| 2022 | AAAI | Expert-Informed, User-Centric Explanations for Machine Learning. | Michael J. Pazzani, Severine Soltani, Robert Kaufman, Samson Qian, Albert Hsiao |
| 2022 | CogSci | User-Centric Enhancements to Explainable AI Algorithms for Image Classification. | Severine Soltani, Robert Kaufman, Michael J. Pazzani |
| 2020 | ECAI | CDeepEx: Contrastive Deep Explanations. | Amir Feghahati, Christian R. Shelton, Michael J. Pazzani, Kevin Tang |
| 2018 | IUI | Explaining Contrasting Categories. | Michael J. Pazzani, Amir Feghahati, Christian R. Shelton, Aaron R. Seitz |
| 2017 | PERCOM | A natural language query interface for searching personal information on smartwatches. | Reza Rawassizadeh, Chelsea Dobbins, Manouchehr Nourizadeh, Zahra Ghamchili, Michael J. Pazzani |
| 2011 | KDD | Active learning using on-line algorithms. | Chris Mesterharm, Michael J. Pazzani |
| 2010 | KDD | An energy-efficient mobile recommender system. | Yong Ge, Hui Xiong, Alexander Tuzhilin, Keli Xiao, Marco Gruteser, Michael J. Pazzani |
| 2006 | KDD | Mining for proposal reviewers: lessons learned at the national science foundation. | Seth Hettich, Michael J. Pazzani |
| 2004 | ICTAI | Machine Learning for Personalized Wireless Portals. | Michael J. Pazzani |
| 2002 | PRICAI | Commercial Applications of Machine Learning for Personalized Wireless Portals. | Michael J. Pazzani |
| 2002 | SDM | Iterative Deepening Dynamic Time Warping for Time Series. | Selina Chu, Eamonn J. Keogh, David M. Hart, Michael J. Pazzani |
| 2001 | ICDM | An Online Algorithm for Segmenting Time Series. | Eamonn J. Keogh, Selina Chu, David M. Hart, Michael J. Pazzani |
| 2001 | KDD | Ensemble-index: a new approach to indexing large databases. | Eamonn J. Keogh, Selina Chu, Michael J. Pazzani |
| 2001 | WWW | Improving mobile internet usability. | George Buchanan, Sarah Farrant, Matt Jones, Harold W. Thimbleby, Gary Marsden, Michael J. Pazzani |
| 2001 | SIGMOD | Locally Adaptive Dimensionality Reduction for Indexing Large Time Series Databases. | Eamonn J. Keogh, Kaushik Chakrabarti, Sharad Mehrotra, Michael J. Pazzani |
| 2001 | SDM | Derivative Dynamic Time Warping. | Eamonn J. Keogh, Michael J. Pazzani |
| 2000 | ICML | Characterizing Model Erros and Differences. | Stephen D. Bay, Michael J. Pazzani |
| 2000 | IUI | A learning agent for wireless news access. | Daniel Billsus, Michael J. Pazzani, James Chen |
| 2000 | IUI | Representation of electronic mail filtering profiles: a user study. | Michael J. Pazzani |
| 2000 | KDD | Scaling up dynamic time warping for datamining applications. | Eamonn J. Keogh, Michael J. Pazzani |
| 2000 | PAKDD | A Simple Dimensionality Reduction Technique for Fast Similarity Search in Large Time Series Databases. | Eamonn J. Keogh, Michael J. Pazzani |
| 2000 | PRICAI | Collaborative Filtering with the Simple Bayesian Classifier. | Koji Miyahara, Michael J. Pazzani |
| 1999 | AISTATS | Learning augmented Bayesian classifiers: A comparison of distribution-based and classification-based approaches. | Eamonn J. Keogh, Michael J. Pazzani |
| 1999 | KDD | Detecting Change in Categorical Data: Mining Contrast Sets. | Stephen D. Bay, Michael J. Pazzani |
| 1999 | SIGIR | Relevance Feedback Retrieval of Time Series Data. | Eamonn J. Keogh, Michael J. Pazzani |
| 1999 | SSDBM | An Indexing Scheme for Fast Similarity Search in Large Time Series Databases. | Eamonn J. Keogh, Michael J. Pazzani |
| 1998 | AAAI | Knowledge-Based Avoidance of Drug-Resistant HIV Mutants. | Richard H. Lathrop, Nicholas R. Steffen, Miriam P. Raphael, Sophia Deeds-Rubin, Michael J. Pazzani, Paul J. Cimoch, Darryl M. See, Jeremiah G. Tilles |
| 1998 | AMIA | Guideline generation from data by induction of decision tables using a Bayesian network framework. | Subramani Mani, Michael J. Pazzani |
| 1998 | DIS | Learning with Globally Predictive Tests. | Michael J. Pazzani |
| 1998 | ICML | Learning Collaborative Information Filters. | Daniel Billsus, Michael J. Pazzani |
| 1998 | KDD | An Enhanced Representation of Time Series Which Allows Fast and Accurate Classification, Clustering and Relevance Feedback. | Eamonn J. Keogh, Michael J. Pazzani |
| 1997 | AIME | Knowledge Discovery from a Breast Cancer Database. | Subramani Mani, Michael J. Pazzani, John West |
| 1997 | AIME | Detecting Very Early Stages of Dementia from Normal Aging with Machine Learning Methods. | William Rodman Shankle, Subramani Mani, Michael J. Pazzani, Padhraic Smyth |
| 1997 | AISTATS | Combining Neural Network Regression Estimates Using Principal Components. | Christopher J. Merz, Michael J. Pazzani |
