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ORCIDSriraam Natarajan
Person information
- affiliation: University of Texas at Dallas, Center for Machine Learning, TX, USA
- affiliation: Indiana University, School of Informatics and Computing, Bloomington, IN, USA
- affiliation: Wake Forest University, School of Medicine, NC, USA
- affiliation: University of Wisconsin Madison, Department of Computer Science, WI, USA
- affiliation (PhD 2007): Oregon State University, School of Electrical Engineering and Computer Science, Corvallis, OR, USA
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2020 – today
- 2024
[j30]Siwen Yan
, Sriraam Natarajan, Saket Joshi, Roni Khardon, Prasad Tadepalli:
Explainable models via compression of tree ensembles. Mach. Learn. 113(3): 1303-1328 (2024)- [c118]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. AAAI 2024: 22833-22841 - [c117]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:
Modeling Multiple Adverse Pregnancy Outcomes: Learning from Diverse Data Sources. AIME (1) 2024: 293-302 - [c116]Sahil Sidheekh, Saurabh Mathur, Athresh Karanam, Sriraam Natarajan:
Deep Tractable Probabilistic Models. COMAD/CODS 2024: 501-504 - [c115]Sahil Sidheekh, Pranuthi Tenali, Saurabh Mathur, Erik Blasch, Sriraam Natarajan:
On the Robustness and Reliability of Late Multi-Modal Fusion using Probabilistic Circuits. FUSION 2024: 1-8 - [c114]Sahil Sidheekh, Sriraam Natarajan:
Building Expressive and Tractable Probabilistic Generative Models: A Review. IJCAI 2024: 8234-8243 - [c113]Brian Ricks, Patrick Tague, Bhavani Thuraisingham, Sriraam Natarajan:
Utilizing Threat Partitioning for More Practical Network Anomaly Detection. SACMAT 2024 - [e3]Michael J. Wooldridge, Jennifer G. Dy, Sriraam Natarajan:
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 Press 2024 [contents] - [e2]Sriraam Natarajan, Indrajit Bhattacharya, Richa Singh, Arun Kumar, Sayan Ranu, Kalika Bali, Abinaya K:
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. ACM 2024 [contents] - [i35]Sahil Sidheekh, Sriraam Natarajan:
Building Expressive and Tractable Probabilistic Generative Models: A Review. CoRR abs/2402.00759 (2024) - [i34]Sahil Sidheekh, Pranuthi Tenali, Saurabh Mathur, Erik Blasch, Kristian Kersting, Sriraam Natarajan:
Credibility-Aware Multi-Modal Fusion Using Probabilistic Circuits. CoRR abs/2403.03281 (2024) - [i33]Athresh Karanam, Saurabh Mathur, Sahil Sidheekh, Sriraam Natarajan:
A Unified Framework for Human-Allied Learning of Probabilistic Circuits. CoRR abs/2405.02413 (2024) - 2023
- [j29]Srijita Das, Nandini Ramanan, Gautam Kunapuli, Predrag Radivojac, Sriraam Natarajan:
Active feature elicitation: An unified framework. Frontiers Artif. Intell. 6 (2023) - [j28]Siwen Yan, Phillip Odom, Rahul Pasunuri, Kristian Kersting, Sriraam Natarajan:
Learning with privileged and sensitive information: a gradient-boosting approach. Frontiers Artif. Intell. 6 (2023) - [j27]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 Comput. Appl. 35(23): 16877-16892 (2023) - [c112]Sriraam Natarajan, Kristian Kersting:
Never Ending Reasoning and Learning: Opportunities and Challenges. AAAI Bridge Program 2023: 71-74 - [c111]Nandini Ramanan, Phillip Odom, Kristian Kersting, Sriraam Natarajan:
Active Feature Acquisition via Human Interaction in Relational domains. COMAD/CODS 2023: 70-78 - [c110]Saurabh Mathur, Vibhav Gogate, Sriraam Natarajan:
Knowledge Intensive Learning of Cutset Networks. UAI 2023: 1380-1389 - [c109]Sahil Sidheekh, Kristian Kersting, Sriraam Natarajan:
Probabilistic Flow Circuits: Towards Unified Deep Models for Tractable Probabilistic Inference. UAI 2023: 1964-1973 - [i32]Alakh Aggarwal, Rishita Bansal, Parth Padalkar, Sriraam Natarajan:
MACOptions: Multi-Agent Learning with Centralized Controller and Options Framework. CoRR abs/2302.03800 (2023) - [i31]Siwen Yan, Phillip Odom, Sriraam Natarajan:
Knowledge-based Refinement of Scientific Publication Knowledge Graphs. CoRR abs/2309.05681 (2023) - [i30]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. CoRR abs/2309.09404 (2023) - 2022
- [c108]Michael A. Skinner, Priscilla Yu, Lakshmi Raman, Sriraam Natarajan:
