Interactive Script Instantiation. - Michael J. Pazzani (AAAI, 1983)

Knowledge Based Question Answering. - Michael J. Pazzani; Carl Engelman (ANLP, 1983)

Conceptual Analysis of Garden-Path Sentences. - Michael J. Pazzani (COLING, 1984)

Word-Meaning Selection in Multiprocess Language Understanding Programs. - Richard E. Cullingford; Michael J. Pazzani (IEEE Trans. Pattern Anal. Mach. Intell., 1984)

Refining the Knowledge Base of a Diagnostic Expert System: An Application of Failure-Driven Learning. - Michael J. Pazzani (AAAI, 1986)

The Role of Prior Causal Theories in Generalization. - Michael J. Pazzani; Michael G. Dyer; Margot Flowers (AAAI, 1986)

A Comparison of Concept Identification in Human Learning and Network Learning with the Generalized Delta Rule. - Michael J. Pazzani; Michael G. Dyer (IJCAI, 1987)

Using Prior Learning to Facilitate the Learning of New Causal Theories. - Michael J. Pazzani; Michael G. Dyer; Margot Flowers (IJCAI, 1987)

Creating High Level Knowledge Structures from Simple Elements. - Michael J. Pazzani (Knowledge Representation and Organization in Machine Learning, 1987)

Explanation-Based Learning for Knowledge-Based Systems. - Michael J. Pazzani (Int. J. Man Mach. Stud., 1987)

Failure-Driven Learning of Fault Diagnosis Heuristics. - Michael J. Pazzani (IEEE Trans. Syst. Man Cybern., 1987)

Integrating Explanation-Based and Empirical Learning Methods in OCCAM. - Michael J. Pazzani (EWSL, 1988)

Integrated Learning with Incorrect and Incomplete Theories. - Michael J. Pazzani (ML, 1988)

Explanation-Based Learning with Week Domain Theories. - Michael J. Pazzani (ML, 1989)

One-Sided Algorithms for Integrating Empirical and Explanation-Based Learning. - Wendy Sarrett; Michael J. Pazzani (ML, 1989)

Detecting and Correcting Errors of Omission After Explanation-Based Learning. - Michael J. Pazzani (IJCAI, 1989)

Creating a memory of causal relationships - an integration of empirical and explanation-based learning methods. - Michael J. Pazzani (Lawrence Erlbaum, 1990)

Average Case Analysis of Conjunctive Learning Algorithms. - Michael J. Pazzani; Wendy Sarrett (ML, 1990)

An Investigation of Noise-Tolerant Relational Concept Learning Algorithms. - Clifford Brunk; Michael J. Pazzani (ML, 1991)

Constructive Induction of M-of-N Terms. - Patrick M. Murphy; Michael J. Pazzani (ML, 1991)

A Knowledge-intensive Approach to Learning Relational Concepts. - Michael J. Pazzani; Clifford Brunk; Glenn Silverstein (ML, 1991)

Relational Clichés: Constraining Induction During Relational Learning. - Glenn Silverstein; Michael J. Pazzani (ML, 1991)

A Computational Theory of Learning Causal Relationships. - Michael J. Pazzani (Cogn. Sci., 1991)

Detecting and correcting errors in rule-based expert systems: an integration of empirical and explanation-based learning. - Michael J. Pazzani; Clifford Brunk (Knowl. Acquis., 1991)

Average Case Analysis of Learning kappa-CNF Concepts. - Daniel S. Hirschberg; Michael J. Pazzani (ML, 1992)

The Utility of Knowledge in Inductive Learning. - Michael J. Pazzani; Dennis F. Kibler (Mach. Learn., 1992)

A Framework for Average Case Analysis of Conjunctive Learning Algorithms. - Michael J. Pazzani; Wendy Sarrett (Mach. Learn., 1992)

Finding Accurate Frontiers: A Knowledge-Intensive Approach to Relational Learning. - Michael J. Pazzani; Clifford Brunk (AAAI, 1993)

HYDRA: A Noise-tolerant Relational Concept Learning Algorithm. - Kamal M. Ali; Michael J. Pazzani (IJCAI, 1993)

A Methodology for Evaluating Theory Revision Systems: Results with Audrey II. - James Wogulis; Michael J. Pazzani (IJCAI, 1993)

A Reply to Cohen's Book Review of Creating a Memory of Causal Relationships. - Michael J. Pazzani (Mach. Learn., 1993)

