David Maxwell Chickering

According to our database, David Maxwell Chickering authored at least 62 papers between 1994 and 2020.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of three.

2020

Statistically Efficient Greedy Equivalence Search.

Max Chickering

Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, 2020

2017

Machine Teaching: A New Paradigm for Building Machine Learning Systems.

Patrice Y. Simard, Saleema Amershi, David Maxwell Chickering, Alicia Edelman Pelton, Soroush Ghorashi, Christopher Meek, Gonzalo A. Ramos, Jina Suh, Johan Verwey, Mo Wang, John Wernsing

CoRR, 2017

Algorithms for Active Classifier Selection: Maximizing Recall with Precision Constraints.

Paul N. Bennett, David Maxwell Chickering, Christopher Meek, Xiaojin Zhu

Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, 2017

2016

Interactive Semantic Featuring for Text Classification.

Camille Jandot, Patrice Y. Simard, Max Chickering, David Grangier, Jina Suh

CoRR, 2016

2015

Selective Greedy Equivalence Search: Finding Optimal Bayesian Networks Using a Polynomial Number of Score Evaluations.

David Maxwell Chickering, Christopher Meek

Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence, 2015

ModelTracker: Redesigning Performance Analysis Tools for Machine Learning.

Saleema Amershi, Max Chickering, Steven Mark Drucker, Bongshin Lee, Patrice Y. Simard, Jina Suh

Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, 2015

2014

ICE: Enabling Non-Experts to Build Models Interactively for Large-Scale Lopsided Problems.

Patrice Y. Simard, David Maxwell Chickering, Aparna Lakshmiratan, Denis Xavier Charles, Léon Bottou, Carlos Garcia Jurado Suarez, David Grangier, Saleema Amershi, Johan Verwey, Jina Suh

CoRR, 2014

Approximating the shapley value via multi-issue decompositions.

Hossein Azari Soufiani, David Maxwell Chickering, Denis Xavier Charles, David C. Parkes

Proceedings of the International conference on Autonomous Agents and Multi-Agent Systems, 2014

2013

Counterfactual reasoning and learning systems: the example of computational advertising.

Léon Bottou, Jonas Peters, Joaquin Quiñonero Candela, Denis Xavier Charles, Max Chickering, Elon Portugaly, Dipankar Ray, Patrice Y. Simard, Ed Snelson

J. Mach. Learn. Res., 2013

Learning Equivalence Classes of Bayesian Networks Structures

David Maxwell Chickering

CoRR, 2013

Budget smoothing for internet ad auctions: a game theoretic approach.

Denis Xavier Charles, Deeparnab Chakrabarty, Max Chickering, Nikhil R. Devanur, Lei Wang

Proceedings of the fourteenth ACM Conference on Electronic Commerce, 2013

2012

Counterfactual Reasoning and Learning Systems

Léon Bottou, Jonas Peters, Joaquin Quiñonero Candela, Denis Xavier Charles, Max Chickering, Elon Portugaly, Dipankar Ray, Patrice Y. Simard, Ed Snelson

CoRR, 2012

Proceedings of the Twentieth Conference on Uncertainty in Artificial Intelligence (2004)

Max Chickering, Joseph Y. Halpern

CoRR, 2012

2011

The Pollution Effect: Optimizing Keyword Auctions by Favoring Relevant Advertising

Greg Linden, Christopher Meek, Max Chickering

CoRR, 2011

2010

Market design & analysis for a P2P backup system.

Sven Seuken, Denis Xavier Charles, Max Chickering, Sidd Puri

Proceedings of the Proceedings 11th ACM Conference on Electronic Commerce (EC-2010), 2010

Fast algorithms for finding matchings in lopsided bipartite graphs with applications to display ads.

Denis Xavier Charles, Max Chickering, Nikhil R. Devanur, Kamal Jain, Manan Sanghi

Proceedings of the Proceedings 11th ACM Conference on Electronic Commerce (EC-2010), 2010

Predicting the Importance of Newsfeed Posts and Social Network Friends.

Tim Paek, Michael Gamon, Scott Counts, David Maxwell Chickering, Aman Dhesi

Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence, 2010

2009

Learning consensus opinion: mining data from a labeling game.

Paul N. Bennett, David Maxwell Chickering, Anton Mityagin

Proceedings of the 18th International Conference on World Wide Web, 2009

Picture this: preferences for image search.

Paul N. Bennett, David Maxwell Chickering, Anton Mityagin

Proceedings of the ACM SIGKDD Workshop on Human Computation, Paris, France, June 28, 2009, 2009

Intentions: a game for classifying search query intent.

