Machine Learning
Machine Learning is a very broad category that encompasses most of the work done in the AI Lab in one way or another. Essentially, any system that performs some sort of behavior (defined by a model or policy), and changes its behavior based on data (learns), can be considered a Machine Learning system.
Subareas:
People
     [Expand to show all 24][Minimize]
Bishal Barman Formerly affiliated Ph.D. Student bbarman [at] apple com
Samuel Barrett Ph.D. Alumni sbarrett [at] cs utexas edu
Yinon Bentor Formerly affiliated Ph.D. Student yinon [at] cs utexas edu
Shruti Bhosale Formerly affiliated Masters Student shruti [at] cs utexas edu
Rodolfo Corona Undergraduate Alumni rud721 [at] gmail com
Dan Garrette Ph.D. Alumni dhg [at] cs utexas edu
Lu Guo Masters Alumni guolu [at] cs utexas edu
Joohyun Kim Ph.D. Alumni scimitar [at] cs utexas edu
Hyeonseo Ku Masters Alumni yorq [at] cs utexas edu
Elad Liebman Ph.D. Student eladlieb [at] cs utexas edu
Angela S. Lin Masters Alumni alin [at] cs utexas edu
Angela S. Lin Masters Alumni alin [at] cs utexas edu
Tanvi S Motwani Masters Alumni tanvi [at] cs utexas edu
Sanmit Narvekar Ph.D. Student sanmit [at] cs utexas edu
Aishwarya Padmakumar Ph.D. Alumni aish [at] cs utexas edu
Sheena Panthaplackel Ph.D. Alumni spantha [at] cs utexas edu
Nazneen Rajani Ph.D. Alumni nrajani [at] cs utexas edu
Stephen Roller Ph.D. Alumni roller [at] cs utexas edu
Peter Stone Faculty pstone [at] cs utexas edu
Julia Strout Masters Alumni jstrout [at] utexas edu
Subhashini Venugopalan Ph.D. Alumni vsub [at] cs utexas edu
Heath Vinicombe Formerly affiliated Masters Student vini [at] cs utexas edu
Lemeng Wu Ph.D. Student lmwu [at] cs utexas edu
Harel Yedidsion Postdoctoral Fellow harel [at] cs utexas edu
Publications
     [Expand to show all 437][Minimize]
AutoInit: Analytic Signal-Preserving Weight Initialization for Neural Networks 2023
Garrett Bingham and Risto Miikkulainen, In Proceedings of the 37th AAAI Conference on Artificial Intelligence, 2023. (also arXiv:2021.08958).
Causal Policy Gradient for Whole-Body Mobile Manipulation 2023
Jiaheng Hu, Peter Stone, and Roberto Martin-Martin, In Robotics: Science and Systems (RSS2023), Daegu, Republic of Korea, July 2023.
D-Shape: Demonstration-Shaped Reinforcement Learning via Goal Conditioning 2023
Caroline Wang, Garrett Warnell, and Peter Stone, In Proceedings of the 22nd International Conference on Autonomous Agents and Multiagent Systems (AAMAS), London, UK, May 2023.
DM$^2$: Decentralized Multi-Agent Reinforcement Learning via Distribution Matching 2023
Caroline Wang, Ishan Durugkar, Elad Liebman, and Peter Stone, In Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI-23), Washington, D.C., February 2023.
Learning Perceptual Hallucination for Multi-Robot Navigation in Narrow Hallways 2023
Jinsoo Park, Xuesu Xiao, Garrett Warnell, Harel Yedidsion, and Peter Stone, In Proceedings of the 2023 IEEE International Conference on Robotics and Automation (ICRA 2023), London, England, May 2023.
MACTA: A Multi-agent Reinforcement Learning Approach for Cache Timing Attacks and Detection 2023
Jiaxun Cui, Xiaomeng Yang, Mulong Luo, Geunbae Lee, Peter Stone, Hsien-Hsin S. Lee, Benjamin Lee, G. Edward Suh, Wenjie Xiong, and Yuandong Tian, In The Eleventh International Conference on Learning Representations (ICLR), Kigali, Rwanda, May 2023.
Metric Residual Networks for Sample Efficient Goal-Conditioned Reinforcement Learning 2023
Bo Liu, Yihao Feng, Qiang Liu, and Peter Stone, In Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI), Washington, DC, US, February 2023.
Model-Based Meta Automatic Curriculum Learning 2023
Zifan Xu, Yulin Zhang, Shahaf S. Shperberg, Reuth Mirsky, Yuqian Jiang, Bo Liu, and Peter Stone, In The Second Conference on Lifelong Learning Agents (CoLLAs 2023), Montreal, Canada, August 2023.
Optimizing Neural Networks through Activation Function Discovery and Automatic Weight Initialization 2023
Garrett Bingham, PhD Thesis, Department of Computer Science, The University of Texas at Austin.
Reward (Mis)design for autonomous driving 2023
W. Bradley Knox, Alessandro Allievi, Holger Banzhaf, Felix Schmitt, and Peter Stone, Artificial Intelligence, Vol. 316 (2023).
The Perils of Trial-and-Error Reward Design: Misdesign through Overfitting and Invalid Task Specifications 2023
Serena Booth, W Bradley Knox, Julie Shah, Scott Niekum, Peter Stone, and Alessandro Allievi, In Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI), Washington, D.C., February 2023.
VaryNote: A Method to Automatically Vary the Number of Notes in Symbolic Music 2023
Juan M. Huerta, Bo Liu, and Peter Stone, In Bridge after the turmoil - The 16th International Symposium, CMMR 2023, Tokyo, Japan, November 13-17, 2023, Tokyo, Japan, November 2023.
A Rule-based Shield: Accumulating Safety Rules from Catastrophic Action Effects 2022
Shahaf Shperberg, Bo Liu, Allessandro Allievi, and Peter Stone, In Proceedings of the 1st Conference on Lifelong Learning Agents (CoLLAs), Montreal, Canada, August 2022.
Adversarial Imitation Learning from Video using a State Observer 2022
Haresh Karnan, Garrett Warnell, Faraz Torabi, and Peter Stone, In International Conference on Robotics and Automation, 2022, Philadelphia, Pennsylvania, May 2022.
APPL: Adaptive Planner Parameter Learning 2022
Xuesu Xiao, Zizhao Wang, Zifan Xu, Bo Liu, abd Gauraang Dhamankar, Anirudh Nair, Garrett Warnell, and Peter Stone, Robotics and Autonomous Systems (2022).
Autonomous Ground Navigation in Highly Constrained Spaces: Lessons Learned From the Benchmark Autonomous Robot Navigation Challenge at ICRA 2022 2022
Xuesu Xiao, Zifan Xu, Zizhao Wang, Yunlong Song, Garrett Warnell, Peter Stone, Tingnan Zhang, Shravan Ravi, Gary Wang, Haresh Karnan, Joydeep Biswas, Nicholas Mohammad, Lauren Bramblett, Rahul Peddi, Nicola Bezzo, Zhanteng Xie, and Philip Dames, IEEE Robotics and Automation Magazine (2022).
BOME! Bilevel Optimization Made Easy: A Simple First-Order Approach 2022
Bo Liu, Mao Ye, Stephen Wright, Peter Stone, and Qiang Liu, In Conference on Neural Information Processing Systems, 2022, New Orleans, LA, December 2022.
Bottom-Up Skill Discovery from Unsegmented Demonstrations for Long-Horizon Robot Manipulation 2022
Yifeng Zhu, Peter Stone, and Yuke Zhu, IEEE Robotics and Automation Letters (2022).
Causal Dynamics Learning for Task-Independent State Abstraction 2022
Zizhao Wang, Xuesu Xiao, Zifan Xu, Yuke Zhu, and Peter Stone, In roceedings of the 39th International Conference on Machine Learning (ICML2022), Baltimore, USA, July 2022.
Continual Learning and Private Unlearning 2022
Bo Liu, Qiang Liu, and Peter Stone, In Proceedings of the 1st Conference on Lifelong Learning Agents (CoLLAs), Montreal, Canada, August 2022.
Effective Mutation Rate Adaptation through Group Elite Selection 2022
Akarsh Kumar, Bo Liu, Risto Miikkulainen, and Peter Stone, In Proceedings of the Genetic and Evolutionary Computation Conference, Boston, United States, July 2022.
Learning a Robust Multiagent Driving Policy for Traffic Congestion Reduction 2022
Yulin Zhang, William Macke, Jiaxun Cui, Daniel Urieli, and Peter Stone, In Proceedings of the Adaptive and Learning Agents Workshop (ALA), Auckland, NZ, May 2022.
Model-Based Meta Automatic Curriculum Learning 2022
Zifan Xu, Yulin Zhang, Shahaf S. Shperberg, Reuth Mirsky, Yulin Zhan, Yuqian Jiang, Bo Liu, and Peter Stone, In Decision Awareness in Reinforcement Learning (DARL) workshop t the +39th International Conference on Machine Learning (ICML), Baltimore, Maryland, USA, July 2022.
Motion Planning and Control for Mobile Robot Navigation Using Machine Learning: a Survey 2022
Xuesu Xiao, Bo Liu, Garrett Warnell, and Peter Stone, Autonomous Robots (2022).
Socially CompliAnt Navigation Dataset (SCAND): A Large-Scale Dataset Of Demonstrations For Social Navigation 2022
Haresh Karnan, Anirudh Nair, Xuesu Xiao, Garrett Warnell, Soren Pirk, Alexander Toshev, Justin Hart, Joydeep Biswas, and Peter Stone, Robotics and Automation Letters (RA-L), 2022 (2022).
Task Factorization in Curriculum Learning 2022
Reuth Mirsky, Shahaf S. Shperberg, Yulin Zhang, Zifan Xu, Yuqian Jiang, Jiaxun Cui, and Peter Stone, In Decision Awareness in Reinforcement Learning (DARL) workshop t the 39th International Conference on Machine Learning (ICML), Baltimore, Maryland, USA, July 2022.
VI-IKD: High-Speed Accurate Off-Road Navigation using Learned Visual-Inertial Inverse Kinodynamics 2022
Haresh Karnan, Kavan Sikand, Pranav Atreya, Sadegh Rabiee, Xuesu Xiao, Garrett Warnell, Peter Stone, and Joydeep Biswas, In International Conference on Intelligent Robots and Systems, 2022, Kyoto, Japan, October 2022.
VIOLA: Imitation Learning for Vision-Based Manipulation with Object Proposal Priors 2022
Yifeng Zhu, Abhishek Joshi, Peter Stone, and Yuke Zhu, In Proceedings of the 6th Conference on Robot Learning (CoRL 2022), Auckland, New Zealand, January 2022.
VOILA: Visual-Observation-Only Imitation Learning for Autonomous Navigation 2022
Haresh Karnan, Garrett Warnell, Xuesu Xiao, and Peter Stone, In International Conference on Robotics and Automation, 2022, Philadelphia, Pennsylvania, May 2022.
A Scavenger Hunt for Service Robots 2021
Harel Yedidsion, Jennifer Suriadinata, Zifan Xu, Stefan Debruyn, and Peter Stone, In Proceedings of the 2021 International Conference on Robotics and Automation (ICRA 2021), Xi'an China, May 2021.
Adversarial Intrinsic Motivation for Reinforcement Learning 2021
Ishan Durugkar, Mauricio Tec, Scott Niekum, and Peter Stone, In Proceedings of the 35th International Conference on Neural Information Processing Systems (NeurIPS 2021), Sydney, Australia, December 2021.
Agile Robot Navigation through Hallucinated Learning and Sober Deployment 2021
Xuesu Xiao, Bo Liu, and Peter Stone, In Proceedings of the 2021 IEEE International Conference on Robotics and Automation (ICRA 2021), Xi'an, China, June 2021.
APPLE: Adaptive Planner Parameter Learning From Evaluative Feedback 2021
Zizhao Wang, Xuesu Xiao, Bo Liu, Garrett Warnell, and Peter Stone, {IEEE} Robotics and Automation Letters, presented at International Conference on Intelligent Robots and Systems ({IROS}) (2021).
APPLI: Adaptive Planner Parameter Learning From Interventions 2021
Zizhao Wang, Xuesu Xiao, Bo Liu, Garrett Warnell, and Peter Stone, In Proceedings of the International Conference on Robotics and Automation (ICRA 2021), Xi'an, China, May 2021.
Capturing Skill State in Curriculum Learning for Human Skill Acquisition 2021
Keya Ghonasgi, Reuth Mirsky, Sanmit Narvekar, Bharath Masetty, Adrian M. Haith, Peter Stone, and Ashish D. Deshpande, In International Conference on Intelligent Robots and Systems (IROS), Virtual, September 2021.
Coach-Player Multi-Agent Reinforcement Learning for Dynamic Team Composition 2021
Bo Liu, Qiang Liu, Peter Stone, Animesh Garg, Yuke Zhu, and Animashree Anandkumar, In Proceedings of the 38th International Conference on Machine Learning, PMLR 139, 2021 (ICML), Vienna, Austria, July 2021.
Conflict-Averse Gradient Descent for Multi-task learning 2021
Bo Liu, Xingchao Liu, Xiaojie Jin, Peter Stone, and Qiang Liu, In Conference on Neural Information Processing Systems, 2021, Virtual, December 2021.
DEALIO: Data-Efficient Adversarial Learning for Imitation from Observation 2021
Faraz Torabi, Garrett Warnell, and Peter Stone, In Proceedings of The IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Prague, Czech Republic, September 2021.