Learning Causal Patterns: Making a Transition from Data-Driven to Theory-Driven Learning. - Michael J. Pazzani (Mach. Learn., 1993)

Revision of Production System Rule-Bases. - Patrick M. Murphy; Michael J. Pazzani (ICML, 1994)

Reducing Misclassification Costs. - Michael J. Pazzani; Christopher J. Merz; Patrick M. Murphy; Kamal M. Ali; Timothy Hume; Clifford Brunk (ICML, 1994)

On Learning Multiple Descriptions of a Concept. - Kamal M. Ali; Clifford Brunk; Michael J. Pazzani (ICTAI, 1994)

Parameter Tuning for the MAX Expert System. - Christopher J. Merz; Michael J. Pazzani (ICTAI, 1994)

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 (LOPSTR, 1994)

Exploring the Decision Forest: An Empirical Investigation of Occam's Razor in Decision Tree Induction. - Patrick M. Murphy; Michael J. Pazzani (J. Artif. Intell. Res., 1994)

Guest Editor's Introduction. - Michael J. Pazzani (Mach. Learn., 1994)

Classification Using Bayes Averaging of Multiple, Relational Rule-based Models. - Kamal M. Ali; Michael J. Pazzani (AISTATS, 1995)

Searching for Dependencies in Bayesian Classifiers. - Michael J. Pazzani (AISTATS, 1995)

A Lexical Based Semantic Bias for Theory Revision. - Clifford Brunk; Michael J. Pazzani (ICML, 1995)

Learning Hierarchies from Ambiguous Natural Language Data. - Takefumi Yamazaki; Michael J. Pazzani; Christopher J. Merz (ICML, 1995)

Learning from hotlists and coldlists: towards a WWW information filtering and seeking agent. - Michael J. Pazzani; Larry Nguyen; Stefanus Mantik (ICTAI, 1995)

Acquiring and updating hierarchical knowledge for machine translation based on a clustering technique. - Takefumi Yamazaki; Michael J. Pazzani; Christopher J. Merz (Learning for Natural Language Processing, 1995)

An Iterative Improvement Approach for the Discretization of Numeric Attributes in Bayesian Classifiers. - Michael J. Pazzani (KDD, 1995)

Hydra-mm: Learning Multiple Descriptions to Improve Classification Accuracy. - Kamal M. Ali; Michael J. Pazzani (Int. J. Artif. Intell. Tools, 1995)

Simple Bayesian Classifiers Do Not Assume Independence. - Pedro M. Domingos; Michael J. Pazzani (AAAI/IAAI, Vol. 2, 1996)

Syskill & Webert: Identifying Interesting Web Sites. - Michael J. Pazzani; Jack Muramatsu; Daniel Billsus (AAAI/IAAI, Vol. 1, 1996)

Do-I-Care: a collaborative Web agent. - Brian Starr; Mark S. Ackerman; Michael J. Pazzani (CHI Conference Companion, 1996)

Beyond Independence: Conditions for the Optimality of the Simple Bayesian Classifier. - Pedro M. Domingos; Michael J. Pazzani (ICML, 1996)

Combining Neural Network Regression Estimates with Regularized Linear Weights. - Christopher J. Merz; Michael J. Pazzani (NIPS, 1996)

Tuning Numeric Parameters to Troubleshoot a Telephone-Network Loop. - Christopher J. Merz; Michael J. Pazzani; Andrea Pohoreckyj Danyluk (IEEE Expert, 1996)

Error Reduction through Learning Multiple Descriptions. - Kamal M. Ali; Michael J. Pazzani (Mach. Learn., 1996)

Review of "Inductive Logic Programming: Techniques and Applications" by Nada Lavrac, Saso Dzeroski. - Michael J. Pazzani (Mach. Learn., 1996)

Knowledge Discovery from a Breast Cancer Database. - Subramani Mani; Michael J. Pazzani; John West (AIME, 1997)

Detecting Very Early Stages of Dementia from Normal Aging with Machine Learning Methods. - William Rodman Shankle; Subramani Mani; Michael J. Pazzani; Padhraic Smyth (AIME, 1997)

Combining Neural Network Regression Estimates Using Principal Components. - Christopher J. Merz; Michael J. Pazzani (AISTATS, 1997)

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 (AMIA, 1997)

Beyond Concise and Colorful: Learning Intelligible Rules. - Michael J. Pazzani; Subramani Mani; William Rodman Shankle (KDD, 1997)

