Selected Publications of Max Chickering

Chickering (2020). Statistically Efficient Greedy Equivalence Search. In Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, Toronto, Canada. [Bib Entry]
Bennett, Chickering, Meek and Zhu (2017). Algorithms for Active Classifier Selection: Maximizing Recall with Precision Constraints. In Proceedings of the 10th ACM International Conference on Web Search and Data Mining (WSDM 2017), Cambridge, UK, pages 711-719. [Bib Entry]
Jandot, Simard, Chickering, Grangier and Suh (2016). Interactive Semantic Featuring for Text Classification. In Proceedings of the 2016 ICML Workshop on Human Interpretability in Machine Learning (WHI 2016), New York, NY, pages 51-55. [Bib Entry]
Chickering and Meek (2015). Selective Greedy Equivalence Search: Finding Optimal Bayesian Networks Using a Polynomial Number of Score Evaluations. In Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence, Amsterdam, Netherlands, pages 211-219. [Bib Entry]
Amershi, Chickering, Drucker, Lee, Simard and Suh (2015). ModelTracker: Redesigning Performance Analysis Tools for Machine Learning. In Proceedings of the Conference on Human Factors in Computing Systems (CHI 2015), Seoul, Korea, pages 337-346. [Bib Entry]
Azari Soufiani, Chickering, Charles and Parkes (2014). Approximating the Shapley Value via Multi-Issue Decomposition. In Proceedings of the Thirteenth International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2014), Paris, France, pages 1209-1216.
Charles, Chakrabarty, Chickering, Devanur and Wang (2013). Budget Smoothing for Internet Ad Auctions: A Game Theoretic Approach. In Proceedings of the ACM Conference on Electronic Commerce (EC13), Philadelphia, PA, pages 163-180.
Bottou, Peters, Quinonero-Candela, Charles, Chickering, Portugaly, Ray, Simard and Snelson (2013). Counterfactual Reasoning and Learning Systems: the Example of Computational Advertising. Journal of Machine Learning Research, 14:3207-3260
Paek, Gamon, Counts, Chickering and Dhesi (2010). Predicting the Importance of Newsfeed Posts and Social Network Friends . In Proceedings of the Twenty Fourth National Conference on Artificial Intelligence (AAAI 2010), Atlanta, GA, pages 1419-1424. [Bib Entry]
Seuken, Charles, Chickering and Puri (2010). Market Design and Analysis for a P2P Backup System. In Proceedings of the ACM Conference on Electronic Commerce (EC10), Boston, MA, pages 97-108. [Bib Entry]
Charles, Chickering, Devanur, Jain and Sanghi (2010). Matchings in Lopsided Bipartite Graphs with Applications to Display Ads. In Proceedings of the ACM Conference on Electronic Commerce (EC10), Boston, MA, pages 121-128. [Bib Entry]

Bennett, Chickering and Mityagin (2009). Picture This: Preferences for Image Search. In Proceedings of the ACM SIGKDD Workshop on Human Computation (HCOMP09), Paris, France, pages 25-26. [Bib entry]
Bennett, Chickering and Mityagin (2009). Learning Consensus Opinion: Mining Data from a Labeling Game. In Proceedings of the Eighteenth International World Wide Web Conference (WWW09), Madrid, Spain, pages 121-130. [Bib Entry]
Law, Mityagin and Chickering (2009). Intentions: A Game for Classifying Search Query Intent. In Extended Abstracts on Human Factors in Computing Systems (CHI EA 09), Boston, MA, pages 3805-3810. [Bib Entry]
Linden, Meek and Chickering (2009). The Pollution Effect: Optimizing Keyword Auctions by Favoring Relevant Advertising. In Fifth Workshop on Ad Auctions, [Bib Entry]

Engel and Chickering (2008). Incorporating User Utility Into Sponsored-Search Auctions. In Proceedings of the Seventh International Conference on Autonomous Agents and Multiagent Systems, Estoril, Portugal, pages 1565-1568. [Bib entry]
Shani, Chickering and Meek (2008), Mining Recommendations from the web. In Proceedings of the 2008 ACM Conference on Recommender Systems (RecSys 08), New York, NY, pages 35-42. [Bib Entry]
Carterette, Bennett Chickering, and Dumais (2008), Here or There: Preference Judgments for Relevance, In Proceedings of the 30th European Conference on IR Research (ECIR 2008), Glasgow, UK, pages 16-27. Springer. [Bib entry]
Paek, Gandhe and Chickering (2008). Rapidly deploying grammar-based speech applications with active learning and back-off grammars. In Proceedings of the 9th SIGDIAL Workshop on Discourse and Dialogue, pages 64-67. [Bib entry]