| 1997 | AMIA | Differential Diagnosis of Dementia: A Knowledge Discovery and Data Mining (KDD) Approach. | Subramani Mani, William Rodman Shankle, Michael J. Pazzani, Padhraic Smyth, Malcolm B. Dick |
| 1997 | KDD | Beyond Concise and Colorful: Learning Intelligible Rules. | Michael J. Pazzani, Subramani Mani, William Rodman Shankle |
| 1996 | AAAI | Simple Bayesian Classifiers Do Not Assume Independence. | Pedro M. Domingos, Michael J. Pazzani |
| 1996 | AAAI | Syskill & Webert: Identifying Interesting Web Sites. | Michael J. Pazzani, Jack Muramatsu, Daniel Billsus |
| 1996 | CHI | Do-I-Care: a collaborative Web agent. | Brian Starr, Mark S. Ackerman, Michael J. Pazzani |
| 1996 | ICML | Beyond Independence: Conditions for the Optimality of the Simple Bayesian Classifier. | Pedro M. Domingos, Michael J. Pazzani |
| 1995 | AISTATS | Classification Using Bayes Averaging of Multiple, Relational Rule-based Models. | Kamal M. Ali, Michael J. Pazzani |
| 1995 | AISTATS | Searching for Dependencies in Bayesian Classifiers. | Michael J. Pazzani |
| 1995 | ICML | A Lexical Based Semantic Bias for Theory Revision. | Clifford Brunk, Michael J. Pazzani |
| 1995 | ICML | Learning Hierarchies from Ambiguous Natural Language Data. | Takefumi Yamazaki, Michael J. Pazzani, Christopher J. Merz |
| 1995 | IJCAI | Acquiring and updating hierarchical knowledge for machine translation based on a clustering technique. | Takefumi Yamazaki, Michael J. Pazzani, Christopher J. Merz |
| 1995 | ICTAI | Learning from hotlists and coldlists: towards a WWW information filtering and seeking agent. | Michael J. Pazzani, Larry Nguyen, Stefanus Mantik |
| 1995 | KDD | An Iterative Improvement Approach for the Discretization of Numeric Attributes in Bayesian Classifiers. | Michael J. Pazzani |
| 1994 | ICML | Revision of Production System Rule-Bases. | Patrick M. Murphy, Michael J. Pazzani |
| 1994 | ICML | Reducing Misclassification Costs. | Michael J. Pazzani, Christopher J. Merz, Patrick M. Murphy, Kamal M. Ali, Timothy Hume, Clifford Brunk |
| 1994 | ICTAI | On Learning Multiple Descriptions of a Concept. | Kamal M. Ali, Clifford Brunk, Michael J. Pazzani |
| 1994 | ICTAI | Parameter Tuning for the MAX Expert System. | Christopher J. Merz, Michael J. Pazzani |
| 1994 | LOPSTR | Avoiding Non-Termination when Learning Logical Programs: A Case Study with FOIL and FOCL. | Giovanni Semeraro, Floriana Esposito, Donato Malerba, Clifford Brunk, Michael J. Pazzani |
| 1993 | AAAI | Finding Accurate Frontiers: A Knowledge-Intensive Approach to Relational Learning. | Michael J. Pazzani, Clifford Brunk |
| 1993 | IJCAI | HYDRA: A Noise-tolerant Relational Concept Learning Algorithm. | Kamal M. Ali, Michael J. Pazzani |
| 1993 | IJCAI | A Methodology for Evaluating Theory Revision Systems: Results with Audrey II. | James Wogulis, Michael J. Pazzani |
| 1992 | ICML | Average Case Analysis of Learning kappa-CNF Concepts. | Daniel S. Hirschberg, Michael J. Pazzani |
| 1991 | ICML | An Investigation of Noise-Tolerant Relational Concept Learning Algorithms. | Clifford Brunk, Michael J. Pazzani |
| 1991 | ICML | Constructive Induction of M-of-N Terms. | Patrick M. Murphy, Michael J. Pazzani |
| 1991 | ICML | A Knowledge-intensive Approach to Learning Relational Concepts. | Michael J. Pazzani, Clifford Brunk, Glenn Silverstein |
| 1991 | ICML | Relational Clichs: Constraining Induction During Relational Learning. | Glenn Silverstein, Michael J. Pazzani |
| 1990 | ICML | Average Case Analysis of Conjunctive Learning Algorithms. | Michael J. Pazzani, Wendy Sarrett |
| 1989 | ICML | Explanation-Based Learning with Week Domain Theories. | Michael J. Pazzani |
| 1989 | ICML | One-Sided Algorithms for Integrating Empirical and Explanation-Based Learning. | Wendy Sarrett, Michael J. Pazzani |
| 1989 | IJCAI | Detecting and Correcting Errors of Omission After Explanation-Based Learning. | Michael J. Pazzani |
| 1988 | ICML | Integrated Learning with Incorrect and Incomplete Theories. | Michael J. Pazzani |
| 1987 | IJCAI | A Comparison of Concept Identification in Human Learning and Network Learning with the Generalized Delta Rule. | Michael J. Pazzani, Michael G. Dyer |
| 1987 | IJCAI | Using Prior Learning to Facilitate the Learning of New Causal Theories. | Michael J. Pazzani, Michael G. Dyer, Margot Flowers |
| 1986 | AAAI | Refining the Knowledge Base of a Diagnostic Expert System: An Application of Failure-Driven Learning. | Michael J. Pazzani |
| 1986 | AAAI | The Role of Prior Causal Theories in Generalization. | Michael J. Pazzani, Michael G. Dyer, Margot Flowers |
| 1984 | COLING | Conceptual Analysis of Garden-Path Sentences. | Michael J. Pazzani |
| 1983 | AAAI | Interactive Script Instantiation. | Michael J. Pazzani |