The Do-I-Care Agent: Effective Social Discovery and Filtering on the Web. - Mark S. Ackerman; Brian Starr; Michael J. Pazzani (RIAO, 1997)

Learning Probabilistic User Profiles: Applications for Finding Interesting Web Sites, Notifying Users of Relevant Changes to Web Pages, and Locating Grant Opportunities. - Mark S. Ackerman; Daniel Billsus; Scott Gaffney; Seth Hettich; Gordon Khoo; Dong Joon Kim; Raymond Klefstad; Charles Lowe; Alexius Ludeman; Jack Muramatsu; Kazuo Omori; Michael J. Pazzani; et al. (AI Mag., 1997)

On the Optimality of the Simple Bayesian Classifier under Zero-One Loss. - Pedro M. Domingos; Michael J. Pazzani (Mach. Learn., 1997)

Learning and Revising User Profiles: The Identification of Interesting Web Sites. - Michael J. Pazzani; Daniel Billsus (Mach. Learn., 1997)

Guideline generation from data by induction of decision tables using a Bayesian network framework. - Subramani Mani; Michael J. Pazzani (AMIA, 1998)

Adjusted Probability Naive Bayesian Induction. - Geoffrey I. Webb; Michael J. Pazzani (Australian Joint Conference on Artificial Intelligence, 1998)

Learning Collaborative Information Filters. - Daniel Billsus; Michael J. Pazzani (ICML, 1998)

An Enhanced Representation of Time Series Which Allows Fast and Accurate Classification, Clustering and Relevance Feedback. - Eamonn J. Keogh; Michael J. Pazzani (KDD, 1998)

Simple Models for Estimating Dementia Severity Using Machine Learning. - William Rodman Shankle; Subramani Mani; Malcolm B. Dick; Michael J. Pazzani (MedInfo, 1998)

Syskill and Webert Web Page Ratings. - Michael J. Pazzani (UCI Machine Learning Repository, 1998)

A Personal News Agent That Talks, Learns and Explains. - Daniel Billsus; Michael J. Pazzani (Agents, 1999)

Refinement of Neuro-psychological Tests for Dementia Screening in a Cross Cultural Population Using Machine Learning. - Subramani Mani; Malcolm B. Dick; Michael J. Pazzani; Evelyn L. Teng; Daniel Kempler; I. Maribell Taussig (AIMDM, 1999)

Learning augmented Bayesian classifiers: A comparison of distribution-based and classification-based approaches. - Eamonn J. Keogh; Michael J. Pazzani (AISTATS, 1999)

Detecting Change in Categorical Data: Mining Contrast Sets. - Stephen D. Bay; Michael J. Pazzani (KDD, 1999)

Scaling up Dynamic Time Warping to Massive Dataset. - Eamonn J. Keogh; Michael J. Pazzani (PKDD, 1999)

Relevance Feedback Retrieval of Time Series Data. - Eamonn J. Keogh; Michael J. Pazzani (SIGIR, 1999)

An Indexing Scheme for Fast Similarity Search in Large Time Series Databases. - Eamonn J. Keogh; Michael J. Pazzani (SSDBM, 1999)

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 (AI Mag., 1999)

A Framework for Collaborative, Content-Based and Demographic Filtering. - Michael J. Pazzani (Artif. Intell. Rev., 1999)

Two-Stage Machine Learning model for guideline development. - Subramani Mani; William Rodman Shankle; Malcolm B. Dick; Michael J. Pazzani (Artif. Intell. Medicine, 1999)

Combinatorial Optimization in Rapidly Mutating Drug-Resistant Viruses. - Richard H. Lathrop; Michael J. Pazzani (J. Comb. Optim., 1999)

A Principal Components Approach to Combining Regression Estimates. - Christopher J. Merz; Michael J. Pazzani (Mach. Learn., 1999)

Workshop on Recommender Systems: Algorithms and Evaluation. - Ian Soboroff; Charles K. Nicholas; Michael J. Pazzani (SIGIR Forum, 1999)

Pseudo Periodic Synthetic Time Series. - Eamonn J. Keogh; Michael J. Pazzani (UCI Machine Learning Repository, 1999)

Characterizing Model Erros and Differences. - Stephen D. Bay; Michael J. Pazzani (ICML, 2000)

A learning agent for wireless news access. - Daniel Billsus; Michael J. Pazzani; James Chen (IUI, 2000)

Representation of electronic mail filtering profiles: a user study. - Michael J. Pazzani (IUI, 2000)