Chickering and Paek (2007). Personalizing Influence Diagrams: Applying Online Learning Strategies to Dialogue Management. User Modeling and User-Adapted Interaction, 17(1-2):71-91. [Bib entry]
Paek and Chickering (2007). Improving Command and Control Speech Recognition on Mobile Devices: Using Predictive User Models for Language Modeling. User Modeling and User-Adapted Interaction, 17(1-2):93-117. [Bib entry]
Paek, Gandhe, Chickering, and Ju (2007). Handling out-of-grammar commands in mobile speech interaction using backoff filler models. In Proceedings of the ACL Workshop on Grammar-Based Approaches to Spoken Language Processing (SPEECHGRAM), pages 33-40. [Bib entry]
Becker, Meek, and Chickering (2007). Modeling Contextual Factors of Click Rates. In Proceedings of the Twenty Second National Conference on Artificial Intelligence (AAAI-2007), Vancouver, BC, pages 1310-1315. [Bib entry]

Chickering and Meek (2006). On the Incompatibility of Faithfulness and Monotone DAG Faithfulness. Artificial Intelligence, 170(8):653-666. [Bib entry]
Paek and Chickering (2006). Evaluating the Markov assumption in Markov decision processes for spoken dialogue management. Language Resources and Evaluation, Special Issue on Data Resources, Evaluation and Dialogue Interaction, 40(1): 47-66. [Bib entry]
Chellapilla and Chickering (2006). Improving Cloaking Detection using Search Query Popularity and Monetizability, In Proceedings of the Second International Workshop on Adversarial Information Retrieval on the Web, Seattle, WA. pages 17-23. [Bib entry]

Paek and Chickering (2005). The Markov assumption in spoken dialogue management. In Proceedings of the 6th SIGDIAL Workshop on Discourse and Dialogue, pages 35-44. [Bib entry]
Meek, Chickering and Wilson (2005), Stochastic and contingent payment auctions, In First Workshop on Sponsored Search Auctions, ACM Electronic Commerce. [Bib entry]

Chickering, Heckerman, and Meek (2004). Large-Sample Learning of Bayesian Networks is NP-Hard. Journal of Machine Learning Research, 5:1287-1330. [Bib entry]
Thiesson, Chickering, Heckerman, and Meek (2004). ARMA Time-Series Modeling With Graphical Models. In Proceedings of the Twentieth Conference on Uncertainty in Artificial Intelligence, Banff, Canada, pages 552-560. AUAI Press. [Bib entry]

Chickering and Heckerman (2003). Targeted Advertising on the Web with Inventory Management. Interfaces, 33(5):71-77. [Bib entry]
Meek and Chickering (2003). Practically Perfect. In Proceedings of Nineteenth Conference on Uncertainty in Artificial Intelligence, Acapulco, Mexico, pages 411-416. Morgan Kaufmann. [Bib entry]
Hulten, Chickering and Heckerman (2003). Learning Bayesian Networks From Dependency Networks: A Preliminary Study. In Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics, Key West, FL. [Bib entry]
Chickering, Meek, and Heckerman (2003). Large-Sample Learning of Bayesian Networks is NP-Hard. In Proceedings of Nineteenth Conference on Uncertainty in Artificial Intelligence, Acapulco, Mexico, pages 124-133. Morgan Kaufmann. [Bib entry]
Chickering and Meek (2003). Monotone DAG Faithfulness: A Bad Assumption. Technical Report MSR-TR-2003-16, Microsoft, Redmond, WA. [Bib entry]

Chickering (2002). Optimal Structure Identification with Greedy Search . Journal of Machine Learning Research, 3:507-554. [Bib entry]
Chickering (2002). Learning Equivalence Classes of Bayesian-Network Structures. Journal of Machine Learning Research, 2:445-498. [Bib entry]
Meek, Chickering and Heckerman (2002). Autoregressive Tree Models for Time-Series Analysis. In Proceedings of the Second International SIAM Conference on Data Mining, Arlington, VA, pages 229-244. [Bib entry]
Chickering and Meek (2002). Finding Optimal Bayesian Networks. In Proceedings of Eighteenth Conference on Uncertainty in Artificial Intelligence, Edmonton, AB, pages 94-102. Morgan Kaufmann. [Bib entry]

Chickering, Meek and Rounthwaite (2001). Efficient Determination of Dynamic Split Points in a Decision Tree. In Proceedings of the 2001 IEEE International Conference on Data Mining, San Jose, CA, pages 91-98. [Bib entry]
Horvitz, Ruan, Gomes, Kautz, Selman and Chickering (2001). A Bayesian Approach to Solving Hard Computational Problems. In Proceedings of the 17th Conference on Uncertainty in Artificial Intelligence, Seattle, WA, pages 235-244. Morgan Kaufmann. [Bib entry]
Zimdars, Chickering and Meek (2001). Using Temporal Data for Making Recommendations. In Proceedings of Seventeenth Conference on Uncertainty in Artificial Intelligence, Seattle, WA, pages 580-588. Morgan Kaufmann. [Bib entry]

