Russell Greiner

Mohsen Hajiloo, Yadav Sapkota, John R. Mackey, Paula Robson, Russell Greiner, Sambasivarao Damaraju: ETHNOPRED: a novel machine learning method for accurate continental and sub-continental ancestry identification and population stratification correction. BMC Bioinformatics 14: 61 (2013)

David S. Wishart, Timothy Jewison, Anchi Guo, Michael Wilson, Craig Knox, Yifeng Liu, Yannick Djoumbou, Rupasri Mandal, Farid Aziat, Edison Dong, Souhaila Bouatra, Igor Sinelnikov, David Arndt, Jianguo Xia, Philip Liu, Faizath Yallou, Trent C. Bjorndahl, Rolando Perez-Pineiro, Roman Eisner, Felicity Allen, Vanessa Neveu, Russell Greiner, Augustin Scalbert: HMDB 3.0 - The Human Metabolome Database in 2013. Nucleic Acids Research 41(Database-Issue): 801-807 (2013)

Tim Van Allen, Russell Greiner, Peter Hooper: Bayesian Error-Bars for Belief Net Inference. CoRR abs/1301.2313 (2013)

Jie Cheng, Russell Greiner: Comparing Bayesian Network Classifiers. CoRR abs/1301.6684 (2013)

Russell Greiner, Adam J. Grove, Dale Schuurmans: Learning Bayesian Nets that Perform Well. CoRR abs/1302.1542 (2013)

Baidya Nath Saha, Nilanjan Ray, Russell Greiner, Albert Murtha, Hong Zhang: Quick detection of brain tumors and edemas: A bounding box method using symmetry. Comp. Med. Imag. and Graph. 36(2): 95-107 (2012)

Ashkan Zarnani, Petr Musílek, Xiaoyu Shi, Xiaodi Ke, Hua He, Russell Greiner: Learning to predict ice accretion on electric power lines. Eng. Appl. of AI 25(3): 609-617 (2012)

Davoud Moulavi, Mohsen Hajiloo, Jörg Sander, Philip F. Halloran, Russell Greiner: Combining gene expression and interaction network data to improve kidney lesion score prediction. IJBRA 8(1/2): 54-66 (2012)

Daniel J. Lizotte, Russell Greiner, Dale Schuurmans: An experimental methodology for response surface optimization methods. J. Global Optimization 53(4): 699-736 (2012)

Siamak (Moshen) Ravanbakhsh, Chun-Nam Yu, Russell Greiner: A Generalized Loop Correction Method for Approximate Inference in Graphical Models. ICML 2012

Peter Hooper, Yasin Abbasi-Yadkori, Russell Greiner, Bret Hoehn: Improved Mean and Variance Approximations for Belief Net Responses via Network Doubling. CoRR abs/1205.2642 (2012)

Alejandro Isaza, Csaba Szepesvári, Vadim Bulitko, Russell Greiner: Speeding Up Planning in Markov Decision Processes via Automatically Constructed Abstractions. CoRR abs/1206.3233 (2012)

Siamak (Moshen) Ravanbakhsh, Chun-Nam Yu, Russell Greiner: A Generalized Loop Correction Method for Approximate Inference in Graphical Models. CoRR abs/1206.4654 (2012)

Omid Madani, Daniel J. Lizotte, Russell Greiner: Active Model Selection. CoRR abs/1207.4138 (2012)

Daniel J. Lizotte, Omid Madani, Russell Greiner: Budgeted Learning of Naive-Bayes Classifiers. CoRR abs/1212.2472 (2012)

Chun-Nam Yu, Russell Greiner, Hsiu-Chin Lin, Vickie Baracos: Learning Patient-Specific Cancer Survival Distributions as a Sequence of Dependent Regressors. NIPS 2011: 1845-1853

Xiaoyuan Su, Russell Greiner, Taghi M. Khoshgoftaar, Amri Napolitano: Using Classifier-Based Nominal Imputation to Improve Machine Learning. PAKDD (1) 2011: 124-135