From Agile Ground to Aerial Navigation: Learning from Learned Hallucination 2021
Zizhao Wang, Xuesu Xiao, Alexander J Nettekoven, Kadhiravan Umasankar, Anika Singh, Sriram Bommakanti, Ufuk Topcu, and Peter Stone, In Proceedings of the International Conference on Intelligent Robots and Systems (IROS 2021), Prague, Czech Republic, October 2021.
Grounded Action Transformation for Sim-to-Real Reinforcement Learning 2021
Josiah P.Hanna, Siddharth Desai, Haresh Karnan, Garrett Warnell, and Peter Stone, Special Issue on Reinforcement Learning for Real Life, Machine Learning, 2021 (2021).
Importance Sampling in Reinforcement Learning with an Estimated Behavior Policy 2021
Josiah P. Hanna, Scott Niekum, and Peter Stone, Machine Learning (MLJ), Vol. 110, 6 (2021), pp. 1267–1317.
Is the Cerebellum a Model-Based Reinforcement Learning Agent? 2021
Bharath Masetty, Reuth Mirsky, Ashish D. Deshpande, Michael Mauk, and Peter Stone, In Adaptive and Learning Agents Workshop at AAMAS, Virtual, May 2021.
Learning Inverse Kinodynamics for Accurate High-Speed Off-Road Navigation on Unstructured Terrain 2021
Xuesu Xiao, Joydeep Biswas, and Peter Stone, In Opportunities and Challenges with Autonomous Racing Workshop at the 2021 IEEE International Conference on Robotics and Automation (ICRA 2021), Xi'an, China, June 2021.
Learning Inverse Kinodynamics for Accurate High-Speed Off-Road Navigation on Unstructured Terrain 2021
Xuesu Xiao, Joydeep Biswas, and Peter Stone, IEEE Robotics and Automation Letters (2021).
Lucid Dreaming for Experience Replay: Refreshing Past States with the Current Policy 2021
Yunshu Du, Garrett Warnell, Assefaw Gebremedhin, Peter Stone, and Matthew E. Taylor, Neural Computing and Applications (2021).
Machine versus Human Attention in Deep Reinforcement Learning Tasks 2021
Sihang Guo, Ruohan Zhang, Bo Liu, Yifeng Zhu, Mary Hayhoe, Dana Ballard, and Peter Stone, In Conference on Neural Information Processing Systems, 2021, Virtual, December 2021.
RAIL: A modular framework for Reinforcement-learning-based Adversarial Imitation Learning 2021
Eddy Hudson, Garrett Warnell, and Peter Stone, In Autonomous Robots and Multirobot Systems Workshop at the 20th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2021), London, UK, May 2021.
Recent Advances in Leveraging Human Guidance for Sequential Decision-Making Tasks 2021
Ruohan Zhang, Faraz Torabi, Garrett Warnell, and Peter Stone, Autonomous Agents and Multi-Agent Systems (2021).
Temporal-Logic-Based Reward Shaping for Continuing Reinforcement Learning Tasks 2021
Yuqian Jiang, Suda Bharadwaj, Bo Wu, Rishi Shah, Ufuk Topcu, and Peter Stone, In Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI 2021), Virtual Conference, February 2021.
Toward Agile Maneuvers in Highly Constrained Spaces: Learning from Hallucination 2021
Xuesu Xiao, Bo Liu, Garrett Warnell, and Peter Stone, IEEE Robotics and Automation Letters (2021).
Agents teaching agents: a survey on inter-agent transfer learning 2020
Felipe Leno Da Silva, Garrett Warnell, Anna Helena Reali Costa, and Peter Stone, Autonomous Agents and Multi-Agent Systems (2020).
An Imitation from Observation Approach to Transfer Learning with Dynamics Mismatch 2020
Siddarth Desai, Ishan Durugkar, Haresh Karnan, Garrett Warnell, Josiah Hanna, and Peter Stone, In Proceedings of the 34th International Conference on Neural Information Processing Systems (NeurIPS 2020), Virtual, December 2020.
APPLD: Adaptive Planner Parameter Learning from Demonstration 2020
Xuesu Xiao, Bo Liu, Garrett Warnell, Jonathan Fink, and Peter Stone, No other information
Balancing Individual Preferences and Shared Objectives in Multiagent Reinforcement Learning 2020
Ishan Durugkar, Elad Liebman, and Peter Stone, Proceedings of the 29th International Joint Conference on Artificial Intelligence (IJCAI 2020) (2020).
Deep R-Learning for Continual Area Sweeping 2020
Rishi Shah, Yuqian Jiang, Justin Hart, and Peter Stone, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2020) (2020).
Firefly Neural Architecture Descent: a General Approach for Growing Neural Networks 2020
Lemeng Wu, Bo Liu, Peter Stone, and Qiang Liu, In Advances in Neural Information Processing Systems 34 (2020), Vancouver, Canada, December 2020.
On Sampling Error in Batch Action-Value Prediction Algorithms 2020
Brahma S. Pavse, Josiah P. Hanna, Ishan Durugkar, and Peter Stone, In In the Offline Reinforcement Learning Workshop at Neural Information Processing Systems (NeurIPS), December 2020., Remote (Virtual Conference), December 2020.
Policy Evaluation in Continuous MDPs with Efficient Kernelized Gradient Temporal Difference 2020
Alec Koppel, Garrett Warnell, Ethan Stump, Peter Stone, and Alejandro Ribeiro, No other information
Reducing Sampling Error in Batch Temporal Difference Learning 2020
Brahma Pavse, Ishan Durugkar, Josiah Hanna, and Peter Stone, In Proceedings of the 37th International Conference on Machine Learning (ICML), Vienna, Austria (Virtual Conference), July 2020.
Reinforced Grounded Action Transformation for Sim-to-Real Transfer 2020
Haresh Karnan, Siddharth Desai, Josiah P. Hanna, Garrett Warnell, and Peter Stone, In IEEE/RSJ International Conference on Intelligent Robots and Systems(IROS 2020), October 2020.
RIDM: Reinforced Inverse Dynamics Modeling for Learning from a Single Observed Demonstration 2020
Brahma Pavse, Faraz Torabi, Josiah Hanna, Garrett Warnell, and Peter Stone, IEEE Robotics and Automation Letters, presented at International Conference on Intelligent Robots and Systems (IROS) (2020).
Stochastic Grounded Action Transformation for Robot Learning in Simulation 2020
Siddharth Desai, Haresh Karnan, Josiah P. Hanna, Garrett Warnell, and Peter Stone, In IEEE/RSJ International Conference on Intelligent Robots and Systems(IROS 2020), Las Vegas, NV, USA, October 2020.
The EMPATHIC Framework for Task Learning from Implicit Human Feedback 2020
Yuchen Cui, Qiping Zhang, Alessandro Allievi, Peter Stone, Scott Niekum, and W. Bradley Knox, In Proceedings of the 4th Conference on Robot Learning (CoRL 2020), Cambridge MA, USA, November 2020.
The Traveling Observer Model: Multi-task Learning Through Spatial Variable Embeddings 2020
Elliot Meyerson and Risto Miikkulainen, arxiv:2010.02354 (2020).
Building Self-Play Curricula Online by Playing with Expert Agents in Adversarial Games 2019
Felipe Leno Da Silva, Anna Helena Reali Costa, and Peter Stone, In Proceedings of the 8th Brazilian Conference on Intelligent Systems (BRACIS), Salvador, Bahia, Brazil, October 2019.
Generative Adversarial Imitation from Observation 2019
Faraz Torabi, Garrett Warnell, and Peter Stone, Imitation, Intent, and Interaction (I3) Workshop at ICML 2019 (2019).
Imitation Learning from Video by Leveraging Proprioception 2019
Faraz Torabi, Garrett Warnell, and Peter Stone, In Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI), Macao, China, August 2019.
Importance Sampling Policy Evaluation with an Estimated Behavior Policy 2019
Josiah Hanna, Scott Niekum, and Peter Stone, In Proceedings of the 36th International Conference on Machine Learning (ICML), Long Beach, California, U.S.A., June 2019.
Recent Advances in Imitation Learning from Observation 2019
Faraz Torabi, Garrett Warnell, and Peter Stone, Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI) (2019).
Reducing Sampling Error in Policy Gradient Learning 2019
Josiah Hanna and Peter Stone, In Proceedings of the 18th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), Montreal, Canada, May 2019.
RIDM: Reinforced Inverse Dynamics Modeling for Learning from a Single Observed Demonstration 2019
Brahma S. Pavse, Faraz Torabi, Josiah Hanna, Garrett Warnell, and Peter Stone, No other information
Sample-efficient Adversarial Imitation Learning from Observation 2019
Faraz Torabi, Garrett Warnell, and Peter Stone, No other information
The right music at the right time: adaptive personalized playlists based on sequence modeling 2019
Elad Liebman, Maytal Saar-Tsechansky, and Peter Stone Peter Stone, Management Information Systems Quarterly, Vol. 43, 3 (2019), pp. 765--786.
Multi-modal Predicate Identification using Dynamically Learned Robot Controllers 2018
Saeid Amiri, Suhua Wei, Shiqi Zhang, Jivko Sinapov, Jesse Thomason, and Peter Stone, In Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI-18), Stockholm, Sweden, July 2018.
Pseudo-task Augmentation: From Deep Multitask Learning to Intratask Sharing---and Back 2018
Elliot Meyerson, Risto Miikkulainen, In Proceedings of the 35th International Conference on Machine Learning, pp. 739-748 2018.
Variety Wins: Soccer-Playing Robots and Infant Walking 2018
Ori Ossmy, Justine E. Hoch, Patrick MacAlpine, Shohan Hasan, Peter Stone, and Karen E. Adolph, Frontiers in Neurorobotics, Vol. 12 (2018), pp. 19.
On the Impact of Music on Decision Making in Cooperative Tasks 2018
Elad Liebman, Corey N. White, and Peter Stone, In 19th International Society for Music Information retrieval Conference (ISMIR), Paris, France, September 2018.
Stacking With Auxiliary Features 2017
Nazneen Fatema Rajani and Raymond J. Mooney, In Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI-17), pp. 2634-2640, Melbourne, Australia 2017.
Adaptation of Surrogate Tasks for Bipedal Walk Optimization 2016
Patrick MacAlpine, Elad Liebman, and Peter Stone, In GECCO Surrogate-Assisted Evolutionary Optimisation (SAEOpt) Workshop, Denver, Colorado, USA, July 2016.
Continuously Improving Natural Language Understanding for Robotic Systems through Semantic Parsing, Dialog, and Multi-modal Perception 2016
Jesse Thomason, PhD proposal, Department of Computer Science, The University of Texas at Austin.
Deep Compositional Captioning: Describing Novel Object Categories without Paired Training Data 2016
Lisa Anne Hendricks, Subhashini Venugopalan, Marcus Rohrbach, Raymond Mooney, Kate Saenko, and Trevor Darrell, In Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR-16), pp. 1--10 2016.
Improving LSTM-based Video Description with Linguistic Knowledge Mined from Text 2016
Subhashini Venugopalan, Lisa Anne Hendricks, Raymond Mooney, and Kate Saenko, In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (EMNLP-16), pp. 1961--1966, Austin, Texas 2016.
Machine Learning Capabilities of a Simulated Cerebellum 2016
Matthew Hausknecht, Wen-Ke Li, Michael Mauk, and Peter Stone, IEEE Transactions on Neural Networks and Learning Systems (2016).
Bin-Based Estimation of the Amount of Effort for Embedded Software Development Projects with Support Vector Machines 2016
Kazunori Iwata, Elad Liebman, Peter Stone, Toyoshiro Nakashima, Yoshiyuki Anan, and Naohiro Ishii, In {C}omputer and {I}nformation {S}cience , Roger Lee (Eds.), Berlin 2016. Springer Verlag.
Impact of Music on Decision Making in Quantitative Tasks 2016
Elad Liebman, Peter Stone, and Corey N. White, In 17th International Society for Music Information retrieval Conference (ISMIR), NYC, USA, August 2016.
UT Austin Villa RoboCup 3D Simulation Base Code Release 2016
Patrick MacAlpine and Peter Stone, In {R}obo{C}up 2016: Robot Soccer World Cup {XX}, Sven Behnke and Daniel D. Lee and Sanem Sariel and Raymond Sheh (Eds.), Berlin 2016. Springer Verlag.
A Supertag-Context Model for Weakly-Supervised CCG Parser Learning 2015
Dan Garrette, Chris Dyer, Jason Baldridge, and Noah A. Smith , In Proceedings of the 2015 Conference on Computational Natural Language Learning (CoNLL-2015), pp. 22--31, Beijing, China 2015.
Autonomous Trading in Modern Electricity Markets 2015
Daniel Urieli, PhD Thesis, Department of Computer Sciences, The University of Texas at Austin. Code and binaries available at: http://www.cs.utexas.edu/~urieli/thesis.
How Music Alters Decision Making: Impact of Music Stimuli on Emotional Classification 2015
Elad Liebman, Peter Stone, and Corey N. White, In 16th International Society for Music Information Retrieval Conference (ISMIR), Malaga, Spain, October 2015.
Inducing Grammars from Linguistic Universals and Realistic Amounts of Supervision 2015
Dan Garrette, PhD Thesis, Department of Computer Science, The University of Texas at Austin.
Knowledge Base Population using Stacked Ensembles of Information Extractors 2015
Vidhoon Viswanathan, Masters Thesis, Department of Computer Science, The University of Texas at Austin.
Learning Inter-Task Transferability in the Absence of Target Task Samples 2015
Jivko Sinapov, Sanmit Narvekar, Matteo Leonetti, and Peter Stone, In Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS), Istanbul, Turkey, May 2015.
Natural Language Video Description using Deep Recurrent Neural Networks 2015
Subhashini Venugopalan, PhD proposal, Department of Computer Science, The University of Texas at Austin.
