ICDM Gautam Kunapuli, Phillip Odom, Jude W. Shavlik, Sriraam Natarajan
2013	—	`conf`	Knowledge Intensive Learning: Combining Qualitative Constraints with Causal Independence for Parameter Learning in Probabilistic Models. ECML/PKDD (2) Shuo Yang, Sriraam Natarajan
2013	—	`conf`	Learning When to Reject an Importance Sample. AAAI (Late-Breaking Developments) Jeremy C. Weiss, Sriraam Natarajan, C. David Page Jr.
2013	—	`conf`	MapReduce Lifting for Belief Propagation. StarAI@AAAI Babak Ahmadi, Kristian Kersting, Sriraam Natarajan
2013	—	`conf`	Preface. StarAI@AAAI Vibhav Gogate, Kristian Kersting, Sriraam Natarajan, David Poole
2013	J	`jnl`	The AAAI-13 Conference Workshops. AI Mag. Vikas Agrawal, Christopher Archibald, Mehul Bhatt, Hung Bui, Diane J. Cook, Juan Cortés, Christopher W. Geib, Vibhav Gogate, Hans W. Guesgen, Dietmar Jannach, Michael Johanson, Kristian Kersting, George Dimitri Konidaris, Lars Kotthoff, Martin Michalowski, Sriraam Natarajan, Barry O'Sullivan, Marc Pickett, Vedran Podobnik, David Poole, Lokendra Shastri, Amarda Shehu, Gita Sukthankar
2013	—	`conf`	Using Commonsense Knowledge to Automatically Create (Noisy) Training Examples from Text. StarAI@AAAI Sriraam Natarajan, Jose Picado, Tushar Khot, Kristian Kersting, Christopher Ré, Jude W. Shavlik
2012	—	`conf`	A Machine Learning Pipeline for Three-Way Classification of Alzheimer Patients from Structural Magnetic Resonance Images of the Brain. ICMLA (1) Sriraam Natarajan, Saket Joshi, Baidya Nath Saha, Adam Edwards, Tushar Khot, Elizabeth Moody, Kristian Kersting, Christopher T. Whitlow, Joseph A. Maldjian
2012	—	`conf`	A Novel Hierarchical Level Set with AR-boost for White Matter Lesion Segmentation in Diabetes. ICMLA (1) Baidya Nath Saha, Sriraam Natarajan, Gopi Kota, Christopher T. Whitlow, Donald W. Bowden, Jasmin Divers, Barry I. Freedman, Joseph A. Maldjian
2012	J	`jnl`	A relational hierarchical model for decision-theoretic assistance. Knowl. Inf. Syst. Sriraam Natarajan, Prasad Tadepalli, Alan Fern
2012	—	`conf`	Accelarating Imitation Learning in Relational Domains via Transfer by Initialization. StarAI@UAI Sriraam Natarajan, Phillip Odom, Saket Joshi, Tushar Khot, Kristian Kersting, Prasad Tadepalli
2012	—	`conf`	Aggregation and Population Growth: The Relational Logistic Regression and Markov Logic Cases. StarAI@UAI David Poole, David Buchman, Sriraam Natarajan, Kristian Kersting
2012	J	`jnl`	Counting Belief Propagation CoRR Kristian Kersting, Babak Ahmadi, Sriraam Natarajan
2012	J	`jnl`	Gradient-based boosting for statistical relational learning: The relational dependency network case. Mach. Learn. Sriraam Natarajan, Tushar Khot, Kristian Kersting, Bernd Gutmann, Jude W. Shavlik
2012	A*	`conf`	Identifying Adverse Drug Events by Relational Learning. AAAI David Page, Vítor Santos Costa, Sriraam Natarajan, Aubrey Barnard, Peggy L. Peissig, Michael Caldwell
2012	—	`conf`	Initial Empirical Evaluation of Anytime Lifted Belief Propagation. StarAI@UAI Richard G. Freedman, Rodrigo de Salvo Braz, Hung Bui, Sriraam Natarajan