Oliver Schulte, Wei Luo, Russell Greiner: Mind change optimal learning of Bayes net structure from dependency and independency data. Inf. Comput. 208(1): 63-82 (2010)

Siamak (Moshen) Ravanbakhsh, Barnabás Póczos, Russell Greiner: A Cross-Entropy Method that Optimizes Partially Decomposable Problems: A New Way to Interpret NMR Spectra. AAAI 2010

Oliver Schulte, Gustavo Frigo, Russell Greiner, Hassan Khosravi: The IMAP Hybrid Method for Learning Gaussian Bayes Nets. Canadian Conference on AI 2010: 123-134

Liuyang Li, Barnabás Póczos, Csaba Szepesvári, Russell Greiner: Budgeted Distribution Learning of Belief Net Parameters. ICML 2010: 879-886

Babak Bostan, Russell Greiner, Duane Szafron, Paul Lu: Predicting homologous signaling pathways using machine learning. Bioinformatics 25(22): 2913-2920 (2009)

Xiaoyuan Su, Taghi M. Khoshgoftaar, Russell Greiner: Making an accurate classifier ensemble by voting on classifications from imputed learning sets. IJIDS 1(3): 301-322 (2009)

David S. Wishart, Craig Knox, Anchi Guo, Roman Eisner, Nelson Young, Bijaya Gautam, David D. Hau, Nick Psychogios, Edison Dong, Souhaila Bouatra, Rupasri Mandal, Igor Sinelnikov, Jianguo Xia, Leslie Jia, Joseph A. Cruz, Emilia Lim, Constance A. Sobsey, Savita Shrivastava, Paul Huang, Philip Liu, Lydia Fang, Jun Peng, Ryan Fradette, Dean Cheng, Dan Tzur, Melisa Clements, Avalyn Lewis, Andrea De Souza, Azaret Zuniga, Margot Dawe, Yeping Xiong, Derrick Clive, Russell Greiner, Alsu Nazyrova, Rustem Shaykhutdinov, Liang Li, Hans J. Vogel, Ian J. Forsythe: HMDB: a knowledgebase for the human metabolome. Nucleic Acids Research 37(Database-Issue): 603-610 (2009)

Tingshao Zhu, Russell Greiner, Bin Hu: LILAC - Learn from Internet: Log, Annotation, and Content. AAAI Spring Symposium: Experimental Design for Real-World Systems 2009: 57-

Aliaksei Kerhet, Cormac Small, Harvey Quon, Terence Riauka, Russell Greiner, Alexander McEwan, Wilson Roa: Segmentation of Lung Tumours in Positron Emission Tomography Scans: A Machine Learning Approach. AIME 2009: 146-155

Oliver Schulte, Gustavo Frigo, Russell Greiner, Wei Luo, Hassan Khosravi: A new hybrid method for Bayesian network learning With dependency constraints. CIDM 2009: 53-60

Xiaoyuan Su, Taghi M. Khoshgoftaar, Russell Greiner: VipBoost: A More Accurate Boosting Algorithm. FLAIRS Conference 2009

Alireza Farhangfar, Russell Greiner, Csaba Szepesvári: Learning to segment from a few well-selected training images. ICML 2009: 39

Barnabás Póczos, Yasin Abbasi-Yadkori, Csaba Szepesvári, Russell Greiner, Nathan R. Sturtevant: Learning when to stop thinking and do something! ICML 2009: 104

Peter Hooper, Yasin Abbasi-Yadkori, Russell Greiner, Bret Hoehn: Improved Mean and Variance Approximations for Belief Net Responses via Network Doubling. UAI 2009: 232-239

Tim Van Allen, Ajit Singh, Russell Greiner, Peter Hooper: Quantifying the uncertainty of a belief net response: Bayesian error-bars for belief net inference. Artif. Intell. 172(4-5): 483-513 (2008)

Alona Fyshe, Yifeng Liu, Duane Szafron, Russell Greiner, Paul Lu: Improving subcellular localization prediction using text classification and the gene ontology. Bioinformatics 24(21): 2512-2517 (2008)