Representative Selection in Nonmetric Datasets 2015
Elad Liebman, Benny Chor, and Peter Stone, Applied Artificial Intelligence, Vol. 29, 8 (2015), pp. 807--838.
Statistical Script Learning with Recurrent Neural Nets 2015
Karl Pichotta, PhD proposal, Department of Computer Science, The University of Texas at Austin.
Unsupervised Code-Switching for Multilingual Historical Document Transcription 2015
Dan Garrette, Hannah Alpert-Abrams, Taylor Berg-Kirkpatrick, and Dan Klein , In Proceedings the 2015 Conference of the North American Chapter of the Association for Computational Linguistics -- Human Language Technologies (NAACL HLT 2015), pp. 1036--1041, Denver, Colora...
Weakly-Supervised Grammar-Informed Bayesian CCG Parser Learning 2015
Dan Garrette, Chris Dyer, Jason Baldridge, Noah A. Smith, In Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI-15), Austin, TX, January 2015.
Active Multitask Learning Using Both Latent and Supervised Shared Topics 2014
Ayan Acharya, Raymond J. Mooney, and Joydeep Ghosh, In Proceedings of the 2014 SIAM International Conference on Data Mining (SDM14), Philadelphia, Pennsylvania, April 2014.
Infinite-Word Topic Models for Digital Media 2014
Austin Waters, PhD Thesis, Department of Computer Science, The University of Texas at Austin.
Natural Language Semantics using Probabilistic Logic 2014
I. Beltagy, PhD proposal, Department of Computer Science, The University of Texas at Austin.
TacTex'13: A Champion Adaptive Power Trading Agent 2014
Daniel Urieli and Peter Stone, In Proceedings of the Twenty-Eighth Conference on Artificial Intelligence (AAAI 2014), July 2014.
Conditional Random Fields via Univariate Exponential Families 2013
Eunho Yang, Pradeep Ravikumar, Genevera Allen and Zhandong Liu, In Advances in Neural Information Processing Systems (NIPS) 2013.
Dirty Statistical Models 2013
Eunho Yang and Pradeep Ravikumar, Advances in Neural Information Processing Systems (NIPS) (2013).
Generating Natural-Language Video Descriptions Using Text-Mined Knowledge 2013
Niveda Krishnamoorthy, Girish Malkarnenkar, Raymond J. Mooney, Kate Saenko, Sergio Guadarrama, In Proceedings of the 27th AAAI Conference on Artificial Intelligence (AAAI-2013), pp. 541--547, July 2013.
Generating Natural-Language Video Descriptions Using Text-Mined Knowledge 2013
Niveda Krishnamoorthy, Girish Malkarnenkar, Raymond J. Mooney, Kate Saenko, Sergio Guadarrama, Proceedings of the NAACL HLT Workshop on Vision and Language (WVL '13) (2013), pp. 10--19.
Grounded Language Learning Models for Ambiguous Supervision 2013
Joo Hyun Kim, PhD Thesis, Department of Computer Science, University of Texas at Austin.
Learning a Part-of-Speech Tagger from Two Hours of Annotation 2013
Dan Garrette, Jason Baldridge , Proceedings of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT-13) (2013), pp. 138--147.
Model-Selection for Non-Parametric Function Approximation in Continuous Control Problems: A Case Study in a Smart Energy System 2013
Daniel Urieli and Peter Stone, In Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases (ECML-PKDD'13), September 2013.
On Poisson Graphical Models 2013
Eunho Yang, Pradeep Ravikumar, Genevera Allen and Zhandong Liu, In Advances in Neural Information Processing Systems (NIPS) 2013.
On Robust Estimation of High Dimensional Generalized Linear Models 2013
Eunho Yang, Ambuj Tewari and Pradeep Ravikumar, In International Joint Conference on Artificial Intelligence (IJCAI) 2013.
Real-World Semi-Supervised Learning of POS-Taggers for Low-Resource Languages 2013
Dan Garrette, Jason Mielens, and Jason Baldridge , Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (ACL-2013) (2013), pp. 583--592.
A Divide-and-Conquer Procedure for Sparse Inverse Covariance Estimation 2012
Cho-Jui Hsieh, Inderjit Dhillon, Pradeep Ravikumar, and Arindam Banerjee, NIPS (2012).
A unified framework for high-dimensional analysis of M-estimators with decomposable regularizers 2012
S. Negahban, P. Ravikumar, M. J. Wainwright, and B. Yu, Statistical Science (2012).
Bayesian Logic Programs for Plan Recognition and Machine Reading 2012
Sindhu Raghavan, PhD Thesis, Department of Computer Science, University of Texas at Austin. 170.
Fast Online Lexicon Learning for Grounded Language Acquisition 2012
David L. Chen, Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (ACL-2012) (2012), pp. 430--439.
Generative Models of Grounded Language Learning with Ambiguous Supervision 2012
Joohyun Kim, Technical Report, PhD proposal, Department of Computer Science, The University of Texas at Austin.
Graphical Models via Generalized Linear Models 2012
Eunho Yang, Pradeep Ravikumar, Genevera Allen, and Zhandong Liu, NIPS (2012).
High-dimensional Sparse Inverse Covariance Estimation using Greedy Methods 2012
Christopher Johnson, Ali Jalali, and Pradeep Ravikumar, In International Conference on AI and Statistics (AISTATS) 2012.
Improving Video Activity Recognition using Object Recognition and Text Mining 2012
Tanvi S. Motwani and Raymond J. Mooney, In Proceedings of the 20th European Conference on Artificial Intelligence (ECAI-2012), pp. 600--605, August 2012.
Information-theoretic lower bounds on the oracle complexity of convex optimization 2012
A. Agarwal, P. Bartlett, P. Ravikumar, and M. Wainwright, IEEE Transactions on Information Theory, Vol. 58, 5 (2012), pp. 3235-3249.
Learning from Human-Generated Reward 2012
W. Bradley Knox, No other information
Learning Language from Ambiguous Perceptual Context 2012
David L. Chen, PhD Thesis, Department of Computer Science, University of Texas at Austin. 196.
Learning to "Read Between the Lines" using Bayesian Logic Programs 2012
Sindhu Raghavan, Raymond J. Mooney, and Hyeonseo Ku, Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (ACL-2012) (2012), pp. 349--358.
PAC Subset Selection in Stochastioc Multi-armed Bandits 2012
Shivaram Kalyanakrishnan, Ambuj Tewari, Peter Auer, and Peter Stone, In In proceedings of the 29th International Conference on Machine Learning (ICML 2012), June-July 2012.
Perturbation based Large Margin Approach for Ranking 2012
Eunho Yang, Ambuj Tewari and Pradeep Ravikumar, In International Conference on Artificial Intelligence and Statistics (AISTATS) 2012.
Review Quality Aware Collaborative Filtering 2012
Sindhu Raghavan, Suriya Ganasekar, and Joydeep Ghosh, In Sixth ACM Conference on Recommender Systems (RecSys 2012), pp. 123--130, September 2012.
TEXPLORE: Temporal Difference Reinforcement Learning for Robots and Time-Constrained Domains. 2012
Todd Hester, PhD Thesis, The University of Texas at Austin. Code available at: http://www.ros.org/wiki/rl-texplore-ros-pkg.
Type-Supervised Hidden Markov Models for Part-of-Speech Tagging with Incomplete Tag Dictionaries 2012
Dan Garrette and Jason Baldridge, In Proceedings of the Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL 2012), pp. 821--831, Jeju, Korea, July 2012.
Unsupervised PCFG Induction for Grounded Language Learning with Highly Ambiguous Supervision 2012
Joohyun Kim and Raymond J. Mooney, In Proceedings of the Conference on Empirical Methods in Natural Language Processing and Natural Language Learning (EMNLP-CoNLL '12), pp. 433--444, Jeju Island, Korea, July 2012.
Using a million cell simulation of the cerebellum: Network scaling and task generality 2012
Wen-Ke Li, Matthew J. Hausknecht, Peter Stone, and Michael D. Mauk, Neural Networks (2012).
A Real-Time Model-Based Reinforcement Learning Architecture for Robot Control 2011
Todd Hester, Michael Quinlan, and Peter Stone, No other information
Adaptive Trading Agent Strategies Using Market Experience 2011
David Merrill Pardoe, No other information
Empowerment for Continuous Agent-Environment Systems 2011
Tobias Jung, Daniel Polani, and Peter Stone, Adaptive Behavior, Vol. 19, 1 (2011), pp. 16-39.
Encoding and Decoding V1 fMRI Responses to Natural Images with Sparse Nonparametric Models 2011
V. Vu, P. Ravikumar, T. Naselaris, K. Kay, J. Gallant, and B. Yu, Annals of Applied Statistics (2011), pp. 1159-1182.
Greedy Algorithms for Structurally Constrained High Dimensional Problems 2011
A. Tewari, P. Ravikumar, and I. Dhillon, In Neural Information Processing Systems 2011.
High-dimensional covariance estimation by minimizing l1-penalized log-determinant divergence 2011
P. Ravikumar, M. J. Wainwright, G. Raskutti, and B. Yu, Electronic Journal of Statistics, Vol. 5 (2011), pp. 935-980.
Improving the Accuracy and Scalability of Discriminative Learning Methods for Markov Logic Networks 2011
Tuyen N. Huynh, PhD Thesis, Department of Computer Science, University of Texas at Austin.
159 pages.
Learning to Interpret Natural Language Navigation Instructions from Observations 2011
David L. Chen and Raymond J. Mooney, Proceedings of the 25th AAAI Conference on Artificial Intelligence (AAAI-2011) (2011), pp. 859-865.
Nearest Neighbor based Greedy Coordinate Descent 2011
I. Dhillon, P. Ravikumar, and A. Tewari, In Neural Information Processing Systems 2011.
On Learning Discrete Graphical Models using Greedy Methods 2011
Ali Jalali, Christopher Johnson, and Pradeep Ravikumar, In Neural Information Processing Systems 2011.
On Learning Discrete Graphical Models using Group-Sparse Regularization 2011
A. Jalali, P. Ravikumar, V. Vasuki, and S. Sanghavi, In International Conference on AI and Statistics (AISTATS) 2011.
On NDCG Consistency of Listwise Ranking Methods 2011
Pradeep Ravikumar, Ambuj Tewari and Eunho Yang, International Conference on AI and Statistics (AISTATS) (2011).
On the Use of Variational Inference for Learning Discrete Graphical Models 2011
Eunho Yang and Pradeep Ravikumar, In International Conference on Machine learning (ICML) 2011.
Panning for Gold: Finding Relevant Semantic Content for Grounded Language Learning 2011
David L. Chen and Raymond J. Mooney, In Proceedings of Symposium on Machine Learning in Speech and Language Processing (MLSLP 2011), June 2011.
Sparse Inverse Covariance Matrix Estimation Using Quadratic Approximation 2011
C.-J. Hsieh, M. Sustik, I. Dhillon, and P. Ravikumar, In Neural Information Processing Systems 2011.
A Dirty Model for Multi-task Learning 2010
A. Jalali, P. Ravikumar, S. Sanghavi, and C. Ruan, In Neural Information Processing Systems 2010.
Boosting for Regression Transfer 2010
David Pardoe and Peter Stone, In Proceedings of the 27th International Conference on Machine Learning (ICML 2010), June 2010.
Efficient Selection of Multiple Bandit Arms: Theory and Practice 2010
Shivaram Kalyanakrishnan and Peter Stone, In Proceedings of the 27th International Conference on Machine Learning (ICML 2010) 2010.
Gaussian processes for sample efficient reinforcement learning with RMAX-like exploration 2010
Tobias Jung and Peter Stone, In Proceedings of the European Conference on Machine Learning, September 2010.
Information-theoretic lower bounds on the oracle complexity of sparse convex optimization 2010
A. Agarwal, P. Bartlett, P. Ravikumar, and M. Wainwright, In International Workshop on Optimization for Machine Learning (OPT) 2010.
Learning Powerful Kicks on the Aibo ERS-7: The Quest for a Striker 2010
Matthew Hausknecht and Peter Stone, In Robocup International Symposium 2010.
Message-passing for graph-structured linear programs: proximal methods and rounding schemes 2010
P. Ravikumar, A. Agarwal, and M. J. Wainwright, Journal of Machine Learning Research (JMLR), Vol. 11 (2010), pp. 1043-1080.
Real Time Targeted Exploration in Large Domains 2010
Todd Hester and Peter Stone, In Proceedings of the Ninth International Conference on Development and Learning (ICDL 2010), 2010 (Eds.), August 2010.
A unified framework for high-dimensional analysis of M-estimators with decomposable regularizers 2009
S. Negahban, P. Ravikumar, M. J. Wainwright, and B. Yu, In Neural Information Processing Systems 2009.
Activity Retrieval in Closed Captioned Videos 2009
Sonal Gupta, Masters Thesis, Department of Computer Sciences, University of Texas at Austin. 64 pages.
Compositional Models for Reinforcement Learning 2009
Nicholas K. Jong and Peter Stone, In The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, September 2009.
Information-theoretic lower bounds on the oracle complexity of convex optimization 2009
A. Agarwal, P. Bartlett, P. Ravikumar, and M. Wainwright, In Neural Information Processing Systems 2009.
Learning a Compositional Semantic Parser using an Existing Syntactic Parser 2009
Ruifang Ge and Raymond J. Mooney, In Joint Conference of the 47th Annual Meeting of the Association for Computational Linguistics and the 4th International Joint Conference on Natural Language Processing of the Asian Federation of ...