2012	—	`conf`	Integrating Human Instructions and Reinforcement Learners: An SRL Approach. StarAI@UAI Pradyot Korupolu V. N., S. S. Manimaran, Balaraman Ravindran, Sriraam Natarajan
2012	—	`conf`	Learning Relational Structure for Temporal Relation Extraction. StarAI@UAI Tushar Khot, Siddharth Srivastava, Sriraam Natarajan, Jude W. Shavlik
2012	—	`conf`	Lifted Online Training of Relational Models with Stochastic Gradient Methods. ECML/PKDD (1) Babak Ahmadi, Kristian Kersting, Sriraam Natarajan
2012	J	`jnl`	Machine Learning for Personalized Medicine: Predicting Primary Myocardial Infarction from Electronic Health Records. AI Mag. Jeremy C. Weiss, Sriraam Natarajan, Peggy L. Peissig, Catherine A. McCarty, David Page
2012	—	`conf`	Multiplicative Forests for Continuous-Time Processes. NIPS Jeremy C. Weiss, Sriraam Natarajan, David Page
2012	C	`conf`	Statistical Relational Learning to Predict Primary Myocardial Infarction from Electronic Health Records. IAAI Jeremy C. Weiss, Sriraam Natarajan, Peggy L. Peissig, Catherine A. McCarty, David Page
2011	—	`conf`	A machine learning based approach to improve sidechain optimization. BCB Sabareesh Subramaniam, Sriraam Natarajan, Alessandro Senes
2011	A*	`conf`	Imitation Learning in Relational Domains: A Functional-Gradient Boosting Approach. IJCAI Sriraam Natarajan, Saket Joshi, Prasad Tadepalli, Kristian Kersting, Jude W. Shavlik
2011	A*	`conf`	Learning Markov Logic Networks via Functional Gradient Boosting. ICDM Tushar Khot, Sriraam Natarajan, Kristian Kersting, Jude W. Shavlik
2010	—	`ed.`	2nd International Workshop on Statistical Relational AI (StaRAI-12), held at the Uncertainty in Artificial Intelligence Conference (UAI 2012), Catalina Island, CA, USA, August 18, 2012 StarAI@UAI Henry A. Kautz, Kristian Kersting, Sriraam Natarajan, David Poole
2010	B	`conf`	Automating the ILP Setup Task: Converting User Advice about Specific Examples into General Background Knowledge. ILP Trevor Walker, Ciaran O'Reilly, Gautam Kunapuli, Sriraam Natarajan, Richard Maclin, David Page, Jude W. Shavlik
2010	—	`ch.`	Biomedical Informatics. Encyclopedia of Machine Learning C. David Page Jr., Sriraam Natarajan
2010	—	`conf`	Exploiting Causal Independence in Markov Logic Networks: Combining Undirected and Directed Models. StarAI@AAAI Sriraam Natarajan, Tushar Khot, Daniel Lowd, Prasad Tadepalli, Kristian Kersting, Jude W. Shavlik
2010	—	`conf`	Exploiting Causal Independence in Markov Logic Networks: Combining Undirected and Directed Models. ECML/PKDD (2) Sriraam Natarajan, Tushar Khot, Daniel Lowd, Prasad Tadepalli, Kristian Kersting, Jude W. Shavlik
2010	C	`conf`	Multi-Agent Inverse Reinforcement Learning. ICMLA Sriraam Natarajan, Gautam Kunapuli, Kshitij Judah, Prasad Tadepalli, Kristian Kersting, Jude W. Shavlik