Ilya Levner, Hong Zhang, Russell Greiner: Heterogeneous Stacking for Classification-Driven Watershed Segmentation. EURASIP J. Adv. Sig. Proc. 2008 (2008)

Chi-Hoon Lee, Osmar R. Zaïane, Ho-Hyun Park, Jiayuan Huang, Russell Greiner: Clustering high dimensional data: A graph-based relaxed optimization approach. Inf. Sci. 178(23): 4501-4511 (2008)

Chi-Hoon Lee, Matthew R. G. Brown, Russell Greiner, Shaojun Wang, Albert Murtha: Constrained Classification on Structured Data. AAAI 2008: 1812-1813

Alejandro Isaza, Jieshan Lu, Vadim Bulitko, Russell Greiner: A Cover-Based Approach to Multi-Agent Moving Target Pursuit. AIIDE 2008

Xiaoyuan Su, Taghi M. Khoshgoftaar, Russell Greiner: A Mixture Imputation-Boosted Collaborative Filter. FLAIRS Conference 2008: 312-316

Ilya Levner, Russell Greiner, Hong Zhang: Supervised image segmentation via ground truth decomposition. ICIP 2008: 737-740

John Lees-Miller, Fraser Anderson, Bret Hoehn, Russell Greiner: Does Wikipedia Information Help Netflix Predictions? ICMLA 2008: 337-343

Xiaoyuan Su, Taghi M. Khoshgoftaar, Russell Greiner: Using Imputation Techniques to Help Learn Accurate Classifiers. ICTAI (1) 2008: 437-444

Alireza Farhangfar, Russell Greiner, Martin Zinkevich: A Fast Way to Produce Optimal Fixed-Depth Decision Trees. ISAIM 2008

Chi-Hoon Lee, Shaojun Wang, Albert Murtha, Matthew R. G. Brown, Russell Greiner: Segmenting Brain Tumors Using Pseudo-Conditional Random Fields. MICCAI (1) 2008: 359-366

Xiaoyuan Su, Taghi M. Khoshgoftaar, Xingquan Zhu, Russell Greiner: Imputation-boosted collaborative filtering using machine learning classifiers. SAC 2008: 949-950

Alejandro Isaza, Csaba Szepesvári, Vadim Bulitko, Russell Greiner: Speeding Up Planning in Markov Decision Processes via Automatically Constructed Abstraction. UAI 2008: 306-314

Xiaoyuan Su, Taghi M. Khoshgoftaar, Russell Greiner: Imputed Neighborhood Based Collaborative Filtering. Web Intelligence 2008: 633-639

Lihong Li, Vadim Bulitko, Russell Greiner: Focus of Attention in Reinforcement Learning. J. UCS 13(9): 1246-1269 (2007)

David S. Wishart, Dan Tzur, Craig Knox, Roman Eisner, Anchi Guo, Nelson Young, Dean Cheng, Kevin Jewell, David Arndt, Summit Sawhney, Chris Fung, Lisa Nikolai, Mike Lewis, Marie-Aude Coutouly, Ian J. Forsythe, Peter Tang, Savita Shrivastava, Kevin Jeroncic, Paul Stothard, Godwin Amegbey, David Block, David D. Hau, James Wagner, Jessica Miniaci, Melisa Clements, Mulu Gebremedhin, Natalie Guo, Ying Zhang, Gavin E. Duggan, Glen D. MacInnis, Alim M. Weljie, Reza Dowlatabadi, Fiona Bamforth, Derrick Clive, Russell Greiner, Liang Li, Tom Marrie, Brian D. Sykes, Hans J. Vogel, Lori Querengesser: HMDB: the Human Metabolome Database. Nucleic Acids Research 35(Database-Issue): 521-526 (2007)

Oliver Schulte, Wei Luo, Russell Greiner: Mind Change Optimal Learning of Bayes Net Structure. COLT 2007: 187-202