Learning to Disambiguate Search Queries from Short Sessions 2009
Lilyana Mihalkova and Raymond Mooney, In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), Part 2, pp. 111--127, Bled, Slovenia, September 2009.
Learning with Markov Logic Networks: Transfer Learning, Structure Learning, and an Application to Web Query Disambiguation 2009
Lilyana Mihalkova, PhD Thesis, Department of Computer Sciences, University of Texas at Austin. 176 pages.
Max-Margin Weight Learning for Markov Logic Networks 2009
Tuyen N. Huynh and Raymond J. Mooney, In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), Part 1, pp. 564--579, Bled, Slovenia, September 2009.
Max-Margin Weight Learning for Markov Logic Networks 2009
Tuyen N. Huynh and Raymond J. Mooney, In Proceedings of the International Workshop on Statistical Relational Learning (SRL-09), Leuven, Belgium, July 2009.
Probabilistic Abduction using Markov Logic Networks 2009
Rohit J. Kate and Raymond J. Mooney, In Proceedings of the IJCAI-09 Workshop on Plan, Activity, and Intent Recognition (PAIR-09), Pasadena, CA, July 2009.
Semi-supervised graph clustering: a kernel approach 2009
Brian Kulis, Sugato Basu, Inderjit Dhillon, and Raymond Mooney, Machine Learning Journal, Vol. 74, 1 (2009), pp. 1-22.
Sparse Additive Models 2009
P. Ravikumar, J. Lafferty, H. Liu, and L. Wasserman, Journal of the Royal Statistical Society: Series B (Statistical Methodology) (JRSSB), Vol. 71, 5 (2009), pp. 1009-1030.
Speeding up Inference In Statistical Relational Learning by Clustering Similar Query Literals 2009
Lilyana Mihalkova and Matthew Richardson, In Proceedings of the 19th International Conference on Inductive Logic Programming (ILP-09), Leuven, Belgium, July 2009.
Transfer Learning from Minimal Target Data by Mapping across Relational Domains 2009
Lilyana Mihalkova and Raymond Mooney, In Proceedings of the 21st International Joint Conference on Artificial Intelligence (IJCAI-09), pp. 1163--1168, Pasadena, CA, July 2009.
Using Closed Captions to Train Activity Recognizers that Improve Video Retrieval 2009
Sonal Gupta and Raymond Mooney, In Proceedings of the CVPR-09 Workshop on Visual and Contextual Learning from Annotated Images and Videos (VCL), Miami, FL, June 2009.
A Dependency-based Word Subsequence Kernel 2008
Rohit J. Kate, In Proceedings of the conference on Empirical Methods in Natural Language Processing (EMNLP-2008), pp. 400--409, Waikiki, Honolulu, Hawaii, October 2008.
A Neural Network-Based Approach to Robot Motion Control 2008
Uli Grasemann, Daniel Stronger, and Peter Stone, In RoboCup-2007: Robot Soccer World Cup XI, Ubbo Visser and Fernando Ribeiro and Takeshi Ohashi and Frank Dellaert (Eds.), Vol. 5001, pp. 480-87, Berlin 2008. Springer Verlag.
Discriminative Structure and Parameter Learning for Markov Logic Networks 2008
Tuyen N. Huynh and Raymond J. Mooney, In Proceedings of the 25th International Conference on Machine Learning (ICML), Helsinki, Finland, July 2008.
Hierarchical Model-Based Reinforcement Learning: Rmax + MAXQ 2008
Nicholas K. Jong and Peter Stone, In Proceedings of the Twenty-Fifth International Conference on Machine Learning, July 2008.
Integrating declarative knowledge: Issues, Algorithms and Future Work 2008
Doo Soon Kim and Bruce Porter, In AAAI Spring Symposium on Semantic Scientific Knowledge Integration 2008.
Learning to Connect Language and Perception 2008
Raymond J. Mooney, In Proceedings of the 23rd AAAI Conference on Artificial Intelligence (AAAI), pp. 1598--1601, Chicago, IL, July 2008. Senior Member Paper.
Learning to Sportscast: A Test of Grounded Language Acquisition 2008
David L. Chen and Raymond J. Mooney, In Proceedings of the 25th International Conference on Machine Learning (ICML), Helsinki, Finland, July 2008.
Message-passing for graph-structured linear programs: Proximal projections, convergence and rounding schemes 2008
P. Ravikumar, A. Agarwal, and M. J. Wainwright, In International Conference on Machine learning (ICML) 2008.
Model selection in Gaussian graphical models: High-dimensional consistency of l1-regularized MLE 2008
P. Ravikumar, M. J. Wainwright, G. Raskutti, and B. Yu, In Neural Information Processing Systems 2008.
Nonparametric sparse hierarchical models describe V1 fmri responses to natural images 2008
P. Ravikumar, V. Vu, B. Yu, T. Naselaris, K. Kay, and J. Gallant, In Neural Information Processing Systems 2008.
Online Kernel Selection for Bayesian Reinforcement Learning 2008
Joseph Reisinger, Peter Stone, and Risto Miikkulainen, In Proceedings of the Twenty-Fifth International Conference on Machine Learning, July 2008.
Online Multiagent Learning against Memory Bounded Adversaries 2008
Doran Chakraborty and Peter Stone, In Machine Learning and Knowledge Discovery in Databases, Vol. 5212, pp. 211-26, September 2008.
Polynomial Regression with Automated Degree: A Function Approximator for Autonomous Agents 2008
Daniel Stronger and Peter Stone, International Journal on Artificial Intelligence Tools, Vol. 17, 1 (2008), pp. 159-174.
Search Query Disambiguation from Short Sessions 2008
Lilyana Mihalkova and Raymond Mooney, In Beyond Search: Computational Intelligence for the Web Workshop at NIPS 2008.
The Utility of Temporal Abstraction in Reinforcement Learning 2008
Nicholas K. Jong, Todd Hester, and Peter Stone, In The Seventh International Joint Conference on Autonomous Agents and Multiagent Systems, May 2008.
Transfer Learning by Mapping with Minimal Target Data 2008
Lilyana Mihalkova and Raymond J. Mooney, Proceedings of the AAAI-08 Workshop on Transfer Learning For Complex Tasks (2008).
Transforming Meaning Representation Grammars to Improve Semantic Parsing 2008
Rohit J. Kate, In Proceedings of the Twelfth Conference on Computational Natural Language Learning (CoNLL-2008), pp. 33--40, Manchester, UK, August 2008.
Watch, Listen & Learn: Co-training on Captioned Images and Videos 2008
Sonal Gupta, Joohyun Kim, Kristen Grauman and Raymond Mooney, In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD), pp. 457--472, Antwerp Belgium, September 2008.
Action Selection for Illumination Invariant Color Learning 2007
Mohan Sridharan and Peter Stone, In The IEEE International Conference on Intelligent Robots and Systems (IROS) 2007.
Adapting Price Predictions in TAC SCM 2007
David Pardoe and Peter Stone, In AAMAS 2007 Workshop on Agent Mediated Electronic Commerce 2007.
Approximate inference, structure learning and feature estimation in Markov random fields 2007
P. Ravikumar, Technical Report CMU-ML-07-115, Ph.D. Thesis, Carnegie Mellon University (2007).
Autonomous Return on Investment Analysis of Additional Processing Resources 2007
Jonathan Wildstrom, Peter Stone, and Emmett Witchel, In 2007 Workshop on Adaptive Methods in Autonomic Computing Systems, June 2007.
Bottom-Up Learning of Markov Logic Network Structure 2007
Lilyana Mihalkova and Raymond J. Mooney, In Proceedings of 24th International Conference on Machine Learning (ICML-2007), Corvallis, OR, June 2007.
Color Learning on a Mobile Robot: Towards Full Autonomy under Changing Illumination 2007
Mohan Sridharan and Peter Stone, In The 20th International Joint Conference on Artificial Intelligence, pp. 2212-2217, January 2007.
Extracting Relations from Text: From Word Sequences to Dependency Paths 2007
Razvan C. Bunescu and Raymond J. Mooney, In Natural Language Processing and Text Mining, A. Kao and S. Poteet (Eds.), pp. 29-44, Berlin 2007. Springer Verlag.
Generation by Inverting a Semantic Parser That Uses Statistical Machine Translation 2007
Yuk Wah Wong and Raymond J. Mooney, In Proceedings of Human Language Technologies: The Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT-07), pp. 172-179, Rochester, NY 2007.
Improving Learning of Markov Logic Networks using Transfer and Bottom-Up Induction 2007
Lilyana Mihalkova, Technical Report UT-AI-TR-07-341, Artificial Intelligence Lab, University of Texas at Austin.
Learning and Multiagent Reasoning for Autonomous Agents 2007
Peter Stone, In The 20th International Joint Conference on Artificial Intelligence, pp. 13-30, January 2007.
Learning for Information Extraction: From Named Entity Recognition and Disambiguation To Relation Extraction 2007
Razvan Constantin Bunescu, PhD Thesis, Department of Computer Sciences, University of Texas at Austin. 150 pages. Also as Technical Report AI07-345, Artificial Intelligence Lab, University of Texas at Austin, August 2007.
Learning for Semantic Parsing 2007
Raymond J. Mooney, In Computational Linguistics and Intelligent Text Processing: Proceedings of the 8th International Conference (CICLing 2007), A. Gelbukh (Eds.), pp. 311--324, Mexico City, Mexico, February 2007...
Learning for Semantic Parsing and Natural Language Generation Using Statistical Machine Translation Techniques 2007
Yuk Wah Wong, PhD Thesis, Department of Computer Sciences, University of Texas at Austin. 188 pages. Also appears as Technical Report AI07-343, Artificial Intelligence Lab, University of Texas at Austin, August 200...
Learning for Semantic Parsing with Kernels under Various Forms of Supervision 2007
Rohit J. Kate, PhD Thesis, Department of Computer Sciences, University of Texas at Austin. 159 pages.
Learning Language Semantics from Ambiguous Supervision 2007
Rohit J. Kate and Raymond J. Mooney, In Proceedings of the 22nd Conference on Artificial Intelligence (AAAI-07), pp. 895-900, Vancouver, Canada, July 2007.
Learning Synchronous Grammars for Semantic Parsing with Lambda Calculus 2007
Yuk Wah Wong and Raymond J. Mooney, In Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics (ACL-2007), Prague, Czech Republic, June 2007.
Learning to Extract Relations from the Web using Minimal Supervision 2007
Razvan C. Bunescu and Raymond J. Mooney, In Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics (ACL'07), Prague, Czech Republic, June 2007.
Machine Learning for On-Line Hardware Reconfiguration 2007
Jonathan Wildstrom, Peter Stone, Emmett Witchel, and Mike Dahlin, In The 20th International Joint Conference on Artificial Intelligence, pp. 1113-1118, January 2007.
Mapping and Revising Markov Logic Networks for Transfer Learning 2007
Lilyana Mihalkova, Tuyen N. Huynh, Raymond J. Mooney, In Proceedings of the Twenty-Second Conference on Artificial Intelligence (AAAI-07), pp. 608-614, Vancouver, BC, July 2007.
Multiagent learning is not the answer. It is the question 2007
Peter Stone, Artificial Intelligence, Vol. 171 (2007), pp. 402-05.
Multiple Instance Learning for Sparse Positive Bags 2007
Razvan C. Bunescu and Raymond J. Mooney, In Proceedings of the 24th Annual International Conference on Machine Learning (ICML-2007), Corvallis, OR, June 2007.
Planning Actions to Enable Color Learning on a Mobile Robot 2007
Mohan Sridharan and Peter Stone, International Journal of Information and Systems Sciences, Vol. 3, 3 (2007), pp. 510-25.
Semi-Supervised Learning for Semantic Parsing using Support Vector Machines 2007
Rohit J. Kate and Raymond J. Mooney, In Proceedings of the Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics, Short Papers (NAACL/HLT-2007), pp. 81--84, Rochester...
SpAM: sparse additive models 2007
P. Ravikumar, J. Lafferty, H. Liu, and L. Wasserman, In Neural Information Processing Systems 2007.
Statistical Relational Learning for Natural Language Information Extraction 2007
Razvan Bunescu and Raymond J. Mooney, In Introduction to Statistical Relational Learning, L. Getoor and B. Taskar (Eds.), pp. 535-552, Cambridge, MA 2007. MIT Press.
Structure Based Color Learning on a Mobile Robot under Changing Illumination 2007
Mohan Sridharan and Peter Stone, Autonomous Robots, Vol. 23, 3 (2007), pp. 161-182.
Adapting to Workload Changes Through On-The-Fly Reconfiguration 2006
Jonathan Wildstrom, Peter Stone, Emmett Witchel, and Mike Dahlin, Technical Report UT-AI-TR-06-330, The University of Texas at Austin, Department of Computer Sciences, AI Laboratory.
Adaptive Blocking: Learning to Scale Up Record Linkage 2006
Mikhail Bilenko, Beena Kamath, Raymond J. Mooney, In Proceedings of the Sixth IEEE International Conference on Data Mining (ICDM-06), pp. 87--96, Hong Kong, December 2006.
Autonomous Planned Color Learning on a Mobile Robot Without Labeled Data 2006
Mohan Sridharan and Peter Stone, In The Ninth International Conference on Control, Automation, Robotics and Vision, December 2006.
Discriminative Reranking for Semantic Parsing 2006
Ruifang Ge and Raymond J. Mooney, In Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics (COLING/ACL-06), Sydney, Australia, Jul...
Fast and Effective Worm Fingerprinting via Machine Learning 2006
Stewart Yang, Jianping Song, Harish Rajamani, Taewon Cho, Yin Zhang and Raymond Mooney, Technical Report AI-06-335, Artificial Intelligence Lab, The University of Texas at Austin. This is a longer version of our ICAC-2006 paper.