Chickering and Heckerman (2000). A Comparison of Scientific and Engineering Criteria for Bayesian Model Selection. Statistics and Computing, 10(1):55-62. [Bib entry]
Heckerman, Chickering, Meek, Rounthwaite and Kadie (2000). Dependency Networks for Inference, Collaborative Filtering, and Data Visualization. Journal of Machine Learning Research, 1:49-75. [Bib entry]
Chickering and Heckerman (2000). Targeted Advertising with Inventory Management. In ACM Special Interest Group on E-Commerce (EC00), Minneapolis, MN, pages 145-149. [Bib entry]
Chickering and Heckerman (2000). A Decision-Theoretic Approach to Targeted Advertising. In Proceedings of Sixteenth Conference on Uncertainty in Artificial Intelligence, Stanford, CA, pages 264-273. Morgan Kaufmann. [Bib entry]
Chickering, Heckerman, Meek, Platt and Thiesson (2000). Goal-oriented clustering. Technical Report MSR-TR-2000-82, Microsoft, Redmond, WA. [Bib entry]

Thiesson, Meek, Chickering, and Heckerman (1999). Computationally efficient methods for selecting among mixtures of graphical models [ps format]. In Bernardo, J., Berger, J., Dawid, A., and Smith, A., editors, Bayesian Statistics 6, pages 631-656. Oxford University Press. [Bib entry]
Chickering and Heckerman (1999). Fast Learning from Sparse Data. In Proceedings of Fifteenth Conference on Uncertainty in Artificial Intelligence, Stockholm, Sweden, pages 109-115. Morgan Kaufmann. [Bib entry]

Thiesson, Meek, Chickering and Heckerman (1998). Learning Mixtures of DAG Models. In Proceedings of Fourteenth Conference on Uncertainty in Artificial Intelligence, Madison, WI, pages 504-513. Morgan Kaufmann. [Bib entry]

Chickering and Heckerman (1997). Efficient approximations for the marginal likelihood of Bayesian networks with hidden variables. Machine Learning, 29:181-212. [Bib entry]
Chickering, Heckerman and Meek (1997). A Bayesian approach to learning Bayesian networks with local structure. In Proceedings of Thirteenth Conference on Uncertainty in Artificial Intelligence, Providence, RI, pages 80-89. Morgan Kaufmann. [Bib entry]

Chickering (1996). Learning Bayesian networks is NP-Complete. In Fisher, D. and Lenz, H., editors, Learning from Data: Artificial Intelligence and Statistics V, pages 121-130. Springer-Verlag. [Bib entry]
Korf and Chickering (1996). Best-First Minimax Search. Artificial Intelligence, 84(1-2):299-337. [Bib entry]
Chickering (1996). Learning equivalence classes of Bayesian network structures. In Proceedings of Twelfth Conference on Uncertainty in Artificial Intelligence, Portland, OR, pages 150-157. Morgan Kaufmann. [Bib entry]
Chickering and Pearl (1996). A clinician's tool for analyzing non-compliance. In Proceedings of the Thirteenth National Conference on Artificial Intelligence (AAAI-96), Portland, OR, volume 2, pages 1269-1276. [Bib entry]
Chickering and Heckerman (1996). Efficient Approximations for the Marginal Likelihood of Incomplete Data Given a Bayesian Network. In Proceedings of Twelfth Conference on Uncertainty in Artificial Intelligence, Portland, OR, pages 158-168. Morgan Kaufmann. [Bib entry]

Heckerman, Geiger and Chickering (1995). Learning Bayesian networks: The Combination of Knowledge and Statistical Data. Machine Learning, 20:197-243. [Bib entry]
Chickering, Geiger and Heckerman (1995). On finding a cycle basis with a shortest maximal cycle. Information Processing Letters, 54:55-58. [Bib entry]
Chickering (1995). A transformational characterization of equivalent Bayesian network structures. In Proceedings of Eleventh Conference on Uncertainty in Artificial Intelligence, Montreal, QU, pages 87-98. Morgan Kaufmann. [Bib entry]
Chickering, Geiger and Heckerman (1995). Learning Bayesian networks: Search methods and experimental results. In Proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, pages 112-128. [Bib entry]

Korf and Chickering (1994). Best-First Minimax Search: Othello Results. In Proceedings of the Thirteenth National Conference on Artificial Intelligence (AAAI-96), Portland, OR, volume 3, pages 1365-1370. [Bib entry]
Heckerman, Geiger and Chickering (1994). Learning Bayesian networks: The Combination of Knowledge and Statistical Data. In Proceedings of Tenth Conference on Uncertainty in Artificial Intelligence, Seattle, WA, pages 293-301. Morgan Kaufmann. [Bib entry]

Korf and Chickering (1993). Best-First Minimax Search: First Results. In Proceedings of the AAAI Fall Symposium on Games: Planning and Learning, Raleigh, NC, pages 39-47. [Bib entry]