Yuhong Guo, Russell Greiner: Optimistic Active-Learning Using Mutual Information. IJCAI 2007: 823-829

David S. Wishart, Russell Greiner: Session Introduction. Pacific Symposium on Biocomputing 2007: 112-114

Xiaoyuan Su, Russell Greiner, Taghi M. Khoshgoftaar, Xingquan Zhu: Hybrid Collaborative Filtering Algorithms Using a Mixture of Experts. Web Intelligence 2007: 645-649

Russell Greiner, Ryan Hayward, Magdalena Jankowska, Michael Molloy: Finding optimal satisficing strategies for and-or trees. Artif. Intell. 170(1): 19-58 (2006)

Marianne Morris, Russell Greiner, Jörg Sander, Albert Murtha, Mark W. Schmidt: Learning a Classification-based Glioma Growth Model Using MRI Data. JCP 1(7): 21-31 (2006)

Luca Pireddu, Duane Szafron, Paul Lu, Russell Greiner: The Path-A metabolic pathway prediction web server. Nucleic Acids Research 34(Web-Server-Issue): 714-719 (2006)

Brett Poulin, Roman Eisner, Duane Szafron, Paul Lu, Russell Greiner, David S. Wishart, Alona Fyshe, Brandon Pearcy, Cam Macdonell, John Anvik: Visual Explanation of Evidence with Additive Classifiers. AAAI 2006: 1822-1829

Feng Jiao, Shaojun Wang, Chi-Hoon Lee, Russell Greiner, Dale Schuurmans: Semi-Supervised Conditional Random Fields for Improved Sequence Segmentation and Labeling. ACL 2006

Marianne Morris, Russell Greiner, Jörg Sander, Albert Murtha, Mark W. Schmidt: A Classification-Based Glioma Diffusion Model Using MRI Data. Canadian Conference on AI 2006: 98-109

Ramana Isukapalli, Ahmed M. Elgammal, Russell Greiner: Learning to Detect Objects of Many Classes Using Binary Classifiers. ECCV (1) 2006: 352-364

Ramana Isukapalli, Ahmed M. Elgammal, Russell Greiner: Learning to Identify Facial Expression During Detection Using Markov Decision Process. FG 2006: 305-310

Shaojun Wang, Shaomin Wang, Li Cheng, Russell Greiner, Dale Schuurmans: Stochastic Analysis of Lexical and Semantic Enhanced Structural Language Model. ICGI 2006: 97-111

Chi-Hoon Lee, Russell Greiner, Shaojun Wang: Using query-specific variance estimates to combine Bayesian classifiers. ICML 2006: 529-536

Ramana Isukapalli, Ahmed M. Elgammal, Russell Greiner: Learning Policies for Efficiently Identifying Objects of Many Classes. ICPR (3) 2006: 356-361

Robert Price, Russell Greiner, Gerald Häubl, Alden Flatt: Automatic construction of personalized customer interfaces. IUI 2006: 250-257

Chi-Hoon Lee, Shaojun Wang, Feng Jiao, Dale Schuurmans, Russell Greiner: Learning to Model Spatial Dependency: Semi-Supervised Discriminative Random Fields. NIPS 2006: 793-800

Jiayuan Huang, Tingshao Zhu, Russell Greiner, Dengyong Zhou, Dale Schuurmans: Information Marginalization on Subgraphs. PKDD 2006: 199-210

Chi-Hoon Lee, Russell Greiner, Osmar R. Zaïane: Efficient Spatial Classification Using Decoupled Conditional Random Fields. PKDD 2006: 272-283

Russell Greiner, Xiaoyuan Su, Bin Shen, Wei Zhou: Structural Extension to Logistic Regression: Discriminative Parameter Learning of Belief Net Classifiers. Machine Learning 59(3): 297-322 (2005)