Scaling up dynamic time warping for datamining applications. - Eamonn J. Keogh; Michael J. Pazzani (KDD, 2000)

A Simple Dimensionality Reduction Technique for Fast Similarity Search in Large Time Series Databases. - Eamonn J. Keogh; Michael J. Pazzani (PAKDD, 2000)

Collaborative Filtering with the Simple Bayesian Classifier. - Koji Miyahara; Michael J. Pazzani (PRICAI, 2000)

Knowledge discovery from data? - Michael J. Pazzani (IEEE Intell. Syst., 2000)

Learning with Globally Predictive Tests. - Michael J. Pazzani (New Gener. Comput., 2000)

The UCI KDD Archive of Large Data Sets for Data Mining Research and Experimentation. - Stephen D. Bay; Dennis F. Kibler; Michael J. Pazzani; Padhraic Smyth (SIGKDD Explor., 2000)

User Modeling for Adaptive News Access. - Daniel Billsus; Michael J. Pazzani (User Model. User Adapt. Interact., 2000)

An Online Algorithm for Segmenting Time Series. - Eamonn J. Keogh; Selina Chu; David M. Hart; Michael J. Pazzani (ICDM, 2001)

Ensemble-index: a new approach to indexing large databases. - Eamonn J. Keogh; Selina Chu; Michael J. Pazzani (KDD, 2001)

Derivative Dynamic Time Warping. - Eamonn J. Keogh; Michael J. Pazzani (SDM, 2001)

Improving mobile internet usability. - George Buchanan 001; Sarah Farrant; Matt Jones 001; Harold W. Thimbleby; Gary Marsden; Michael J. Pazzani (WWW, 2001)

Detecting Group Differences: Mining Contrast Sets. - Stephen D. Bay; Michael J. Pazzani (Data Min. Knowl. Discov., 2001)

Dimensionality Reduction for Fast Similarity Search in Large Time Series Databases. - Eamonn J. Keogh; Kaushik Chakrabarti; Michael J. Pazzani; Sharad Mehrotra (Knowl. Inf. Syst., 2001)

Machine Learning for User Modeling. - Geoffrey I. Webb; Michael J. Pazzani; Daniel Billsus (User Model. User Adapt. Interact., 2001)

Commercial Applications of Machine Learning for Personalized Wireless Portals. - Michael J. Pazzani (PRICAI, 2002)

Iterative Deepening Dynamic Time Warping for Time Series. - Selina Chu; Eamonn J. Keogh; David M. Hart; Michael J. Pazzani (SDM, 2002)

Adaptive Web Site Agents. - Michael J. Pazzani; Daniel Billsus (Auton. Agents Multi Agent Syst., 2002)

Adaptive interfaces for ubiquitous web access. - Daniel Billsus; Clifford Brunk; Craig Evans; Brian Gladish; Michael J. Pazzani (Commun. ACM, 2002)

Learning the Structure of Augmented Bayesian Classifiers. - Eamonn J. Keogh; Michael J. Pazzani (Int. J. Artif. Intell. Tools, 2002)

Locally adaptive dimensionality reduction for indexing large time series databases. - Kaushik Chakrabarti; Eamonn J. Keogh; Sharad Mehrotra; Michael J. Pazzani (ACM Trans. Database Syst., 2002)

Adaptive Interfaces for Ubiquitous Web Access. - Michael J. Pazzani (User Modeling, 2003)

NSF Research Award Abstracts 1990-2003. - Michael J. Pazzani; Amnon Meyers (UCI Machine Learning Repository, 2003)

Machine Learning for Personalized Wireless Portals. - Michael J. Pazzani (ICTAI, 2004)

The Lowell database research self-assessment. - Serge Abiteboul; Rakesh Agrawal 001; Philip A. Bernstein; Michael J. Carey 001; Stefano Ceri; W. Bruce Croft; David J. DeWitt; Michael J. Franklin; Hector Garcia-Molina; Dieter Gawlick; Jim Gray 001; Laura M. Haas; et al. (Commun. ACM, 2005)

Adaptive Info - Personalizing the Wireless Web: Machine Learning 275 and Business 101. - Michael J. Pazzani (AAAI Spring Symposium: What Went Wrong and Why: Lessons from AI Research and Applications, 2006)

Mining for proposal reviewers: lessons learned at the national science foundation. - Seth Hettich; Michael J. Pazzani (KDD, 2006)

Adaptive News Access. - Daniel Billsus; Michael J. Pazzani (The Adaptive Web, 2007)