An Anytime Querying Algorithm for Predicting Cardiac Arrest in Children: Work-in-Progress. AIME 2022: 353-357 - [c107]Yuqiao Chen, Sriraam Natarajan, Nicholas Ruozzi:
Relational Neural Markov Random Fields. AISTATS 2022: 8260-8269 - [c106]Nandini Ramanan, Phillip Odom, Erik Blasch, Kristian Kersting, Sriraam Natarajan:
Relational Active Feature Elicitation for DDDAS. DDDAS 2022: 227-232 - [c105]Harsha Kokel, Nikhilesh Prabhakar, Balaraman Ravindran, Erik Blasch, Prasad Tadepalli, Sriraam Natarajan:
Hybrid Deep RePReL: Integrating Relational Planning and Reinforcement Learning for Information Fusion. FUSION 2022: 1-8 - [c104]Athresh Karanam, Saurabh Mathur, Predrag Radivojac, David M. Haas, Kristian Kersting, Sriraam Natarajan:
Explaining Deep Tractable Probabilistic Models: The sum-product network case. PGM 2022: 325-336 - [i29]Siwen Yan, Sriraam Natarajan, Saket Joshi, Roni Khardon, Prasad Tadepalli:
Explainable Models via Compression of Tree Ensembles. CoRR abs/2206.07904 (2022) - [i28]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:
Human-guided Collaborative Problem Solving: A Natural Language based Framework. CoRR abs/2207.09566 (2022) - 2021
- [j26]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 Min. Knowl. Discov. 35(5): 2089-2111 (2021) - [c103]Ashutosh Kakadiya, Sriraam Natarajan, Balaraman Ravindran:
Relational Boosted Bandits. AAAI 2021: 12123-12130 - [c102]Devendra Singh Dhami, Siwen Yan, Gautam Kunapuli, David Page, Sriraam Natarajan:
Predicting Drug-Drug Interactions from Heterogeneous Data: An Embedding Approach. AIME 2021: 252-257 - [c101]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. AIME 2021: 497-502 - [c100]Harsha Kokel, Arjun Manoharan, Sriraam Natarajan, Balaraman Ravindran, Prasad Tadepalli:
RePReL: Integrating Relational Planning and Reinforcement Learning for Effective Abstraction. ICAPS 2021: 533-541 - [c99]Srijita Das, Rishabh K. Iyer, Sriraam Natarajan:
A Clustering based Selection Framework for Cost Aware and Test-time Feature Elicitation. COMAD/CODS 2021: 20-28 - [c98]Mayukh Das, Devendra Singh Dhami, Yang Yu, Gautam Kunapuli, Sriraam Natarajan:
Human-Guided Learning of Column Networks: Knowledge Injection for Relational Deep Learning. COMAD/CODS 2021: 110-118 - [c97]Devendra Singh Dhami, Siwen Yan, Gautam Kunapuli, Sriraam Natarajan:
Non-parametric Learning of Embeddings for Relational Data Using Gaifman Locality Theorem. ILP 2021: 95-110 - [c96]Devendra Singh Dhami, Mayukh Das, Sriraam Natarajan:
Beyond Simple Images: Human Knowledge-Guided GANs for Clinical Data Generation. KR 2021: 247-257 - [c95]Matej Zecevic, Devendra Singh Dhami, Athresh Karanam, Sriraam Natarajan, Kristian Kersting:
Interventional Sum-Product Networks: Causal Inference with Tractable Probabilistic Models. NeurIPS 2021: 15019-15031 - [i27]Devendra Singh Dhami, Siwen Yan, Sriraam Natarajan:
Bridging Graph Neural Networks and Statistical Relational Learning: Relational One-Class GCN. CoRR abs/2102.07007 (2021) - [i26]Matej Zecevic, Devendra Singh Dhami, Athresh Karanam, Sriraam Natarajan, Kristian Kersting:
Interventional Sum-Product Networks: Causal Inference with Tractable Probabilistic Models. CoRR abs/2102.10440 (2021) - [i25]Devendra Singh Dhami, Siwen Yan, Gautam Kunapuli, David Page, Sriraam Natarajan:
Predicting Drug-Drug Interactions from Heterogeneous Data: An Embedding Approach. CoRR abs/2103.10916 (2021) - [i24]Harsha Kokel, Arjun Manoharan, Sriraam Natarajan, Balaraman Ravindran, Prasad Tadepalli:
Dynamic probabilistic logic models for effective abstractions in RL. CoRR abs/2110.08318 (2021) - [i23]Yuqiao Chen, Sriraam Natarajan, Nicholas Ruozzi:
Relational Neural Markov Random Fields. CoRR abs/2110.09647 (2021) - [i22]Athresh Karanam, Saurabh Mathur, Predrag Radivojac, Kristian Kersting, Sriraam Natarajan:
Explaining Deep Tractable Probabilistic Models: The sum-product network case. CoRR abs/2110.09778 (2021) - 2020
- [j25]Yan Liu, Sriraam Natarajan:
The Best of SIAM Data Mining 2020. Big Data 8(5): 333-334 (2020) - [j24]Nandini Ramanan, Sriraam Natarajan:
Causal Learning From Predictive Modeling for Observational Data. Frontiers Big Data 3: 535976 (2020) - [j23]Mayukh Das, Nandini Ramanan, Janardhan Rao Doppa, Sriraam Natarajan:
Few-Shot Induction of Generalized Logical Concepts via Human Guidance. Frontiers Robotics AI 7: 122 (2020) - [j22]Navdeep Kaur, Gautam Kunapuli, Sriraam Natarajan:
Non-parametric learning of lifted Restricted Boltzmann Machines. Int. J. Approx. Reason. 120: 33-47 (2020) - [j21]Raksha Kumaraswamy, Nandini Ramanan, Phillip Odom, Sriraam Natarajan:
Interactive Transfer Learning in Relational Domains. Künstliche Intell. 34(2): 181-192 (2020) - [c94]Harsha Kokel, Phillip Odom, Shuo Yang, Sriraam Natarajan:
A Unified Framework for Knowledge Intensive Gradient Boosting: Leveraging Human Experts for Noisy Sparse Domains. AAAI 2020: 4460-4468 - [c93]Siwen Yan, Devendra Singh Dhami, Sriraam Natarajan:
The Curious Case of Stacking Boosted Relational Dependency Networks. ICBINB@NeurIPS 2020: 33-42 - [c92]Yuqiao Chen, Yibo Yang, Sriraam Natarajan, Nicholas Ruozzi:
Lifted Hybrid Variational Inference. IJCAI 2020: 4237-4244 - [c91]Devendra Singh Dhami, Mayukh Das, Sriraam Natarajan:
Knowledge Intensive Learning of Generative Adversarial Networks. KiML@KDD 2020: 10-16 - [c90]Srijita Das, Rishabh K. Iyer, Sriraam Natarajan:
Cost Aware Feature Elicitation. KiML@KDD 2020: 23-29 - [c89]Nandini Ramanan, Mayukh Das, Kristian Kersting, Sriraam Natarajan:
Discriminative Non-Parametric Learning of Arithmetic Circuits. PGM 2020: 353-364 - [i21]Devendra Singh Dhami, Siwen Yan, Gautam Kunapuli, Sriraam Natarajan:
Non-Parametric Learning of Gaifman Models. CoRR abs/2001.00528 (2020) - [i20]Yuqiao Chen, Yibo Yang, Sriraam Natarajan, Nicholas Ruozzi:
Lifted Hybrid Variational Inference. CoRR abs/2001.02773 (2020) - [i19]Michael A. Skinner, Lakshmi Raman, Neel Shah, Abdelaziz Farhat, Sriraam Natarajan:
A Preliminary Approach for Learning Relational Policies for the Management of Critically Ill Children. CoRR abs/2001.04432 (2020) - [i18]Navdeep Kaur, Gautam Kunapuli, Sriraam Natarajan:
Non-Parametric Learning of Lifted Restricted Boltzmann Machines. CoRR abs/2001.10070 (2020) - [i17]Navdeep Kaur, Gautam Kunapuli, Sriraam Natarajan:
Knowledge Graph Alignment using String Edit Distance. CoRR abs/2003.12145 (2020) - [i16]Srijita Das, Sriraam Natarajan, Kaushik Roy, Ronald Parr, Kristian Kersting:
Fitted Q-Learning for Relational Domains. CoRR abs/2006.05595 (2020) - [i15]Ashutosh Kakadiya, Sriraam Natarajan, Balaraman Ravindran:
Relational Boosted Bandits. CoRR abs/2012.09220 (2020)
2010 – 2019
- 2019
- [j20]Fabrizio Riguzzi, Kristian Kersting, Marco Lippi, Sriraam Natarajan:
Editorial: Statistical Relational Artificial Intelligence. Frontiers Robotics AI 6: 68 (2019) - [j19]Mayukh Das, Phillip Odom, Md. Rakibul Islam, Janardhan Rao Doppa, Dan Roth, Sriraam Natarajan:
Planning with actively eliciting preferences. Knowl. Based Syst. 165: 219-227 (2019) - [c88]Mayukh Das, Devendra Singh Dhami, Gautam Kunapuli, Kristian Kersting, Sriraam Natarajan:
Fast Relational Probabilistic Inference and Learning: Approximate Counting via Hypergraphs. AAAI 2019: 7816-7824 - [c87]Yuqiao Chen, Nicholas Ruozzi, Sriraam Natarajan:
Lifted Message Passing for Hybrid Probabilistic Inference. IJCAI 2019: 5701-5707 - [c86]Navdeep Kaur, Gautam Kunapuli, Saket Joshi, Kristian Kersting, Sriraam Natarajan:
Neural Networks for Relational Data. ILP 2019: 62-71 - [i14]Mayukh Das, Yang Yu, Devendra Singh Dhami, Gautam Kunapuli, Sriraam Natarajan:
Human-Guided Learning of Column Networks: Augmenting Deep Learning with Advice. CoRR abs/1904.06950 (2019) - [i13]Mayukh Das, Devendra Singh Dhami, Yang Yu, Gautam Kunapuli, Sriraam Natarajan:
Knowledge-augmented Column Networks: Guiding Deep Learning with Advice. CoRR abs/1906.01432 (2019) - [i12]Navdeep Kaur, Gautam Kunapuli, Saket Joshi, Kristian Kersting, Sriraam Natarajan:
Neural Networks for Relational Data. CoRR abs/1909.04723 (2019) - [i11]Devendra Singh Dhami, Gautam Kunapuli, David Page, Sriraam Natarajan:
Predicting Drug-Drug Interactions from Molecular Structure Images. CoRR abs/1911.06356 (2019) - [i10]Mayukh Das, Nandini Ramanan, Janardhan Rao Doppa, Sriraam Natarajan:
One-Shot Induction of Generalized Logical Concepts via Human Guidance. CoRR abs/1912.07060 (2019) - [i9]Alexander L. Hayes, Mayukh Das, Phillip Odom, Sriraam Natarajan:
User Friendly Automatic Construction of Background Knowledge: Mode Construction from ER Diagrams. CoRR abs/1912.07650 (2019) - 2018
- [j18]Phillip Odom, Sriraam Natarajan:
Human-Guided Learning for Probabilistic Logic Models. Frontiers Robotics AI 5: 56 (2018) - [c85]Alejandro Molina, Antonio Vergari, Nicola Di Mauro, Sriraam Natarajan, Floriana Esposito, Kristian Kersting:
Mixed Sum-Product Networks: A Deep Architecture for Hybrid Domains. AAAI 2018: 3828-3835 - [c84]Mayukh Das, Phillip Odom, Md. Rakibul Islam, Janardhan Rao Doppa, Dan Roth, Sriraam Natarajan:
Preference-Guided Planning: An Active Elicitation Approach. AAMAS 2018: 1921-1923 - [c83]Erik Blasch, Robert Cruise, Sriraam Natarajan, Ali K. Raz, Tim Kelly:
Control Diffusion of Information Collection for Situation Understanding Using Boosting MLNs. FUSION 2018: 1-8 - [c82]Sriraam Natarajan, Srijita Das, Nandini Ramanan, Gautam Kunapuli, Predrag Radivojac:
On Whom Should I Perform this Lab Test Next? An Active Feature Elicitation Approach. IJCAI 2018: 3498-3505 - [c81]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. KR 2018: 661-662 - [i8]Mayukh Das, Phillip Odom, Md. Rakibul Islam, Janardhan Rao Doppa, Dan Roth, Sriraam Natarajan:
Preference-Guided Planning: An Active Elicitation Approach. CoRR abs/1804.07404 (2018) - [i7]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. CoRR abs/1808.02123 (2018) - 2017
- [j17]Sriraam Natarajan, Vishal Bangera, Tushar Khot, Jose Picado, Anurag Wazalwar, Vítor Santos Costa, David Page, Michael Caldwell:
Markov logic networks for adverse drug event extraction from text. Knowl. Inf. Syst. 51(2): 435-457 (2017) - [j16]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. Knowl. Based Syst. 136: 37-45 (2017) - [c80]Mayukh Das, Md. Rakibul Islam, Janardhan Rao Doppa, Dan Roth, Sriraam Natarajan:
Active Preference Elicitation for Planning. AAAI Workshops 2017 - [c79]Devendra Singh Dhami, David Leake, Sriraam Natarajan:
Knowledge-Based Morphological Classification of Galaxies from Vision Features. AAAI Workshops 2017 - [c78]Alejandro Molina, Sriraam Natarajan, Kristian Kersting:
Poisson Sum-Product Networks: A Deep Architecture for Tractable Multivariate Poisson Distributions. AAAI 2017: 2357-2363 - [c77]Anjali Narayan-Chen, Colin Graber, Mayukh Das, Md. Rakibul Islam, Soham Dan, Sriraam Natarajan, Janardhan Rao Doppa, Julia Hockenmaier, Martha Palmer, Dan Roth:
Towards Problem Solving Agents that Communicate and Learn. RoboNLP@ACL 2017: 95-103 - [c76]Devendra Singh Dhami, Ameet Soni, David Page, Sriraam Natarajan:
Identifying Parkinson's Patients: A Functional Gradient Boosting Approach. AIME 2017: 332-337 - [c75]Shuo Yang, Fabian Hadiji, Kristian Kersting, Shaun J. Grannis, Sriraam Natarajan:
Modeling heart procedures from EHRs: An application of exponential families. BIBM 2017: 491-497 - [c74]Nandini Ramanan, Shuo Yang, Shaun J. Grannis, Sriraam Natarajan:
Discriminative boosted Bayes networks for learning multiple cardiovascular procedures. BIBM 2017: 870-873 - [c73]Sriraam Natarajan, Annu Prabhakar, Nandini Ramanan, Anna N. Baglione, Katie A. Siek, Kay Connelly:
Boosting for Postpartum Depression Prediction. CHASE 2017: 232-240 - [c72]Noah Hammarlund, Sriraam Natarajan:
Does Race Play a Role in Invasive Procedure Treatments? An Initial Analysis. CHASE 2017: 243-244 - [c71]Navdeep Kaur, Gautam Kunapuli, Tushar Khot, Kristian Kersting, William Cohen, Sriraam Natarajan:
Relational Restricted Boltzmann Machines: A Probabilistic Logic Learning Approach. ILP 2017: 94-111 - [c70]Alexander L. Hayes, Mayukh Das, Phillip Odom, Sriraam Natarajan:
User Friendly Automatic Construction of Background Knowledge: Mode Construction from ER Diagrams. K-CAP 2017: 30:1-30:8 - [r2]C. David Page, Sriraam Natarajan:
Biomedical Informatics. Encyclopedia of Machine Learning and Data Mining 2017: 143-163 - [i6]Alejandro Molina, Antonio Vergari, Nicola Di Mauro, Sriraam Natarajan, Floriana Esposito, Kristian Kersting:
Sum-Product Networks for Hybrid Domains. CoRR abs/1710.03297 (2017) - 2016