Paul Lu, Duane Szafron, Russell Greiner, David S. Wishart, Alona Fyshe, Brandon Pearcy, Brett Poulin, Roman Eisner, Danny Ngo, Nicholas Lamb: PA-GOSUB: a searchable database of model organism protein sequences with their predicted Gene Ontology molecular function and subcellular localization. Nucleic Acids Research 33(Database-Issue): 147-153 (2005)

Gary H. Van Domselaar, Paul Stothard, Savita Shrivastava, Joseph A. Cruz, Anchi Guo, Xiaoli Dong, Paul Lu, Duane Szafron, Russell Greiner, David S. Wishart: BASys: a web server for automated bacterial genome annotation. Nucleic Acids Research 33(Web-Server-Issue): 455-459 (2005)

Tingshao Zhu, Russell Greiner, Gerald Häubl, Kevin Jewell, Robert Price: Goal-Directed Site-Independent Recommendations from Passive Observations. AAAI 2005: 549-557

Yuhong Guo, Russell Greiner: Discriminative Model Selection for Belief Net Structures. AAAI 2005: 770-776

Brett Poulin, Duane Szafron, Paul Lu, Russell Greiner, David S. Wishart, Roman Eisner, Alona Fyshe, Brandon Pearcy, Luca Pireddu: The Proteome Analyst Suite of Automated Function Prediction Tools. AAAI 2005: 1698-1699

Ramana Isukapalli, Ahmed M. Elgammal, Russell Greiner: Learning a Dynamic Classification Method to Detect Faces and Identify Facial Expression. AMFG 2005: 70-84

Roman Eisner, Brett Poulin, Duane Szafron, Paul Lu, Russell Greiner: Improving Protein Function Prediction Using the Hierarchical Structure of the Gene Ontology. CIBCB 2005: 354-363

Chi-Hoon Lee, Mark W. Schmidt, Albert Murtha, Aalo Bistritz, Jörg Sander, Russell Greiner: Segmenting Brain Tumors with Conditional Random Fields and Support Vector Machines. CVBIA 2005: 469-478

Aloak Kapoor, Russell Greiner: Learning and Classifying Under Hard Budgets. ECML 2005: 170-181

Shaojun Wang, Shaomin Wang, Russell Greiner, Dale Schuurmans, Li Cheng: Exploiting syntactic, semantic and lexical regularities in language modeling via directed Markov random fields. ICML 2005: 948-955

Mark W. Schmidt, Ilya Levner, Russell Greiner, Albert Murtha, Aalo Bistritz: Segmenting brain tumors using alignment-based features. ICMLA 2005

Yuhong Guo, Russell Greiner, Dale Schuurmans: Learning Coordination Classifiers. IJCAI 2005: 714-721

Tingshao Zhu, Russell Greiner, Gerald Häubl, Kevin Jewell, Robert Price: Using Learned Browsing Behavior Models to Recommend Relevant Web Pages. IJCAI 2005: 1589-1590

Chi-Hoon Lee, Russell Greiner, Mark W. Schmidt: Support Vector Random Fields for Spatial Classification. PKDD 2005: 121-132

Tingshao Zhu, Russell Greiner, Gerald Häubl, Kevin Jewell, Robert Price: Off-line Evaluation of Recommendation Functions. User Modeling 2005: 337-341

Zhiyong Lu, Duane Szafron, Russell Greiner, Paul Lu, David S. Wishart, Brett Poulin, John Anvik, Cam Macdonell, Roman Eisner: Predicting subcellular localization of proteins using machine-learned classifiers. Bioinformatics 20(4): 547-556 (2004)

Duane Szafron, Paul Lu, Russell Greiner, David S. Wishart, Brett Poulin, Roman Eisner, Zhiyong Lu, John Anvik, Cam Macdonell, Alona Fyshe, David Meeuwis: Proteome Analyst: custom predictions with explanations in a web-based tool for high-throughput proteome annotations. Nucleic Acids Research 32(Web-Server-Issue): 365-371 (2004)

Omid Madani, Daniel J. Lizotte, Russell Greiner: The Budgeted Multi-armed Bandit Problem. COLT 2004: 643-645