Fast and Effective Worm Fingerprinting via Machine Learning 2006
Stewart Yang, Jianping Song, Harish Rajamani, Taewon Cho, Yin Zhang and Raymond Mooney, In Proceedings of the 3rd IEEE International Conference on Autonomic Computing (ICAC-2006), Dublin, Ireland, June 2006. Poster Session.
High-dimensional graphical model selection using l1-regularized logistic regression 2006
M. J. Wainwright, P. Ravikumar, and J. Lafferty, In Neural Information Processing Systems 2006.
Integrating Co-occurrence Statistics with Information Extraction for Robust Retrieval of Protein Interactions from Medline 2006
Razvan Bunescu, Raymond Mooney, Arun Ramani and Edward Marcotte, In Proceedings of the HLT-NAACL Workshop on Linking Natural Language Processing and Biology (BioNLP'06), pp. 49-56, New York, NY, June 2006.
Learnable Similarity Functions and Their Application to Record Linkage and Clustering 2006
Mikhail Bilenko, PhD Thesis, Department of Computer Sciences, University of Texas at Austin. 136 pages.
Learning for Semantic Parsing with Statistical Machine Translation 2006
Yuk Wah Wong and Raymond J. Mooney, In Proceedings of Human Language Technology Conference / North American Chapter of the Association for Computational Linguistics Annual Meeting (HLT-NAACL-06), pp. 439-446, New York City, NY 20...
Learning Language from Perceptual Context: A Challenge Problem for AI 2006
Raymond J. Mooney, In Proceedings of the 2006 AAAI Fellows Symposium, Boston, MA, July 2006.
Learning Semantic Parsers Using Statistical Syntactic Parsing Techniques 2006
Ruifang Ge, unpublished. Doctoral Dissertation Proposal, University of Texas at Austin" , year="2006.
Multiagent Traffic Management: Opportunities for Multiagent Learning 2006
Kurt Dresner and Peter Stone, In LAMAS 2005, K. Tuyls et al. (Eds.), Vol. 3898, pp. 129-138, Berlin 2006. Springer Verlag.
Preconditioner approximations for probabilistic graphical models 2006
P. Ravikumar and J. Lafferty, In Neural Information Processing Systems, pp. 1113-1120 2006.
Probabilistic Semi-Supervised Clustering with Constraints 2006
Sugato Basu, Mikhail Bilenko, Arindam Banerjee and Raymond J. Mooney, In Semi-Supervised Learning, O. Chapelle and B. Sch{"{o}}lkopf and A. Zien (Eds.), Cambridge, MA 2006. MIT Press.
Quadratic programming relaxations for metric labeling and Markov random field MAP estimation 2006
P. Ravikumar and J. Lafferty, In International Conference on Machine learning (ICML), pp. 737-744 2006.
Subsequence Kernels for Relation Extraction 2006
Razvan Bunescu and Raymond J. Mooney, In Advances in Neural Information Processing Systems, Vol. 18: Proceedings of the 2005 Conference (NIPS), Y. Weiss, B. Schoelkopf, J. Platt (Eds.) 2006.
Transfer Learning with Markov Logic Networks 2006
Lilyana Mihalkova and Raymond Mooney, In Proceedings of the ICML-06 Workshop on Structural Knowledge Transfer for Machine Learning, Pittsburgh, PA, June 2006.
Using Active Relocation to Aid Reinforcement Learning 2006
Lilyana Mihalkova and Raymond Mooney, In Prodeedings of the 19th International FLAIRS Conference (FLAIRS-2006), pp. 580-585, Melbourne Beach, FL, May 2006.
Using Encyclopedic Knowledge for Named Entity Disambiguation 2006
Razvan Bunescu and Marius Pasca, In Proceesings of the 11th Conference of the European Chapter of the Association for Computational Linguistics (EACL-06), pp. 9-16, Trento, Italy 2006.
Using String-Kernels for Learning Semantic Parsers 2006
Rohit J. Kate and Raymond J. Mooney, In ACL 2006: Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the ACL, pp. 913-920, Morristown, NJ, USA 2006. Association for Computa...
A Kernel-based Approach to Learning Semantic Parsers 2005
Rohit J. Kate, unpublished. Doctoral Dissertation Proposal, University of Texas at Austin.
A Shortest Path Dependency Kernel for Relation Extraction 2005
R. C. Bunescu, and Raymond J. Mooney, In Proceedings of the Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP-05), pp. 724-731, Vancouver, BC, October 2005.
A Statistical Semantic Parser that Integrates Syntax and Semantics 2005
Ruifang Ge and Raymond J. Mooney, In Proceedings of CoNLL-2005, Ann Arbor, Michigan, June 2005.
Active Learning for Probability Estimation using Jensen-Shannon Divergence 2005
P. Melville, S. M. Yang, M. Saar-Tsechansky and Raymond J. Mooney, In Proceedings of the 16th European Conference on Machine Learning, pp. 268--279, Porto, Portugal, October 2005.
Adaptive Product Normalization: Using Online Learning for Record Linkage in Comparison Shopping 2005
Mikhail Bilenko, Sugato Basu, and Mehran Sahami, In Proceedings of the 5th International Conference on Data Mining (ICDM-2005), pp. 58--65, Houston, TX, November 2005.
Alignments and String Similarity in Information Integration: A Random Field Approach 2005
Mikhail Bilenko and Raymond J. Mooney, In Proceedings of the 2005 Dagstuhl Seminar on Machine Learning for the Semantic Web, Dagstuhl, Germany, February 2005.
An Expected Utility Approach to Active Feature-value Acquisition 2005
P. Melville, M. Saar-Tsechansky, F. Provost and Raymond J. Mooney, In Proceedings of the International Conference on Data Mining, pp. 745-748, Houston, TX, November 2005.
Autonomous Color Learning on a Mobile Robot 2005
Mohan Sridharan and Peter Stone, In Proceedings of the Twentieth National Conference on Artificial Intelligence, July 2005.
Combining Bias and Variance Reduction Techniques for Regression 2005
Y. L. Suen, P. Melville and Raymond J. Mooney, In Proceedings of the 16th European Conference on Machine Learning, pp. 741-749, Porto, Portugal, October 2005.
Combining Bias and Variance Reduction Techniques for Regression 2005
Yuk Lai Suen, Prem Melville and Raymond J. Mooney, Technical Report UT-AI-TR-05-321, University of Texas at Austin. www.cs.utexas.edu/~ml/publication.
Comments: The Sensitivity of Economic Statistics to Coding Errors in Personal Identifiers 2005
W. W. Cohen, S. Fienberg, and P. Ravikumar, Journal of Business and Economic Statistics, Vol. 23, 2 (2005), pp. 160-162.
Comparative Experiments on Learning Information Extractors for Proteins and their Interactions 2005
Razvan Bunescu, Ruifang Ge, Rohit J. Kate, Edward M. Marcotte, Raymond J. Mooney, Arun Kumar Ramani, and Yuk Wah Wong, Artificial Intelligence in Medicine (special issue on Summarization and Information Extraction from Medical Documents), 2 (2005), pp. 139-155.
Consolidating the Set of Known Human Protein-Protein Interactions in Preparation for Large-Scale Mapping of the Human Interactome 2005
A.K. Ramani, R.C. Bunescu, Raymond J. Mooney and E.M. Marcotte, Genome Biology, Vol. 6, 5 (2005), pp. r40.
Creating Diverse Ensemble Classifiers to Reduce Supervision 2005
Prem Melville, PhD Thesis, Department of Computer Sciences, University of Texas at Austin. 141 pages. Technical Report TR-05-49.
Economical Active Feature-value Acquisition through Expected Utility Estimation 2005
P. Melville, M. Saar-Tsechansky, F. Provost and Raymond J. Mooney, In Proceedings of the KDD-05 Workshop on Utility-Based Data Mining, pp. 10-16, Chicago, IL, August 2005.
Explaining Recommendations: Satisfaction vs. Promotion 2005
Mustafa Bilgic and Raymond J. Mooney, In Proceedings of Beyond Personalization 2005: A Workshop on the Next Stage of Recommender Systems Research at the 2005 International Conference on Intelligent User Interfaces, San Diego, CA, J...
Learning for Collective Information Extraction 2005
Razvan C. Bunescu, Technical Report TR-05-02, Department of Computer Sciences, University of Texas at Austin. Ph.D. proposal.
Learning for Semantic Parsing Using Statistical Machine Translation Techniques 2005
Yuk Wah Wong, unpublished. Doctoral Dissertation Proposal, University of Texas at Austin.
Learning to Transform Natural to Formal Languages 2005
Rohit J. Kate, Yuk Wah Wong and Raymond J. Mooney, In Proceedings of the Twentieth National Conference on Artificial Intelligence (AAAI-05), pp. 1062-1068, Pittsburgh, PA, July 2005.
Mining Knowledge from Text Using Information Extraction 2005
Raymond J. Mooney and R. Bunescu, SIGKDD Explorations (special issue on Text Mining and Natural Language Processing), Vol. 7, 1 (2005), pp. 3-10.
Model-based Overlapping Clustering 2005
A. Banerjee, C. Krumpelman, S. Basu, Raymond J. Mooney and Joydeep Ghosh, In Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-05) 2005.
Real-Time Vision on a Mobile Robot Platform 2005
Mohan Sridharan and Peter Stone, In IEEE/RSJ International Conference on Intelligent Robots and Systems, August 2005.
Semi-supervised Clustering: Probabilistic Models, Algorithms and Experiments 2005
Sugato Basu, PhD Thesis, University of Texas at Austin.
Semi-supervised Graph Clustering: A Kernel Approach 2005
B. Kulis, S. Basu, I. Dhillon and Raymond J. Mooney, In Proceedings of the 22nd International Conference on Machine Learning, pp. 457--464, Bonn, Germany, August 2005. (Distinguished Student Paper Award).
Towards Illumination Invariance in the Legged League 2005
Mohan Sridharan and Peter Stone, In RoboCup-2004: Robot Soccer World Cup VIII, Daniele Nardi and Martin Riedmiller and Claude Sammut (Eds.), Vol. 3276, pp. 196-208, Berlin 2005. Springer Verlag.
Towards Self-Configuring Hardware for Distributed Computer Systems 2005
Jonathan Wildstrom, Peter Stone, E. Witchel, Raymond Mooney and M. Dahlin, In The Second International Conference on Autonomic Computing, pp. 241-249, June 2005.
Using Biomedical Literature Mining to Consolidate the Set of Known Human Protein-Protein Interactions 2005
A. Ramani, E. Marcotte, R. Bunescu and Raymond J. Mooney, In Proceedings of the ISMB/ACL-05 Workshop of the BioLINK SIG: Linking Literature, Information and Knowledge for Biology, Detroit, MI, June 2005.
A Comparison of Inference Techniques for Semi-supervised Clustering with Hidden Markov Random Fields 2004
Mikhail Bilenko and Sugato Basu, In Proceedings of the ICML-2004 Workshop on Statistical Relational Learning and its Connections to Other Fields (SRL-2004), Banff, Canada, July 2004.
A Hierarchical Graphical Model for Record Linkage 2004
P. Ravikumar and W. W. Cohen, In Uncertainty in Artificial Intelligence (UAI), pp. 454-461 2004.
A Probabilistic Framework for Semi-Supervised Clustering 2004
Sugato Basu, Mikhail Bilenko, and Raymond J. Mooney, In Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2004), pp. 59-68, Seattle, WA, August 2004.
A Secure Protocol for Computing String Distance Metrics 2004
P. Ravikumar, W. W. Cohen, and S. E. Fienberg, In In IEEE International Conference on Data Mining (ICDM) 04, Workshop on Privacy and Security Aspects of Data Mining 2004.
Active Feature-Value Acquisition for Classifier Induction 2004
Prem Melville, Maytal Saar-Tsechansky, Foster Provost, and Raymond J. Mooney, Technical Report UT-AI-TR-04-311, Artificial Intelligence Lab, University of Texas at Austin.
Active Feature-Value Acquisition for Classifier Induction 2004
Prem Melville, Maytal Saar-Tsechansky, Foster Provost, and Raymond J. Mooney, In Proceedings of the Fourth IEEE International Conference on Data Mining (ICDM-2004), pp. 483-486, Brighton, UK, November 2004.
Active Semi-Supervision for Pairwise Constrained Clustering 2004
Sugato Basu, Arindam Banerjee, and Raymond J. Mooney, In Proceedings of the 2004 SIAM International Conference on Data Mining (SDM-04), April 2004.
Collective Information Extraction with Relational Markov Networks 2004
Razvan Bunescu and Raymond J. Mooney, In Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL-04), pp. 439-446, Barcelona, Spain, July 2004.
Creating Diversity in Ensembles Using Artificial Data 2004
Prem Melville and Raymond J. Mooney, Journal of Information Fusion: Special Issue on Diversity in Multi Classifier Systems, Vol. 6, 1 (2004), pp. 99-111.
Diverse Ensembles for Active Learning 2004
Prem Melville and Raymond J. Mooney, In Proceedings of 21st International Conference on Machine Learning (ICML-2004), pp. 584-591, Banff, Canada, July 2004.
Experiments on Ensembles with Missing and Noisy Data 2004
Prem Melville, Nishit Shah, Lilyana Mihalkova, and Raymond J. Mooney, In {Lecture Notes in Computer Science:} Proceedings of the Fifth International Workshop on Multi Classifier Systems (MCS-2004), F. Roli, J. Kittler, and T. Windeatt (Eds.), Vol. 3077, pp. 293-3...