2010	J	`jnl`	Reports of the AAAI 2010 Conference Workshops. AI Mag. David W. Aha, Mark S. Boddy, Vadim Bulitko, Artur S. d'Avila Garcez, Prashant Doshi, Stefan Edelkamp, Christopher W. Geib, Piotr J. Gmytrasiewicz, Robert P. Goldman, Pascal Hitzler, Charles L. Isbell Jr., Darsana P. Josyula, Leslie Pack Kaelbling, Kristian Kersting, Maithilee Kunda, Luís C. Lamb, Bhaskara Marthi, Keith McGreggor, Vivi Nastase, Gregory M. Provan, Anita Raja, Ashwin Ram, Mark O. Riedl, Stuart Russell, Ashish Sabharwal, Jan-Georg Smaus, Gita Sukthankar, Karl Tuyls, Ron van der Meyden, Alon Y. Halevy, Lilyana Mihalkova, Sriraam Natarajan
2009	—	`conf`	Anytime Lifted Belief Propagation. MLG/SRL@ILP Rodrigo de Salvo Braz, Sriraam Natarajan, Hung Bui, Jude W. Shavlik, Stuart Russell
2009	A	`conf`	Counting Belief Propagation. UAI Kristian Kersting, Babak Ahmadi, Sriraam Natarajan
2009	—	`conf`	Knowledge Intensive Learning - Directed vs Undirected SRL Models. MLG/SRL@ILP Sriraam Natarajan, Prasad Tadepalli, Gautam Kunapuli, Jude W. Shavlik
2009	C	`conf`	Learning Parameters for Relational Probabilistic Models with Noisy-Or Combining Rule. ICMLA Sriraam Natarajan, Prasad Tadepalli, Gautam Kunapuli, Jude W. Shavlik
2009	A*	`conf`	Speeding Up Inference in Markov Logic Networks by Preprocessing to Reduce the Size of the Resulting Grounded Network. IJCAI Jude W. Shavlik, Sriraam Natarajan
2008	—	`conf`	A context-aware personal desktop assistant. AAMAS (Demos) Hung Hai Bui, Federico Cesari, Daniel Elenius, David N. Morley, Sriraam Natarajan, Shahin Saadati, Eric Yeh, Neil Yorke-Smith
2008	J	`jnl`	Learning first-order probabilistic models with combining rules. Ann. Math. Artif. Intell. Sriraam Natarajan, Prasad Tadepalli, Thomas G. Dietterich, Alan Fern
2008	B	`conf`	Logical Hierarchical Hidden Markov Models for Modeling User Activities. ILP Sriraam Natarajan, Hung Hai Bui, Prasad Tadepalli, Kristian Kersting, Weng-Keen Wong
2008	J	`jnl`	Transfer in variable-reward hierarchical reinforcement learning. Mach. Learn. Neville Mehta, Sriraam Natarajan, Prasad Tadepalli, Alan Fern
2007	—	`conf`	A Decision-Theoretic Model of Assistance - Evaluation, Extensions and Open Problems. AAAI Spring Symposium: Interaction Challenges for Intelligent Assistants Sriraam Natarajan, Kshitij Judah, Prasad Tadepalli, Alan Fern
2007	—	`conf`	A Decision-Theoretic Model of Assistance - Evaluation, Extensions and Open Problems. Interaction Challenges for Intelligent Assistants Sriraam Natarajan, Kshitij Judah, Prasad Tadepalli, Alan Fern
2007	A*	`conf`	A Decision-Theoretic Model of Assistance. IJCAI Alan Fern, Sriraam Natarajan, Kshitij Judah, Prasad Tadepalli
2007	B	`conf`	A Relational Hierarchical Model for Decision-Theoretic Assistance. ILP Sriraam Natarajan, Prasad Tadepalli, Alan Fern
2007	—	`conf`	Exploiting prior knowledge in Intelligent Assistants - Combining relational models with hierarchies. Probabilistic, Logical and Relational Learning - A Further Synthesis Sriraam Natarajan, Prasad Tadepalli, Alan Fern
2005	A*	`conf`	Dynamic preferences in multi-criteria reinforcement learning. ICML Sriraam Natarajan, Prasad Tadepalli
2005	A*	`conf`	Learning first-order probabilistic models with combining rules. ICML Sriraam Natarajan, Prasad Tadepalli, Eric Altendorf, Thomas G. Dietterich, Alan Fern, Angelo C. Restificar