Lihong Li, Vadim Bulitko, Russell Greiner: Batch Reinforcement Learning with State Importance. ECML 2004: 566-568

Omid Madani, Daniel J. Lizotte, Russell Greiner: Active Model Selection. UAI 2004: 357-365

Ilya Levner, Vadim Bulitko, Lihong Li, Greg Lee, Russell Greiner: Towards Automated Creation of Image Interpretation Systems. Australian Conference on Artificial Intelligence 2003: 653-665

Bin Shen, Xiaoyuan Su, Russell Greiner, Petr Musílek, Corrine Cheng: Discriminative Parameter Learning of General Bayesian Network Classifiers. ICTAI 2003: 296-305

Tingshao Zhu, Russell Greiner, Gerald Häubl, Robert Price: Predicting Web Information Content. ITWP 2003: 241-254

Ramana Isukapalli, Russell Greiner: Use of Off-line Dynamic Programming for Efficient Image Interpretation. IJCAI 2003: 1319-1325

Vadim Bulitko, Lihong Li, Russell Greiner, Ilya Levner: Lookahead Pathologies for Single Agent Search. IJCAI 2003: 1531-1533

Daniel J. Lizotte, Omid Madani, Russell Greiner: Budgeted Learning of Naive-Bayes Classifiers. UAI 2003: 378-385

Tingshao Zhu, Russell Greiner, Gerald Häubl: Learning a Model of a Web User's Interests. User Modeling 2003: 65-75

Tingshao Zhu, Russell Greiner, Gerald Häubl: An Effective Complete-Web Recommender System. WWW (Alternate Paper Tracks) 2003

Jie Cheng, Russell Greiner, Jonathan Kelly, David A. Bell, Weiru Liu: Learning Bayesian networks from data: An information-theory based approach. Artif. Intell. 137(1-2): 43-90 (2002)

Russell Greiner, Adam J. Grove, Dan Roth: Learning cost-sensitive active classifiers. Artif. Intell. 139(2): 137-174 (2002)

Russell Greiner, Wei Zhou: Structural Extension to Logistic Regression: Discriminative Parameter Learning of Belief Net Classifiers. AAAI/IAAI 2002: 167-173

Russell Greiner, Ryan Hayward, Michael Molloy: Optimal Depth-First Strategies for And-Or Trees. AAAI/IAAI 2002: 725-730

Ilya Levner, Vadim Bulitko, Omid Madani, Russell Greiner: Performance of Lookahead Control Policies in the Face of Abstractions and Approximations. SARA 2002: 299-307

Russell Greiner, Christian Darken, N. Iwan Santoso: Efficient reasoning. ACM Comput. Surv. 33(1): 1-30 (2001)

Jie Cheng, Russell Greiner: Learning Bayesian Belief Network Classifiers: Algorithms and System. Canadian Conference on AI 2001: 141-151

Ramana Isukapalli, Russell Greiner: Efficient Car Recognition Policies. ICRA 2001: 2134-2139

Ramana Isukapalli, Russell Greiner: Efficient Interpretation Policies. IJCAI 2001: 1381-1390

Tim Van Allen, Russell Greiner, Peter Hooper: Bayesian Error-Bars for Belief Net Inference. UAI 2001: 522-529

Benjamin Korvemaker, Russell Greiner: Predicting UNIX Command Lines: Adjusting to User Patterns. AAAI/IAAI 2000: 230-235

Tim Van Allen, Russell Greiner: Model Selection Criteria for Learning Belief Nets: An Empirical Comparison. ICML 2000: 1047-1054

Russell Greiner: The Complexity of Revising Logic Programs. J. Log. Program. 40(2-3): 273-298 (1999)

Jie Cheng, Russell Greiner: Comparing Bayesian Network Classifiers. UAI 1999: 101-108

Devika Subramanian, Russell Greiner, Judea Pearl: The Relevance of Relevance (Editorial). Artif. Intell. 97(1-2): 1-5 (1997)