- [b2]Luc De Raedt, Kristian Kersting, Sriraam Natarajan, David Poole:
Statistical Relational Artificial Intelligence: Logic, Probability, and Computation. Synthesis Lectures on Artificial Intelligence and Machine Learning, Morgan & Claypool Publishers 2016, ISBN 978-3-031-00022-5 - [c69]Shuo Yang, Tushar Khot, Kristian Kersting, Sriraam Natarajan:
Learning Continuous-Time Bayesian Networks in Relational Domains: A Non-Parametric Approach. AAAI 2016: 2265-2271 - [c68]Ameet Soni, Dileep Viswanathan, Niranjan Pachaiyappan, Sriraam Natarajan:
A Comparison of Weak Supervision methods for Knowledge Base Construction. AKBC@NAACL-HLT 2016: 97-102 - [c67]Phillip Odom, Sriraam Natarajan:
Active Advice Seeking for Inverse Reinforcement Learning. AAMAS 2016: 512-520 - [c66]Sriraam Natarajan, Ameet Soni, Anurag Wazalwar, Dileep Viswanathan, Kristian Kersting:
Deep Distant Supervision: Learning Statistical Relational Models for Weak Supervision in Natural Language Extraction. Solving Large Scale Learning Tasks 2016: 331-345 - [c65]Haley MacLeod, Shuo Yang, Kim Oakes, Kay Connelly, Sriraam Natarajan:
Identifying Rare Diseases from Behavioural Data: A Machine Learning Approach. CHASE 2016: 130-139 - [c64]Marcin Malec, Tushar Khot, James G. Nagy, Erik Blask, Sriraam Natarajan:
Inductive Logic Programming Meets Relational Databases: Efficient Learning of Markov Logic Networks. ILP 2016: 14-26 - [c63]Phillip Odom, Raksha Kumaraswamy, Kristian Kersting, Sriraam Natarajan:
Learning Through Advice-Seeking via Transfer. ILP 2016: 40-51 - [c62]Ameet Soni, Dileep Viswanathan, Jude W. Shavlik, Sriraam Natarajan:
Learning Relational Dependency Networks for Relation Extraction. ILP 2016: 81-93 - [c61]Phillip Odom, Sriraam Natarajan:
Actively Interacting with Experts: A Probabilistic Logic Approach. ECML/PKDD (2) 2016: 527-542 - [c60]Mayukh Das, Yuqing Wu, Tushar Khot, Kristian Kersting, Sriraam Natarajan:
Scaling Lifted Probabilistic Inference and Learning Via Graph Databases. SDM 2016: 738-746 - [p1]Sriraam Natarajan, Peggy L. Peissig, David Page:
Relational Learning for Sustainable Health. Computational Sustainability 2016: 245-264 - [i5]Dileep Viswanathan, Ameet Soni, Jude W. Shavlik, Sriraam Natarajan:
Learning Relational Dependency Networks for Relation Extraction. CoRR abs/1607.00424 (2016) - [i4]Shuo Yang, Mohammed Korayem, Khalifeh AlJadda, Trey Grainger, Sriraam Natarajan:
Application of Statistical Relational Learning to Hybrid Recommendation Systems. CoRR abs/1607.01050 (2016) - 2015
- [j15]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:
Reports of the AAAI 2014 Conference Workshops. AI Mag. 36(1): 87-98 (2015) - [j14]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:
A Summary of the Twenty-Ninth AAAI Conference on Artificial Intelligence. AI Mag. 36(3): 99-106 (2015) - [j13]Kristian Kersting, Sriraam Natarajan:
Statistical Relational Artificial Intelligence: From Distributions through Actions to Optimization. Künstliche Intell. 29(4): 363-368 (2015) - [j12]Tushar Khot, Sriraam Natarajan, Kristian Kersting, Jude W. Shavlik:
Gradient-based boosting for statistical relational learning: the Markov logic network and missing data cases. Mach. Learn. 100(1): 75-100 (2015) - [j11]Fabian Hadiji, Alejandro Molina, Sriraam Natarajan, Kristian Kersting:
Poisson Dependency Networks: Gradient Boosted Models for Multivariate Count Data. Mach. Learn. 100(2-3): 477-507 (2015) - [c59]Phillip Odom, Tushar Khot, Reid B. Porter, Sriraam Natarajan:
Knowledge-Based Probabilistic Logic Learning. AAAI 2015: 3564-3570 - [c58]Jeremy C. Weiss, Sriraam Natarajan, C. David Page Jr.:
Learning to Reject Sequential Importance Steps for Continuous-Time Bayesian Networks. AAAI 2015: 3628-3634 - [c57]Phillip Odom, Sriraam Natarajan:
Active Advice Seeking for Inverse Reinforcement Learning. AAAI 2015: 4186-4187 - [c56]Phillip Odom, Tushar Khot, Sriraam Natarajan:
Learning Probabilistic Logic Models with Human Advice. AAAI Spring Symposia 2015 - [c55]Shuo Yang, Kristian Kersting, Greg Terry, Jefferey Carr, Sriraam Natarajan:
Modeling Coronary Artery Calcification Levels from Behavioral Data in a Clinical Study. AIME 2015: 182-187 - [c54]Phillip Odom, Vishal Bangera, Tushar Khot, David Page, Sriraam Natarajan:
Extracting Adverse Drug Events from Text Using Human Advice. AIME 2015: 195-204 - [c53]Raksha Kumaraswamy, Anurag Wazalwar, Tushar Khot, Jude W. Shavlik, Sriraam Natarajan:
Anomaly Detection in Text: The Value of Domain Knowledge. FLAIRS 2015: 225-228 - [c52]Raksha Kumaraswamy, Phillip Odom, Kristian Kersting, David Leake, Sriraam Natarajan:
Transfer Learning via Relational Type Matching. ICDM 2015: 811-816 - [i3]Dileep Viswanathan, Anurag Wazalwar, Sriraam Natarajan, Ameet Soni, Jude W. Shavlik:
TAC KBP 2015 : English Slot Filling Track Relational Learning with Expert Advice. TAC 2015 - 2014
- [b1]Sriraam Natarajan, Kristian Kersting, Tushar Khot, Jude W. Shavlik:
Boosted Statistical Relational Learners - From Benchmarks to Data-Driven Medicine. Springer Briefs in Computer Science, Springer 2014, ISBN 978-3-319-13643-1, pp. 1-68 - [j10]Alan Fern, Sriraam Natarajan, Kshitij Judah, Prasad Tadepalli:
A Decision-Theoretic Model of Assistance. J. Artif. Intell. Res. 50: 71-104 (2014) - [j9]Sriraam Natarajan, Baidya Nath Saha, Saket Joshi, Adam Edwards, Tushar Khot, Elizabeth M. Davenport, 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. Int. J. Mach. Learn. Cybern. 5(5): 659-669 (2014) - [c51]Guy Van den Broeck, Kristian Kersting, Sriraam Natarajan, David Poole:
Preface. StarAI@AAAI 2014 - [c50]Seyed Mehran Kazemi, David Buchman, Kristian Kersting, Sriraam Natarajan, David Poole:
Relational Logistic Regression: The Directed Analog of Markov Logic Networks. StarAI@AAAI 2014 - [c49]Tushar Khot, Sriraam Natarajan, Jude W. Shavlik:
Relational One-Class Classification: A Non-Parametric Approach. AAAI 2014: 2453-2459 - [c48]Tushar Khot, Sriraam Natarajan, Jude W. Shavlik:
Classification from One Class of Examples for Relational Domains. StarAI@AAAI 2014 - [c47]Sriraam Natarajan:
Organizers. StarAI@AAAI 2014 - [c46]Shrutika Poyrekar, Sriraam Natarajan, Kristian Kersting:
A Deeper Empirical Analysis of CBP Algorithm: Grounding Is the Bottleneck. StarAI@AAAI 2014 - [c45]Shuo Yang, Tushar Khot, Kristian Kersting, Gautam Kunapuli, Kris Hauser, Sriraam Natarajan:
Learning from Imbalanced Data in Relational Domains: A Soft Margin Approach. ICDM 2014: 1085-1090 - [c44]Sriraam Natarajan, Jose Picado, Tushar Khot, Kristian Kersting, Christopher Ré, Jude W. Shavlik:
Effectively Creating Weakly Labeled Training Examples via Approximate Domain Knowledge. ILP 2014: 92-107 - [c43]Arti Shivram, Tushar Khot, Sriraam Natarajan, Venu Govindaraju:
Statistical Relational Learning for Handwriting Recognition. ILP 2014: 126-138 - [c42]Seyed Mehran Kazemi, David Buchman, Kristian Kersting, Sriraam Natarajan, David Poole:
Relational Logistic Regression. KR 2014 - [c41]Chris S. Magnano, Ameet Soni, Sriraam Natarajan, Gautam Kunapuli:
A graphical model approach to ATLAS-free mining of MRI images. SDM 2014: 974-982 - [c40]David Poole, David Buchman, Seyed Mehran Kazemi, Kristian Kersting, Sriraam Natarajan:
Population Size Extrapolation in Relational Probabilistic Modelling. SUM 2014: 292-305 - 2013
- [j8]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:
The AAAI-13 Conference Workshops. AI Mag. 34(4): 9- (2013) - [j7]Babak Ahmadi, Kristian Kersting, Martin Mladenov, Sriraam Natarajan:
Exploiting symmetries for scaling loopy belief propagation and relational training. Mach. Learn. 92(1): 91-132 (2013) - [c39]Babak Ahmadi, Kristian Kersting, Sriraam Natarajan:
MapReduce Lifting for Belief Propagation. StarAI@AAAI 2013 - [c38]Vibhav Gogate, Kristian Kersting, Sriraam Natarajan, David Poole:
Preface. StarAI@AAAI 2013 - [c37]Sriraam Natarajan, Jose Picado, Tushar Khot, Kristian Kersting, Christopher Ré, Jude W. Shavlik:
Using Commonsense Knowledge to Automatically Create (Noisy) Training Examples from Text. StarAI@AAAI 2013 - [c36]Jeremy C. Weiss, Sriraam Natarajan, C. David Page Jr.:
Learning When to Reject an Importance Sample. AAAI (Late-Breaking Developments) 2013 - [c35]Sriraam Natarajan, Kristian Kersting, Edward Hak-Sing Ip, David R. Jacobs Jr., Jeffrey Carr:
Early Prediction of Coronary Artery Calcification Levels Using Machine Learning. IAAI 2013: 1557-1562 - [c34]Gautam Kunapuli, Phillip Odom, Jude W. Shavlik, Sriraam Natarajan:
Guiding Autonomous Agents to Better Behaviors through Human Advice. ICDM 2013: 409-418 - [c33]Sriraam Natarajan, Phillip Odom, Saket Joshi, Tushar Khot, Kristian Kersting, Prasad Tadepalli:
Accelerating Imitation Learning in Relational Domains via Transfer by Initialization. ILP 2013: 64-75 - [c32]Baidya Nath Saha, Gautam Kunapuli, Nilanjan Ray, Joseph A. Maldjian, Sriraam Natarajan:
AR-Boost: Reducing Overfitting by a Robust Data-Driven Regularization Strategy. ECML/PKDD (3) 2013: 1-16 - [c31]Shuo Yang, Sriraam Natarajan:
Knowledge Intensive Learning: Combining Qualitative Constraints with Causal Independence for Parameter Learning in Probabilistic Models. ECML/PKDD (2) 2013: 580-595 - [c30]Tushar Khot, Ce Zhang, Jude W. Shavlik, Sriraam Natarajan, Christopher Ré:
Bootstrapping Knowledge Base Acceleration. TREC 2013 - 2012
- [j6]Jeremy C. Weiss, Sriraam Natarajan, Peggy L. Peissig, Catherine A. McCarty, David Page:
Machine Learning for Personalized Medicine: Predicting Primary Myocardial Infarction from Electronic Health Records. AI Mag. 33(4): 33-45 (2012) - [j5]Sriraam Natarajan, Prasad Tadepalli, Alan Fern:
A relational hierarchical model for decision-theoretic assistance. Knowl. Inf. Syst. 32(2): 329-349 (2012) - [j4]Sriraam Natarajan, Tushar Khot, Kristian Kersting, Bernd Gutmann, Jude W. Shavlik:
Gradient-based boosting for statistical relational learning: The relational dependency network case. Mach. Learn. 86(1): 25-56 (2012) - [c29]David Page, Vítor Santos Costa, Sriraam Natarajan, Aubrey Barnard, Peggy L. Peissig, Michael Caldwell:
Identifying Adverse Drug Events by Relational Learning. AAAI 2012: 1599-1605 - [c28]Jeremy C. Weiss, Sriraam Natarajan, Peggy L. Peissig, Catherine A. McCarty, David Page:
Statistical Relational Learning to Predict Primary Myocardial Infarction from Electronic Health Records. IAAI 2012: 2341-2347 - [c27]Baidya Nath 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. ICMLA (1) 2012: 90-95 - [c26]Sriraam Natarajan, Saket Joshi, Baidya Nath Saha, Adam Edwards, Tushar Khot, Elizabeth Moody, 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. ICMLA (1) 2012: 203-208 - [c25]Jeremy C. Weiss, Sriraam Natarajan, David Page:
Multiplicative Forests for Continuous-Time Processes. NIPS 2012: 467-475 - [c24]Babak Ahmadi, Kristian Kersting, Sriraam Natarajan:
Lifted Online Training of Relational Models with Stochastic Gradient Methods. ECML/PKDD (1) 2012: 585-600 - [c23]Richard G. Freedman, Rodrigo de Salvo Braz, Hung Bui, Sriraam Natarajan:
Initial Empirical Evaluation of Anytime Lifted Belief Propagation. StarAI@UAI 2012 - [c22]Tushar Khot, Siddharth Srivastava, Sriraam Natarajan, Jude W. Shavlik:
Learning Relational Structure for Temporal Relation Extraction. StarAI@UAI 2012 - [c21]Pradyot Korupolu V. N., S. S. Manimaran, Balaraman Ravindran, Sriraam Natarajan:
Integrating Human Instructions and Reinforcement Learners: An SRL Approach. StarAI@UAI 2012 - [c20]Sriraam Natarajan, Phillip Odom, Saket Joshi, Tushar Khot, Kristian Kersting, Prasad Tadepalli:
Accelarating Imitation Learning in Relational Domains via Transfer by Initialization. StarAI@UAI 2012 - [c19]David Poole, David Buchman, Sriraam Natarajan, Kristian Kersting:
Aggregation and Population Growth: The Relational Logistic Regression and Markov Logic Cases. StarAI@UAI 2012 - [i2]Kristian Kersting, Babak Ahmadi, Sriraam Natarajan:
Counting Belief Propagation. CoRR abs/1205.2637 (2012) - 2011
- [c18]Sabareesh Subramaniam, Sriraam Natarajan, Alessandro Senes:
A machine learning based approach to improve sidechain optimization. BCB 2011: 478-480 - [c17]Tushar Khot, Sriraam Natarajan, Kristian Kersting, Jude W. Shavlik:
Learning Markov Logic Networks via Functional Gradient Boosting. ICDM 2011: 320-329 - [c16]Sriraam Natarajan, Saket Joshi, Prasad Tadepalli, Kristian Kersting, Jude W. Shavlik:
Imitation Learning in Relational Domains: A Functional-Gradient Boosting Approach. IJCAI 2011: 1414-1420 - 2010
- [j3]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:
Reports of the AAAI 2010 Conference Workshops. AI Mag. 31(4): 95-108 (2010) - [c15]Sriraam Natarajan, Tushar Khot, Daniel Lowd, Prasad Tadepalli, Kristian Kersting, Jude W. Shavlik:
Exploiting Causal Independence in Markov Logic Networks: Combining Undirected and Directed Models. StarAI@AAAI 2010 - [c14]Sriraam Natarajan, Gautam Kunapuli, Kshitij Judah, Prasad Tadepalli, Kristian Kersting, Jude W. Shavlik:
Multi-Agent Inverse Reinforcement Learning. ICMLA 2010: 395-400 - [c13]Trevor Walker, Ciaran O'Reilly, Gautam Kunapuli, Sriraam Natarajan, Richard Maclin, David Page, Jude W. Shavlik:
Automating the ILP Setup Task: Converting User Advice about Specific Examples into General Background Knowledge. ILP 2010: 253-268 - [c12]Sriraam Natarajan, Tushar Khot, Daniel Lowd, Prasad Tadepalli, Kristian Kersting, Jude W. Shavlik:
Exploiting Causal Independence in Markov Logic Networks: Combining Undirected and Directed Models. ECML/PKDD (2) 2010: 434-450 - [e1]Henry A. Kautz, Kristian Kersting, Sriraam Natarajan, David Poole:
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. 2010 [contents] - [r1]C. David Page Jr., Sriraam Natarajan:
Biomedical Informatics. Encyclopedia of Machine Learning 2010: 132
2000 – 2009
- 2009
- [c11]Sriraam Natarajan, Prasad Tadepalli, Gautam Kunapuli, Jude W. Shavlik:
Learning Parameters for Relational Probabilistic Models with Noisy-Or Combining Rule. ICMLA 2009: 141-146 - [c10]Jude W. Shavlik, Sriraam Natarajan:
Speeding Up Inference in Markov Logic Networks by Preprocessing to Reduce the Size of the Resulting Grounded Network. IJCAI 2009: 1951-1956 - [c9]Kristian Kersting, Babak Ahmadi, Sriraam Natarajan:
Counting Belief Propagation. UAI 2009: 277-284 - 2008
- [j2]Sriraam Natarajan, Prasad Tadepalli, Thomas G. Dietterich, Alan Fern:
Learning first-order probabilistic models with combining rules. Ann. Math. Artif. Intell. 54(1-3): 223-256 (2008) - [j1]Neville Mehta, Sriraam Natarajan, Prasad Tadepalli, Alan Fern:
Transfer in variable-reward hierarchical reinforcement learning. Mach. Learn. 73(3): 289-312 (2008) - [c8]Hung Hai Bui, Federico Cesari, Daniel Elenius, David N. Morley, Sriraam Natarajan, Shahin Saadati, Eric Yeh, Neil Yorke-Smith:
A context-aware personal desktop assistant. AAMAS (Demos) 2008: 1679-1680 - [c7]Sriraam Natarajan, Hung Hai Bui, Prasad Tadepalli, Kristian Kersting, Weng-Keen Wong:
Logical Hierarchical Hidden Markov Models for Modeling User Activities. ILP 2008: 192-209 - 2007
- [c6]Sriraam Natarajan, Kshitij Judah, Prasad Tadepalli, Alan Fern:
A Decision-Theoretic Model of Assistance - Evaluation, Extensions and Open Problems. AAAI Spring Symposium: Interaction Challenges for Intelligent Assistants 2007: 90-97 - [c5]Sriraam Natarajan, Kshitij Judah, Prasad Tadepalli, Alan Fern:
A Decision-Theoretic Model of Assistance - Evaluation, Extensions and Open Problems. Interaction Challenges for Intelligent Assistants 2007: 90-97 - [c4]Alan Fern, Sriraam Natarajan, Kshitij Judah, Prasad Tadepalli:
A Decision-Theoretic Model of Assistance. IJCAI 2007: 1879-1884 - [c3]Sriraam Natarajan, Prasad Tadepalli, Alan Fern:
A Relational Hierarchical Model for Decision-Theoretic Assistance. ILP 2007: 175-190 - [i1]Sriraam Natarajan, Prasad Tadepalli, Alan Fern:
Exploiting prior knowledge in Intelligent Assistants - Combining relational models with hierarchies. Probabilistic, Logical and Relational Learning - A Further Synthesis 2007 - 2005
- [c2]Sriraam Natarajan, Prasad Tadepalli:
Dynamic preferences in multi-criteria reinforcement learning. ICML 2005: 601-608 - [c1]Sriraam Natarajan, Prasad Tadepalli, Eric Altendorf, Thomas G. Dietterich, Alan Fern, Angelo C. Restificar:
Learning first-order probabilistic models with combining rules. ICML 2005: 609-616
Coauthor Index
[c117] [i34] [j28] [c112] [c111] [c109] [c106] [c104] [j26] [c95] [i26] [i22] [c89] [i16] [j20] [c88] [c86] [i12] [c85] [c81] [i7] [c78] [c75] [c71] [i6] [b2] [c69] [c66] [c63] [c60] [j15] [j13] [j12] [j11] [c55] [c52] [b1] [j9] [c51] [c50] [c46] [c45] [c44] [c42] [c40] [j8] [j7] [c39] [c38] [c37] [c35] [c33] [j4] [c26] [c24] [c20] [c19] [i2] [c17] [c16] [j3] [c15] [c14] [c12] [e1] [c9] [c7]
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