Content-Based Recommendation Systems. - Michael J. Pazzani; Daniel Billsus (The Adaptive Web, 2007)

An energy-efficient mobile recommender system. - Yong Ge; Hui Xiong 001; Alexander Tuzhilin; Keli Xiao; Marco Gruteser; Michael J. Pazzani (KDD, 2010)

Active learning using on-line algorithms. - Chris Mesterharm; Michael J. Pazzani (KDD, 2011)

Farm Ads. - Chris Mesterharm; Michael J. Pazzani (UCI Machine Learning Repository, 2011)

Energy-Efficient Integration of Continuous Context Sensing and Prediction into Smartwatches. - Reza Rawassizadeh; Martin Tomitsch; Manouchehr Nourizadeh; Elaheh Momeni; Aaron Peery; Liudmila Ulanova; Michael J. Pazzani (Sensors, 2015)

Scalable Daily Human Behavioral Pattern Mining from Multivariate Temporal Data. - Reza Rawassizadeh; Elaheh Momeni; Chelsea Dobbins; Joobin Gharibshah; Michael J. Pazzani (IEEE Trans. Knowl. Data Eng., 2016)

A natural language query interface for searching personal information on smartwatches. - Reza Rawassizadeh; Chelsea Dobbins; Manouchehr Nourizadeh; Zahra Ghamchili; Michael J. Pazzani (PerCom Workshops, 2017)

Explaining Contrasting Categories. - Michael J. Pazzani; Amir Feghahati; Christian R. Shelton; Aaron R. Seitz (IUI Workshops, 2018)

Manifestation of virtual assistants and robots into daily life: vision and challenges. - Reza Rawassizadeh; Taylan K. Sen; Sunny Jung Kim; Christian Meurisch; Hamidreza Keshavarz; Max Mühlhäuser; Michael J. Pazzani (CCF Trans. Pervasive Comput. Interact., 2019)

Indexing Multivariate Mobile Data through Spatio-Temporal Event Detection and Clustering. - Reza Rawassizadeh; Chelsea Dobbins; Mohammad Akbari 001; Michael J. Pazzani (Sensors, 2019)

CDeepEx: Contrastive Deep Explanations. - Amir Feghahati; Christian R. Shelton; Michael J. Pazzani; Kevin Tang (ECAI, 2020)

Ghost Imputation: Accurately Reconstructing Missing Data of the Off Period. - Reza Rawassizadeh; Hamidreza Keshavarz; Michael J. Pazzani (IEEE Trans. Knowl. Data Eng., 2020)

A Comprehensive Explanation Framework for Biomedical Time Series Classification. - Praharsh Ivaturi; Matteo Gadaleta; Amitabh C. Pandey; Michael J. Pazzani; Steven R. Steinhubl; Giorgio Quer (IEEE J. Biomed. Health Informatics, 2021)

Expert-Informed, User-Centric Explanations for Machine Learning. - Michael J. Pazzani; Severine Soltani; Robert A. Kaufman; Samson Qian; Albert Hsiao (AAAI, 2022)

User-Centric Enhancements to Explainable AI Algorithms for Image Classification. - Severine Soltani; Robert A. Kaufman; Michael J. Pazzani (CogSci, 2022)

Deep Learning Radiographic Assessment of Pulmonary Edema: Training with Serum Biomarkers. - Justin Huynh; Samira Masoudi; Abraham Noorbakhsh; Kyle Hasenstab; Michael J. Pazzani; Albert Hsiao (MIDL, 2022)

Deep Learning Radiographic Assessment of Pulmonary Edema: Optimizing Clinical Performance, Training With Serum Biomarkers. - Justin Huynh; Samira Masoudi; Abraham Noorbakhsh; Amin Mahmoodi; Seth Kligerman; Andrew Yen; Kathleen Jacobs; Lewis Hahn; Kyle Hasenstab; Michael J. Pazzani; Albert Hsiao (IEEE Access, 2022)

Explaining Image Classifiers Using Contrastive Counterfactuals in Generative Latent Spaces. - Kamran Alipour; Aditya Lahiri; Ehsan Adeli 001; Babak Salimi; Michael J. Pazzani (CoRR, 2022)

Feature Interpretation Using Generative Adversarial Networks (FIGAN): A Framework for Visualizing a CNN's Learned Features. - Kyle Hasenstab; Justin Huynh; Samira Masoudi; Guilherme M. Cunha; Michael J. Pazzani; Albert Hsiao (IEEE Access, 2023)