Russell Greiner, Adam J. Grove, Alexander Kogan: Knowing what doesn't Matter: Exploiting the Omission of Irrelevant Data. Artif. Intell. 97(1-2): 345-380 (1997)

Tobias Scheffer, Russell Greiner, Christian Darken: Why Experimentation can be better than "Perfect Guidance". ICML 1997: 331-339

Russell Greiner, Adam J. Grove, Dale Schuurmans: Learning Bayesian Nets that Perform Well. UAI 1997: 198-207

Russell Greiner, Pekka Orponen: Probably Approximately Optimal Satisficing Strategies. Artif. Intell. 82(1-2): 21-44 (1996)

Russell Greiner: PALO: A Probabilistic Hill-Climbing Algorithm. Artif. Intell. 84(1-2): 177-208 (1996)

Russell Greiner, Ramana Isukapalli: Learning to select useful landmarks. IEEE Transactions on Systems, Man, and Cybernetics, Part B 26(3): 437-449 (1996)

Russell Greiner, Adam J. Grove, Dan Roth: Learning Active Classifiers. ICML 1996: 207-215

Russell Greiner, Adam J. Grove, Alexander Kogan: Exploiting the Omission of Irrelevant Data. ICML 1996: 216-224

Dale Schuurmans, Russell Greiner: Sequential PAC Learning. COLT 1995: 377-384

Russell Greiner: The Challenge of Revising an Impure Theory. ICML 1995: 269-277

Russell Greiner: The Complexity of Theory Revision. IJCAI 1995: 1162-1168

Dale Schuurmans, Russell Greiner: Practical PAC Learning. IJCAI 1995: 1169-1177

Russell Greiner, Ramana Isukapalli: Learning to Select Useful Landmarks. AAAI 1994: 1251-1256

Charles Elkan, Russell Greiner: D. B. Lenat and R. V. Guha, Building Large Knowledge-Based Systems: Representation and Inference in the Cyc Project. Artif. Intell. 61(1): 41-52 (1993)

Russell Greiner, Igor Jurisica: A Statistical Approach to Solving the EBL Utility Problem. AAAI 1992: 241-248

Russell Greiner, Dale Schuurmans: Learning an Optimally Accurate Representation System. ECAI Workshop on Knowledge Representation and Reasoning 1992: 145-159

Russell Greiner, Dale Schuurmans: Learning Useful Horn Approximations. KR 1992: 383-392

Russell Greiner: Learning Efficient Query Processing Strategies. PODS 1992: 33-46

Russell Greiner: Finding Optimal Derivation Strategies in Redundant Knowledge Bases. Artif. Intell. 50(1): 95-115 (1991)

Russell Greiner, Charles Elkan: Measuring and Improving the Effectiveness of Representations. IJCAI 1991: 518-524

Russell Greiner, Pekka Orponen: Probably Approximately Optimal Derivation Strategies. KR 1991: 277-288

Pekka Orponen, Russell Greiner: On the Sample Complexity of Finding Good Search Strategies. COLT 1990: 352-358

Russell Greiner, Barbara A. Smith, Ralph W. Wilkerson: A Correction to the Algorithm in Reiter's Theory of Diagnosis. Artif. Intell. 41(1): 79-88 (1989)

Russell Greiner: Towards a Formal Analysis of EBL. ML 1989: 450-453

Russell Greiner, J. Likuski: Incorporating Redundant Learned Rules: A Preliminary Formal Analysis of EBL. IJCAI 1989: 744-749

Russell Greiner: Learning by Understanding Analogies. Artif. Intell. 35(1): 81-125 (1988)

Russell Greiner: Against the unjustified use of probabilities. Computational Intelligence 4: 79-83 (1988)

Russell Greiner: A Review of Machine Learning at AAAI-87. Machine Learning 3: 79-92 (1988)

Russell Greiner, Michael R. Genesereth: What's New? A Semantic Definition of Novelty. IJCAI 1983: 450-454

Russell Greiner, Douglas B. Lenat: A Representation Language Language. AAAI 1980: 165-169