Explanation for Recommender Systems: Satisfaction vs. Promotion 2004
Mustafa Bilgic, unpublished. Undergraduate Honor Thesis, Department of Computer Sciences, University of Texas at Austin.
Guiding a Reinforcement Learner with Natural Language Advice: Initial Results in RoboCup Soccer 2004
Gregory Kuhlmann, Peter Stone, Raymond J. Mooney, and Jude W. Shavlik, In The AAAI-2004 Workshop on Supervisory Control of Learning and Adaptive Systems, July 2004.
Integrating Constraints and Metric Learning in Semi-Supervised Clustering 2004
Mikhail Bilenko, Sugato Basu, and Raymond J. Mooney, In Proceedings of 21st International Conference on Machine Learning (ICML-2004), pp. 81-88, Banff, Canada, July 2004.
Learnable Similarity Functions and Their Applications to Clustering and Record Linkage 2004
Mikhail Bilenko, In Proceedings of the Ninth AAAI/SIGART Doctoral Consortium, pp. 981--982, San Jose, CA, July 2004.
Learning Semantic Parsers: An Important But Under-Studied Problem 2004
Raymond J. Mooney, In Papers from the AAAI 2004 Spring Symposium on Language Learning: An Interdisciplinary Perspective, pp. 39--44, Stanford, CA, March 2004.
Learning Transformation Rules for Semantic Parsing 2004
Rohit J. Kate, Yuk Wah Wong, Ruifang Ge, and Raymond J. Mooney, unpublished. Unpublished Technical Report.
Relational Data Mining with Inductive Logic Programming for Link Discovery 2004
Raymond J. Mooney, P. Melville, L. R. Tang, J. Shavlik, I. Dutra and D. Page, Data Mining: Next Generation Challenges and Future DirectionsKargupta, H., Joshi, A., Sivakumar K., and Yesha, Y. (Eds.) (2004), pp. 239--254. AAAI Press.
Relational Markov Networks for Collective Information Extraction 2004
Razvan Bunescu and Raymond J. Mooney, In Proceedings of the ICML-04 Workshop on Statistical Relational Learning and its Connections to Other Fields, Banff, Alberta, July 2004.
Semi-supervised Clustering with Limited Background Knowledge 2004
Sugato Basu, In Proceedings of the Ninth AAAI/SIGART Doctoral Consortium, pp. 979--980, San Jose, CA, July 2004.
Semi-supervised Clustering: Learning with Limited User Feedback 2004
Sugato Basu, Technical Report, Cornell University.
Semisupervised Clustering for Intelligent User Management 2004
Sugato Basu, Mikhail Bilenko, and Raymond J. Mooney, In Proceedings of the IBM Austin Center for Advanced Studies 5th Annual Austin CAS Conference, Austin, TX, February 2004.
Text Mining with Information Extraction 2004
Un Yong Nahm, PhD Thesis, Department of Computer Sciences, University of Texas at Austin. 217 pages. Also appears as Technical Report UT-AI-TR-04-311.
Using Soft-Matching Mined Rules to Improve Information Extraction 2004
Un Yong Nahm and Raymond J. Mooney, In Proceedings of the AAAI-2004 Workshop on Adaptive Text Extraction and Mining (ATEM-2004), pp. 27-32, San Jose, CA, July 2004.
Variational Chernoff bounds for graphical models 2004
P. Ravikumar and J. Lafferty, In Uncertainty in Artificial Intelligence (UAI), pp. 462-469 2004.
A Comparison of String Distance Metrics for Name-Matching Tasks 2003
W. W. Cohen, P. Ravikumar, and S. Fienberg, In In International Joint Conference on Artificial Intelligence (IJCAI) 18, Workshop on Information Integration on the Web 2003.
A Comparison of String Metrics for Matching Names and Records 2003
W. W. Cohen, P. Ravikumar, and S. Fienberg, In International Conference on Knowledge Discovery and Data Mining (KDD) 09, Workshop on Data Cleaning, Record Linkage, and Object Consolidation 2003.
Acquiring Word-Meaning Mappings for Natural Language Interfaces 2003
Cynthia A. Thompson and Raymond J. Mooney, Journal of Artificial Intelligence Research, Vol. 18 (2003), pp. 1-44.
Adaptive Duplicate Detection Using Learnable String Similarity Measures 2003
Mikhail Bilenko and Raymond J. Mooney, In Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2003), pp. 39-48, Washington, DC, August 2003.
Adaptive Name-Matching in Information Integration 2003
Mikhail Bilenko, William W. Cohen, Stephen Fienberg, Raymond J. Mooney, and Pradeep Ravikumar, IEEE Intelligent Systems, Vol. 18, 5 (2003), pp. 16-23.
Bottom-Up Relational Learning of Pattern Matching Rules for Information Extraction 2003
Mary Elaine Califf and Raymond J. Mooney, Journal of Machine Learning Research (2003), pp. 177-210.
Comparing and Unifying Search-Based and Similarity-Based Approaches to Semi-Supervised Clustering 2003
Sugato Basu, Mikhail Bilenko, and Raymond J. Mooney, In Proceedings of the ICML-2003 Workshop on the Continuum from Labeled to Unlabeled Data in Machine Learning and Data Mining, pp. 42-49, Washington, DC 2003.
Constructing Diverse Classifier Ensembles Using Artificial Training Examples 2003
Prem Melville and Raymond J. Mooney, In Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence (IJCAI-2003), pp. 505-510, Acapulco, Mexico, August 2003.
Creating Diverse Ensemble Classifiers 2003
Prem Melville, Technical Report UT-AI-TR-03-306, Department of Computer Sciences, University of Texas at Austin. Ph.D. proposal.
Decision-Theoretic Bidding Based on Learned Density Models in Simultaneous, Interacting Auctions 2003
Peter Stone, Robert E. Schapire, Michael L. Littman, J'anos A. Csirik, and David McAllester, Journal of Artificial Intelligence Research, Vol. 19 (2003), pp. 209-242.
Employing Trainable String Similarity Metrics for Information Integration 2003
Mikhail Bilenko and Raymond J. Mooney, In Proceedings of the IJCAI-03 Workshop on Information Integration on the Web, pp. 67-72, Acapulco, Mexico, August 2003.
Integrating Top-down and Bottom-up Approaches in Inductive Logic Programming: Applications in Natural Language Processing and Relational Data Mining 2003
Lappoon R. Tang, PhD Thesis, Department of Computer Sciences, University of Texas.
Learnable Similarity Functions and Their Applications to Record Linkage and Clustering 2003
Mikhail Bilenko, unpublished. Doctoral Dissertation Proposal, University of Texas at Austin.
Learning to Extract Proteins and their Interactions from Medline Abstracts 2003
Razvan Bunescu, Ruifang Ge, Rohit J. Kate, Raymond J. Mooney, Yuk Wah Wong, Edward M. Marcotte, and Arun Kumar Ramani, In Proceedings of the ICML-03 Workshop on Machine Learning in Bioinformatics, pp. 46-53, Washington, DC, August 2003.
Machine Learning 2003
Raymond J. Mooney, , McGraw-Hill, New York, NY 2003. McGraw-Hill.
On Evaluation and Training-Set Construction for Duplicate Detection 2003
Mikhail Bilenko and Raymond J. Mooney, In Proceedings of the KDD-03 Workshop on Data Cleaning, Record Linkage, and Object Consolidation, pp. 7-12, Washington, DC, August 2003.
Scaling Up ILP to Large Examples: Results on Link Discovery for Counter-Terrorism 2003
Lappoon R. Tang, Raymond J. Mooney, and Prem Melville, In Proceedings of the KDD-2003 Workshop on Multi-Relational Data Mining (MRDM-2003), pp. 107--121, Washington DC, August 2003.
Text Mining with Information Extraction 2003
Raymond J. Mooney and Un Yong Nahm, In Multilingualism and Electronic Language Management: Proceedings of the 4th International MIDP Colloquium, W. Daelemans and T. du Plessis and C. Snyman and L. Teck (Eds.), pp. 141-160, Bloemf...
Content-Boosted Collaborative Filtering for Improved Recommendations 2002
Prem Melville, Raymond J. Mooney, and Ramadass Nagarajan, In Proceedings of the Eighteenth National Conference on Artificial Intelligence (AAAI-02), pp. 187-192, Edmonton, Alberta 2002.
Extracting Gene and Protein Names from Biomedical Abstracts 2002
Razvan Bunescu, Ruifang Ge, Raymond J. Mooney, Edward Marcotte, and Arun Kumar Ramani, unpublished. Unpublished Technical Note.
Learning to Combine Trained Distance Metrics for Duplicate Detection in Databases 2002
Mikhail Bilenko and Raymond J. Mooney, Technical Report AI 02-296, Artificial Intelligence Laboratory, University of Texas at Austin.
Mining Soft-Matching Association Rules 2002
Un Yong Nahm and Raymond J. Mooney, In Proceedings of the Eleventh International Conference on Information and Knowledge Management (CIKM-2002), pp. 681-683, McLean, VA, November 2002.
Modeling Auction Price Uncertainty Using Boosting-based Conditional Density Estimation 2002
Robert E. Schapire, Peter Stone, David McAllester, Michael L. Littman, and J'anos A. Csirik, In Proceedings of the Nineteenth International Conference on Machine Learning 2002.
Property-Based Feature Engineering and Selection 2002
Noppadon Kamolvilassatian, Masters Thesis, Department of Computer Sciences, University of Texas at Austin. 85 pages.
Relational Data Mining with Inductive Logic Programming for Link Discovery 2002
Raymond J. Mooney, Prem Melville, Lappoon R. Tang, Jude Shavlik, Inês de Castro Dutra, David Page, and Vítor Santos Costa, In Proceedings of the National Science Foundation Workshop on Next Generation Data Mining, Baltimore, MD, November 2002.
Semi-supervised Clustering by Seeding 2002
Sugato Basu, Arindam Banerjee, and Raymond J. Mooney, In Proceedings of 19th International Conference on Machine Learning (ICML-2002), pp. 19-26 2002.
Text Mining with Information Extraction 2002
Un Yong Nahm and Raymond J. Mooney, In Proceedings of the AAAI 2002 Spring Symposium on Mining Answers from Texts and Knowledge Bases, pp. 60-67, Stanford, CA, March 2002.
Two Approaches to Handling Noisy Variation in Text Mining 2002
Un Yong Nahm, Mikhail Bilenko, and Raymond J. Mooney, In Papers from the Nineteenth International Conference on Machine Learning (ICML-2002) Workshop on Text Learning, pp. 18-27, Sydney, Australia, July 2002.
ATTac-2000: An Adaptive Autonomous Bidding Agent 2001
Peter Stone, Michael L. Littman, Satinder Singh, and Michael Kearns, Journal of Artificial Intelligence Research, Vol. 15 (2001), pp. 189-206.
Content-Boosted Collaborative Filtering 2001
Prem Melville, Raymond J. Mooney, and Ramadass Nagarajan, In Proceedings of the SIGIR-2001 Workshop on Recommender Systems, New Orleans, LA, September 2001.
ELIXIR: A Library for Writing Wrappers in Java 2001
Edward Wild, Undergraduate Honor Thesis, Department of Computer Sciences, University of Texas at Austin.
Evaluating the Novelty of Text-Mined Rules using Lexical Knowledge 2001
Sugato Basu, Raymond J. Mooney, Krupakar V. Pasupuleti, and Joydeep Ghosh, In Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2001), pp. 233-239, San Francisco, CA 2001.
Mining Soft-Matching Rules from Textual Data 2001
Un Yong Nahm and Raymond J. Mooney, In Proceedings of the 18th International Joint Conference on Artificial Intelligence 2001.
Text Mining with Information Extraction 2001
Un Yong Nahm, unpublished. Ph.D. proposal, Department of Computer Sciences, University of Texas at Austin.
Using Lexical Knowlege to Evaluate the Novelty of Rules Mined from Text 2001
Sugato Basu, Raymond J. Mooney, Krupakar V. Pasupuleti, and Joydeep Ghosh, In Proceedings of NAACL 2001 Workshop on WordNet and Other Lexical Resources: Applications, Extensions and Customizations, pp. 144--149, Pittsburg, PA, June 2001.
Using Multiple Clause Constructors in Inductive Logic Programming for Semantic Parsing 2001
Lappoon R. Tang and Raymond J. Mooney, In Proceedings of the 12th European Conference on Machine Learning, pp. 466-477, Freiburg, Germany 2001.
A Mutually Beneficial Integration of Data Mining and Information Extraction 2000
Un Yong Nahm and Raymond J. Mooney, In Proceedings of the Seventeenth National Conference on Artificial Intelligence (AAAI-00), pp. 627-632, Austin, TX, July 2000.
Automated Construction of Database Interfaces: Integrating Statistical and Relational Learning for Semantic Parsing 2000
Lappoon R. Tang and Raymond J. Mooney, In Proceedings of the Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora(EMNLP/VLC-2000), pp. 133-141, Hong Kong, October 2000.
Content-Based Book Recommending Using Learning for Text Categorization 2000
Raymond J. Mooney and Loriene Roy, In Proceedings of the Fifth ACM Conference on Digital Libraries, pp. 195-204, San Antonio, TX, June 2000.
Integrating Abduction and Induction in Machine Learning 2000
Raymond J. Mooney, In Abduction and Induction, P. A. Flach and A. C. Kakas (Eds.), pp. 181-191 2000. Kluwer Academic Publishers.
Integrating Statistical and Relational Learning for Semantic Parsing: Applications to Learning Natural Language Interfaces for Databases 2000
Lappoon R. Tang, unpublished. Ph.D. proposal, Department of Computer Sciences, University of Texas at Austin.
Learning for Semantic Interpretation: Scaling Up Without Dumbing Down 2000
Raymond J. Mooney, In Workshop Notes for the Workshop on Learning Language in Logic, pp. 7-15, Bled, Slovenia 2000.
Multiagent Systems: A survey from a machine learning perspective 2000
Peter Stone and Manuela Veloso, Autonomous Robots, Vol. 8, 3 (2000), pp. 345-383.
Using Information Extraction to Aid the Discovery of Prediction Rules from Text 2000
Un Yong Nahm and Raymond J. Mooney, In Proceedings of the Sixth International Conference on Knowledge Discovery and Data Mining (KDD-2000) Workshop on Text Mining, pp. 51--58, Boston, MA, August 2000.
Active Learning for Natural Language Parsing and Information Extraction 1999
Cynthia A. Thompson, Mary Elaine Califf and Raymond J. Mooney, In Proceedings of the Sixteenth International Conference on Machine Learning (ICML-99), pp. 406-414, Bled, Slovenia, June 1999.
Automatic Construction of Semantic Lexicons for Learning Natural Language Interfaces 1999
Cynthia A. Thompson and Raymond J. Mooney, In Proceedings of the Sixteenth National Conference on Artificial Intelligence (AAAI-99), pp. 487-493, Orlando, FL, July 1999.
Content-Based Book Recommending Using Learning for Text Categorization 1999
Raymond J. Mooney and Loriene Roy, In Proceedings of the SIGIR-99 Workshop on Recommender Systems: Algorithms and Evaluation, Berkeley, CA, August 1999.
Relational Learning of Pattern-Match Rules for Information Extraction 1999
Mary Elaine Califf and Raymond J. Mooney, In Proceedings of the Sixteenth National Conference on Artificial Intelligence (AAAI-99), pp. 328-334, Orlando, FL, July 1999.
Using HTML Structure and Linked Pages to Improve Learning for Text Categorization 1999
Michael B. Cline, Technical Report AI 98-270, Department of Computer Sciences, University of Texas at Austin. Undergraduate Honors Thesis.
Advantages of Decision Lists and Implicit Negatives in Inductive Logic Programming 1998
Mary Elaine Califf and Raymond J. Mooney, New Generation Computing, Vol. 16, 3 (1998), pp. 263-281.
An Experimental Comparison of Genetic Programming and Inductive Logic Programming on Learning Recursive List Functions 1998
Lappoon R. Tang, Mary Elaine Califf, and Raymond J. Mooney, Technical Report AI 98-271, Artificial Intelligence Lab, University of Texas at Austin.
Book Recommending Using Text Categorization with Extracted Information 1998
Raymond J. Mooney, Paul N. Bennett, and Loriene Roy, In Proceedings of the Fifteenth National Conference on Artificial Intelligence (AAAI-98)"-REC-WKSHP98, year="1998, pp. 70-74, Madison, WI 1998.
Relational Learning of Pattern-Match Rules for Information Extraction 1998
Mary Elaine Califf and Raymond J. Mooney, In Proceedings of AAAI Spring Symposium on Applying Machine Learning to Discourse Processing, pp. 6-11, Standford, CA, March 1998.
Relational Learning Techniques for Natural Language Information Extraction 1998
Mary Elaine Califf, PhD Thesis, Department of Computer Sciences, University of Texas. 142 pages. Also appears as Artificial Intelligence Laboratory Technical Report AI 98-276.
Semantic Lexicon Acquisition for Learning Natural Language Interfaces 1998
Cynthia Ann Thompson, PhD Thesis, Department of Computer Sciences, University of Texas at Austin. 101 pages. Also appears as Technical Report AI 99-278, Artificial Intelligence Lab, University of Texas at Austin.
Semantic Lexicon Acquisition for Learning Natural Language Interfaces 1998
Cynthia A. Thompson and Raymond J. Mooney, In Proceedings of the Sixth Workshop on Very Large Corpora, Montreal, Quebec, Canada, August 1998. Also available as TR AI 98-273, Artificial Intelligence Lab, University of Texas at Austin, M...
Text Categorization Through Probabilistic Learning: Applications to Recommender Systems 1998
Paul N. Bennett, unpublished. Honors thesis, Department of Computer Sciences, The University of Texas at Austin.
Theory Refinement for Bayesian Networks with Hidden Variables 1998
Sowmya Ramachandran and Raymond J. Mooney, In Proceedings of the Fifteenth International Conference on Machine Learning (ICML-98), pp. 454--462, Madison, WI, July 1998.
Theory Refinement of Bayesian Networks with Hidden Variables 1998
Sowmya Ramachandran and Raymond J. Mooney, PhD Thesis, Department of Computer Sciences, University of Texas at Austin. 139 pages. Also appears as Technical Report AI 98-265, Artificial Intelligence Lab, University of Texas at Austin.
Using Decision Tree Confidence Factors for Multiagent Control 1998
Peter Stone and Manuela Veloso, In RoboCup-97: Robot Soccer World Cup I, Hiroaki Kitano (Eds.), Vol. 1395, pp. 99-111, Berlin 1998. Springer Verlag.
Using Multi-Strategy Learning to Improve Planning Efficiency and Quality 1998
Tara A. Estlin, PhD Thesis, Department of Computer Sciences, The University of Texas at Austin.
An Inductive Logic Programming Method for Corpus-based Parser Construction 1997
John M. Zelle and Raymond J. Mooney, unpublished. Unpublished Technical Note.
Applying ILP-based Techniques to Natural Language Information Extraction: An Experiment in Relational Learning 1997
Mary Elaine Califf and Raymond J. Mooney, In Workshop Notes of the IJCAI-97 Workshop on Frontiers of Inductive Logic Programming, pp. 7--11, Nagoya, Japan, August 1997.
Integrating Abduction and Induction in Machine Learning 1997
Raymond J. Mooney, In Working Notes of the IJCAI-97 Workshop on Abduction and Induction in AI, pp. 37--42, Nagoya, Japan, August 1997.
Learning Parse and Translation Decisions From Examples With Rich Context 1997
Ulf Hermjakob, PhD Thesis, Department of Computer Sciences, The University of Texas at Austin. 175 pages. Technical Report UT-AI97-261.
Learning Parse and Translation Decisions From Examples With Rich Context 1997
Ulf Hermjakob and Raymond J. Mooney, In Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics (ACL'97/EACL'97), pp. 482-489, July 1997.
Learning to Improve both Efficiency and Quality of Planning 1997
Tara A. Estlin and Raymond J. Mooney, In Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence (IJCAI-97), pp. 1227-1232, Nagoya, Japan 1997.
Learning to Parse Natural Language Database Queries into Logical Form 1997
Cynthia A. Thompson, Raymond J. Mooney, and Lappoon R. Tang, In Proceedings of the ML-97 Workshop on Automata Induction, Grammatical Inference, and Language Acquisition, Nashville, TN, July 1997.
Parameter Revision Techniques for Bayesian Networks with Hidden Variables: An Experimental Comparison 1997
Sowmya Ramachandran and Raymond J. Mooney, unpublished. Unpublished Technical Note.
Relational Learning of Pattern-Match Rules for Information Extraction 1997
Mary Elaine Califf and Raymond J. Mooney, In Proceedings of the ACL Workshop on Natural Language Learning, pp. 9-15, Madrid, Spain, July 1997.
Relational Learning Techniques for Natural Language Information Extraction 1997
Mary Elaine Califf, unpublished. Ph.D. proposal, Department of Computer Sciences, University of Texas at Austin.
Semantic Lexicon Acquisition for Learning Parsers 1997
Cynthia A. Thompson and Raymond J. Mooney, unpublished. Submitted for review.
A Novel Application of Theory Refinement to Student Modeling 1996
Paul Baffes and Raymond J. Mooney, In Proceedings of the Thirteenth National Conference on Artificial Intelligence (AAAI-96), pp. 403-408, Portland, OR, August 1996.
Advantages of Decision Lists and Implicit Negative in Inductive Logic Programming 1996
Mary Elaine Califf and Raymond J. Mooney, Technical Report, Artificial Intelligence Lab, University of Texas at Austin.
Beating a Defender in Robotic Soccer: Memory-Based Learning of a Continuous Function 1996
Peter Stone and Manuela Veloso, In Advances in Neural Information Processing Systems 8, David S. Touretzky and Michael C. Mozer and Michael E. Hasselmo (Eds.), pp. 896-902, Cambridge, MA 1996. MIT Press.
Combining Symbolic and Connectionist Learning Methods to Refine Certainty-Factor Rule-Bases 1996
J. Jeffrey Mahoney, PhD Thesis, Department of Computer Sciences, University of Texas at Austin. 113 pages.
Comparative Experiments on Disambiguating Word Senses: An Illustration of the Role of Bias in Machine Learning 1996
Raymond J. Mooney, In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP-96), pp. 82-91, Philadelphia, PA 1996.
Comparative Results on Using Inductive Logic Programming for Corpus-based Parser Construction 1996
John M. Zelle and Raymond J. Mooney, In Connectionist, Statistical, and Symbolic Approaches to Learning for Natural Language Processing, Stefan Wermter and Ellen Riloff and Gabriela Scheler (Eds.), pp. 355-369, Berlin 1996. Spri...
Corpus-Based Lexical Acquisition For Semantic Parsing 1996
Cynthia Thompson, unpublished. Ph.D. proposal.
Hybrid Learning of Search Control for Partial-Order Planning 1996
Tara A. Estlin and Raymond J. Mooney, In New Directions in AI Planning, Malik Ghallab and Alfredo Milani (Eds.), pp. 129-140, Amsterdam 1996. IOS Press.
Inductive Logic Programming for Natural Language Processing 1996
Raymond J. Mooney, In Inductive Logic Programming: Selected papers from the 6th International Workshop, Stephen Muggleton (Eds.), pp. 3-22, Berlin 1996. Springer Verlag.
Integrating EBL and ILP to Acquire Control Rules for Planning 1996
Tara A. Estlin and Raymond J. Mooney, Proceedings of the Third International Workshop on Multi-Strategy Learning (MSL-96) (1996), pp. 271--279.
Integrating Explanation-Based and Inductive Learning Techniques to Acquire Search-Control for Planning 1996
Tara A. Estlin, Technical Report AI96-250, Department of Computer Sciences, University of Texas.
Learning the Past Tense of English Verbs Using Inductive Logic Programming 1996
Raymond J. Mooney and Mary Elaine Califf, In Connectionist, Statistical, and Symbolic Approaches to Learning for Natural Language Processing, {S. Wermter, E. Riloff} and G. Scheler (Eds.), pp. 370-384, Berlin 1996. Springer.
Learning to Parse Database Queries using Inductive Logic Programming 1996
John M. Zelle and Raymond J. Mooney, In AAAI/IAAI, pp. 1050-1055, Portland, OR, August 1996. AAAI Press/MIT Press.
Lexical Acquisition: A Novel Machine Learning Problem 1996
Cynthia A. Thompson and Raymond J. Mooney, Technical Report, Artificial Intelligence Lab, University of Texas at Austin.
Multi-Strategy Learning of Search Control for Partial-Order Planning 1996
Tara A. Estlin and Raymond J. Mooney, In Proceedings of the Thirteenth National Conference on Artificial Intelligence (AAAI-96), pp. 843-848, Portland, OR, August 1996.
Qualitative Multiple-Fault Diagnosis of Continuous Dynamic Systems Using Behavioral Modes 1996
Siddarth Subramanian and Raymond J. Mooney, In Proceedings of the Thirteenth National Conference on Artificial Intelligence (AAAI-96), pp. 965-970, Portland, OR, August 1996.
Refinement-Based Student Modeling and Automated Bug Library Construction 1996
Paul Baffes and Raymond Mooney, Journal of Artificial Intelligence in Education, Vol. 7, 1 (1996), pp. 75-116.
Revising Bayesian Network Parameters Using Backpropagation 1996
Sowmya Ramachandran and Raymond J. Mooney, In Proceedings of the International Conference on Neural Networks (ICNN-96), Special Session on Knowledge-Based Artificial Neural Networks, pp. 82--87, Washington DC, June 1996.
A Comparison of Two Methods Employing Inductive Logic Programming for Corpus-based Parser Constuction 1995
John M. Zelle and Raymond J. Mooney, In Working Notes of the IJCAI-95 Workshop on New Approaches to Learning for Natural Language Processing, pp. 79--86, Montreal, Quebec, Canada, August 1995.
A Preliminary PAC Analysis of Theory Revision 1995
Raymond J. Mooney, In Computational Learning Theory and Natural Learning Systems, Vol. 3, T. Petsche and S. Hanson and Jude W. Shavlik (Eds.), pp. 43-53, Cambridge, MA 1995. MIT Press.
Acquisition of a Lexicon from Semantic Representations of Sentences 1995
Cynthia A. Thompson, In Proceedings of the 33rd Annual Meeting of the Association for Computational Linguistics (ACL-95), pp. 335-337, Cambridge, MA 1995.
Automated Refinement of First-Order Horn-Clause Domain Theories 1995
Bradley L. Richards and Raymond J. Mooney, Machine Learning, Vol. 19, 2 (1995), pp. 95-131.
Encouraging Experimental Results on Learning CNF 1995
Raymond J. Mooney, Machine Learning, Vol. 19, 1 (1995), pp. 79-92.
Inducing Logic Programs without Explicit Negative Examples 1995
John M. Zelle, Cynthia A. Thompson, Mary Elaine Califf, and Raymond J. Mooney, In Proceedings of the Fifth International Workshop on Inductive Logic Programming (ILP-95), pp. 403-416, Leuven, Belgium 1995.
Induction of First-Order Decision Lists: Results on Learning the Past Tense of English Verbs 1995
Raymond J. Mooney and Mary Elaine Califf, Journal of Artificial Intelligence Research, Vol. 3 (1995), pp. 1-24.
Multiple-Fault Diagnosis Using General Qualitative Models with Fault Modes 1995
Siddarth Subramanian and Raymond J. Mooney, In Working Notes of the IJCAI-95 Workshop on Engneering Problems for Qualitative Reasoning, Monreal, Quebec, August 1995.
Qualitative Multiple-Fault Diagnosis of Continuous Dynamic Systems Using Behavioral Modes 1995
Siddarth Subramanian, PhD Thesis, Department of Computer Sciences, University of Texas at Austin. 128 pages. Also appears as Technical Report AI 95-239.
Refinement of Bayesian Networks by Combining Connectionist and Symbolic Techniques 1995
Sowmya Ramachandran, Unpublished Ph.D. Thesis Proposal.
Using Inductive Logic Programming to Automate the Construction of Natural Language Parsers 1995
John M. Zelle, PhD Thesis, Department of Computer Sciences, The University of Texas at Austin.
A Multistrategy Approach to Theory Refinement 1994
Raymond J. Mooney and Dirk Ourston, In Machine Learning: A Multistrategy Approach, Vol. IV, Ryszard S. Michalski and G. Teccuci (Eds.), pp. 141-164, San Mateo, CA 1994. Morgan Kaufmann.
Automatic Student Modeling and Bug Library Construction using Theory Refinement 1994
Paul T. Baffes, PhD Thesis, Department of Computer Sciences, The University of Texas at Austin.
Combining Top-Down And Bottom-Up Techniques In Inductive Logic Programming 1994
John M. Zelle, Raymond J. Mooney, and Joshua B. Konvisser, In Proceedings of the Eleventh International Workshop on Machine Learning (ML-94), pp. 343--351, Rutgers, NJ, July 1994.
Comparing Methods For Refining Certainty Factor Rule-Bases 1994
J. Jeffrey Mahoney and Raymond J. Mooney, In Proceedings of the Eleventh International Workshop on Machine Learning (ML-94), pp. 173--180, Rutgers, NJ, July 1994.
Inducing Deterministic Prolog Parsers From Treebanks: A Machine Learning Approach 1994
John M. Zelle and Raymond J. Mooney, Proceedings of the Twelfth National Conference on Artificial Intelligence (AAAI-94) (1994), pp. 748--753.
Inductive Learning For Abductive Diagnosis 1994
Cynthia A. Thompson and Raymond J. Mooney, In Proceedings of the Twelfth National Conference on Artificial Intelligence (AAAI-94), pp. 664-669, Seattle, WA, August 1994.
Integrating ILP and EBL 1994
Raymond J. Mooney and John M. Zelle, Sigart Bulletin (special issue on Inductive Logic Programmming), Vol. 5, 1 (1994), pp. 12-21.
Learning Qualitative Models for Systems with Multiple Operating Regions 1994
Sowmya Ramachandran, Raymond J. Mooney, and Benjamin J. Kuipers, In Proceedings of the Eighth International Workshop on Qualitative Reasoning about Physical Systems, Nara, Japan 1994.
Modifying Network Architectures For Certainty-Factor Rule-Base Revision 1994
J. Jeffrey Mahoney and Raymond J. Mooney, In Proceedings of the International Symposium on Integrating Knowledge and Neural Heuristics (ISIKNH-94), pp. 75--85, Pensacola, FL, May 1994.
Multiple-Fault Diagnosis Using General Qualitative Models with Fault Modes 1994
Siddarth Subramanian and Raymond J. Mooney, In Working Papers of the Fifth International Workshop on Principles of Diagnosis, pp. 321-325, New Paltz, NY, October 1994.
Theory Refinement Combining Analytical and Empirical Methods 1994
Dirk Ourston and Raymond J. Mooney, Artificial Intelligence (1994), pp. 311-344.
Combining Connectionist and Symbolic Learning to Refine Certainty-Factor Rule-Bases 1993
J. Jeffrey Mahoney and Raymond J. Mooney, Connection Science (1993), pp. 339-364.
Combining FOIL and EBG to Speed-Up Logic Programs 1993
John M. Zelle and Raymond J. Mooney, In Proceedings of the 13th International Joint Conference on Artificial Intelligence, pp. 1106-1111 1993. San Francisco, CA: Morgan Kaufmann.
Extending Theory Refinement to M-of-N Rules 1993
Paul T. Baffes and Raymond J. Mooney, Informatica, Vol. 17 (1993), pp. 387-397.
Induction Over the Unexplained: Using Overly-General Domain Theories to Aid Concept Learning 1993
Raymond J. Mooney, Machine Learning, Vol. 10 (1993), pp. 79-110.
Inductive Learning For Abductive Diagnosis 1993
Cynthia A. Thompson, Masters Thesis, Department of Computer Sciences, The University of Texas at Austin. 53 pages.
Integrating Theory and Data in Category Learning 1993
Raymond J. Mooney, In Categorization by Humans and Machines, G. V. Nakamura and D. L. Medin and R. Taraban (Eds.), pp. 189-218 1993.
Learning Search-Control Heuristics for Logic Programs: Applications to Speedup Learning and Language Acquisition 1993
John M. Zelle, unpublished. Ph.D. proposal, Department of Computer Sciences, University of Texas at Austin.
Learning Semantic Grammars With Constructive Inductive Logic Programming 1993
John M. Zelle and Raymond J. Mooney, In Proceedings of the 11th National Conference on Artificial Intelligence, pp. 817-822 1993. Menlo Park, CA: AAAI Press.
Learning to Model Students: Using Theory Refinement to Detect Misconceptions 1993
Paul T. Baffes, unpublished. Ph.D. proposal, Department of Computer Sciences, University of Texas at Austin.
Symbolic Revision of Theories With M-of-N Rules 1993
Paul T. Baffes and Raymond J. Mooney, In Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence (IJCAI-93), pp. 1135-1140, Chambery, France, August 1993.
A First-Order Horn-Clause Abductive System and Its Use in Plan Recognition and Diagnosis 1992
Hwee Tou Ng and Raymond J. Mooney, unpublished. Unpublished Technical Note.
A General Abductive system with application to plan recognition and diagnosis 1992
Hwee Tou Ng, PhD Thesis, Department of Computer Sciences, University of Texas at Austin. 154 pages.
Abductive Plan Recognition and Diagnosis: A Comprehensive Empirical Evaluation 1992
Hwee Tou Ng and Raymond J. Mooney, In Proceedings of the Third International Conference on Principles of Knowledge Representation and Reasoning, pp. 499--508, Cambridge, MA, October 1992.
Automated Debugging of Logic Programs via Theory Revision 1992
Raymond J. Mooney and Bradley L. Richards, In Proceedings of the Second International Workshop on Inductive Logic Programming (ILP-92), Tokyo, Japan 1992.
Automatic Abduction of Qualitative Models 1992
Bradley L. Richards, Ina Kraan, and Benjamin J. Kuipers, In Proceedings of the Fifth International Workshop on Qualitative Reasoning about Physical Systems, pp. 295-301 1992.
Batch versus Incremental Theory Refinement 1992
Raymond J. Mooney, In Proceedings of the 1992 AAAI Spring Symposium on Knowledge Assimilation, Standford, CA, March 1992.
Belief Revision in the Context of Abductive Explanation 1992
Siddarth Subramanian, Technical Report AI92-179, Artificial Intelligence Laboratory, University of Texas.
Combining Symbolic and Neural Learning to Revise Probabilistic Theories 1992
J. Jeffrey Mahoney and Raymond J. Mooney, In Proceedings of the ML92 Workshop on Integrated Learning in Real Domains, Aberdeen, Scotland, July 1992.
Growing Layers of Perceptrons: Introducing the Extentron Algorithm 1992
Paul T. Baffes and John M. Zelle, In Proceedings of the 1992 International Joint Conference on Neural Networks, pp. 392--397, Baltimore, MD, June 1992.
Learning Relations by Pathfinding 1992
Bradley L. Richards and Raymond J. Mooney, In Proceedings of the Tenth National Conference on Artificial Intelligence (AAAI-92), pp. 50-55, San Jose, CA, July 1992.
Schema acquisition from a single example 1992
W. Ahn, W. F. Brewer and Raymond J. Mooney, Journal of Experimental Psychology: Learning, Memory, and Cognition, Vol. 18 (1992), pp. 391-412.
Speeding-up Logic Programs by Combining EBG and FOIL 1992
John M. Zelle and Raymond J. Mooney, In Proceedings of the 1992 Machine Learning Workshop on Knowledge Compilation and Speedup Learning, Aberdeen, Scotland, July 1992.
Using Theory Revision to Model Students and Acquire Stereotypical Errors 1992
Paul T. Baffes and Raymond J. Mooney, In Proceedings of the Fourteenth Annual Conference of the Cognitive Science Society, pp. 617-622, Bloomington, IN 1992.
An Efficient First-Order Horn-Clause Abduction System Based on the ATMS 1991
Hwee Tou Ng and Raymond J. Mooney, In Proceedings of the Ninth National Conference on Artificial Intelligence (AAAI-91), pp. 494-499, Anaheim, CA, July 1991.
Constructive Induction in Theory Refinement 1991
Raymond J. Mooney and Dirk Ourston, In Proceedings of the Eighth International Workshop on Machine Learning, pp. 178-182, Evanston, IL, June 1991.
Improving Shared Rules in Multiple Category Domain Theories 1991
Dirk Ourston and Raymond J. Mooney, In Proceedings of the Eighth International Workshop on Machine Learning, pp. 534-538, Evanston, IL, June 1991.
Symbolic and Neural Learning Algorithms: An Experimental Comparison 1991
J.W. Shavlik, Raymond J. Mooney and G. Towell, Machine Learning, Vol. 6 (1991), pp. 111-143. Reprinted in {it Readings in Knowledge Acquisition and Learning}, Bruce G. Buchanan and David C. Wilkins (eds.), Morgan Kaufman, San Mateo, CA, 19...
Theory Refinement with Noisy Data 1991
Raymond J. Mooney and Dirk Ourston, Technical Report AI91-153, Artificial Intelligence Laboratory, University of Texas.
Changing the Rules: A Comprehensive Approach to Theory Refinement 1990
D. Ourston and Raymond J. Mooney, In Proceedings of the Eighth National Conference on Artificial Intelligence (AAAI-90), pp. 815-820, Boston, MA, July 1990.
Learning Plan Schemata From Observation: Explanation-Based Learning for Plan Recognition 1990
Raymond J. Mooney, Cognitive Science, Vol. 14, 4 (1990), pp. 483-509.
On the Role of Coherence in Abductive Explanation 1990
Hwee Tou Ng and Raymond J. Mooney, In Proceedings of the Eighth National Conference on Artificial Intelligence (AAAI-90), pp. 337--342, Boston, MA, July 1990.
An Experimental Comparison of Symbolic and Connectionist Learning Algorithms 1989
Raymond J. Mooney, J.W. Shavlik, G. Towell and A. Gove, In Proceedings of the Eleventh International Joint Conference on Artificial Intelligence (IJCAI-89), pp. 775-780, Detroit, MI, August 1989. Reprinted in ``Readings in Machine Learning'', Jude ...
Processing Issues in Comparisons of Symbolic and Connectionist Learning Systems 1989
Douglas Fisher, Kathleen McKusick, Raymond J. Mooney, Jude W. Shavlik, and Geoffrey Towell, In Proceedings of the Sixth International Workshop on Machine Learning, pp. 169--173, Ithaca, New York 1989.
The Effect of Rule Use on the Utility of Explanation-Based Learning 1989
Raymond J. Mooney, In Proceedings of the 11th International Joint Conference on Artificial Intelligence, pp. 725-730 1989. San Francisco, CA: Morgan Kaufmann.
Generalizing the Order of Operators in Macro-Operators 1988
Raymond J. Mooney, In Proceedings of the Fifth International Conference on Machine Learning (ICML-88), pp. 270-283, Ann Arbor, MI, June 1988.
Integrated Learning of Words and their Underlying Concepts 1987
Raymond J. Mooney, In Proceedings of the Ninth Annual Conference of the Cognitive Science Society, pp. 947-978, Seattle, WA, July 1987.
Schema Acquisition from One Example: Psychological Evidence for Explanation-Based Learning 1987
W. Ahn, Raymond J. Mooney, W.F. Brewer and G.F. DeJong, In Proceedings of the Ninth Annual Conference of the Cognitive Science Society, pp. 50-57, Seattle, WA, July 1987.
A Domain Independent Explanation-Based Generalizer 1986
Raymond J. Mooney and S.W. Bennett, In Proceedings of the Fifth National Conference on Artificial Intelligence (AAAI-86), pp. 551-555, Philadelphia, PA, August 1986.
Explanation-Based Learning: An Alternative View 1986
G.F. DeJong and Raymond J. Mooney, Machine Learning (1986), pp. 145-176.
Learning Schemata for Natural Language Processing 1985
Raymond J. Mooney and Gerald F. DeJong, In Proceedings of the Ninth International Joint Conference on Artificial Intelligence (IJCAI-85), pp. 681-687, Los Angeles, CA, August 1985.
Projects
TEXPLORE: Real-Time Sample Efficient Reinforcement Learning 2009 - Present
Demos
A Neuroevolution Approach to General Atari Game PlayingMatthew Hausknecht2013
TEXPLORE: Real-Time Sample Efficient Reinforcement LearningTodd Hester2012
Labs
Machine Learning Learning Agents