Edith Law, Anton Mityagin, David Maxwell Chickering

Proceedings of the 27th International Conference on Human Factors in Computing Systems, 2009

Market Design for a P2P Backup System.

Sven Seuken, Denis Xavier Charles, Max Chickering, Sidd Puri

Proceedings of the Auctions, 2009

2008

Rapidly Deploying Grammar-Based Speech Applications with Active Learning and Back-off Grammars.

Tim Paek, Sudeep Gandhe, Max Chickering

Proceedings of the SIGDIAL 2008 Workshop, 2008

Mining recommendations from the web.

Guy Shani, David Maxwell Chickering, Christopher Meek

Proceedings of the 2008 ACM Conference on Recommender Systems, 2008

Here or There.

Ben Carterette, Paul N. Bennett, David Maxwell Chickering, Susan T. Dumais

Proceedings of the Advances in Information Retrieval , 2008

Incorporating user utility into sponsored-search auctions.

Yagil Engel, David Maxwell Chickering

Proceedings of the 7th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2008), 2008

2007

Improving command and control speech recognition on mobile devices: using predictive user models for language modeling.

Tim Paek, David Maxwell Chickering

User Model. User Adapt. Interact., 2007

Personalizing influence diagrams: applying online learning strategies to dialogue management.

David Maxwell Chickering, Tim Paek

User Model. User Adapt. Interact., 2007

Modeling Contextual Factors of Click Rates.

Hila Becker, Christopher Meek, David Maxwell Chickering

Proceedings of the Twenty-Second AAAI Conference on Artificial Intelligence, 2007

2006

Evaluating the Markov assumption in Markov Decision Processes for spoken dialogue management.

Tim Paek, David Maxwell Chickering

Lang. Resour. Evaluation, 2006

On the incompatibility of faithfulness and monotone DAG faithfulness.

David Maxwell Chickering, Christopher Meek

Artif. Intell., 2006

Improving Cloaking Detection using Search Query Popularity and Monetizability.

Kumar Chellapilla, David Maxwell Chickering

Proceedings of the AIRWeb 2006, 2006

2005

The Markov Assumption in Spoken Dialogue Management.

Tim Paek, David Maxwell Chickering

Proceedings of the 6th SIGdial Workshop on Discourse and Dialogue, 2005

2004

Large-Sample Learning of Bayesian Networks is NP-Hard.

David Maxwell Chickering, David Heckerman, Christopher Meek

J. Mach. Learn. Res., 2004

ARMA Time-Series Modeling with Graphical Models.

Bo Thiesson, David Maxwell Chickering, David Heckerman, Christopher Meek

Proceedings of the UAI '04, 2004

2003

Targeted Advertising on the Web with Inventory Management.

David Maxwell Chickering, David Heckerman

Interfaces, 2003

Practically Perfect.

Christopher Meek, David Maxwell Chickering

Proceedings of the UAI '03, 2003

Learning Bayesian Networks From Dependency Networks: A Preliminary Study.

Geoff Hulten, David Maxwell Chickering, David Heckerman

Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics, 2003

2002

Optimal Structure Identification With Greedy Search.

David Maxwell Chickering

J. Mach. Learn. Res., 2002

Learning Equivalence Classes of Bayesian-Network Structures.

David Maxwell Chickering

J. Mach. Learn. Res., 2002

Finding Optimal Bayesian Networks.

David Maxwell Chickering, Christopher Meek

Proceedings of the UAI '02, 2002

Autoregressive Tree Models for Time-Series Analysis.

Christopher Meek, David Maxwell Chickering, David Heckerman

Proceedings of the Second SIAM International Conference on Data Mining, 2002

2001

A Bayesian Approach to Tackling Hard Computational Problems (Preliminary Report).

Eric Horvitz, Yongshao Ruan, Carla P. Gomes, Henry A. Kautz, Bart Selman, David Maxwell Chickering

Electron. Notes Discret. Math., 2001

Using Temporal Data for Making Recommendations.

Andrew Zimdars, David Maxwell Chickering, Christopher Meek

Proceedings of the UAI '01: Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence, 2001

A Bayesian Approach to Tackling Hard Computational Problems.

Eric Horvitz, Yongshao Ruan, Carla P. Gomes, Henry A. Kautz, Bart Selman, David Maxwell Chickering

Proceedings of the UAI '01: Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence, 2001

Efficient Determination of Dynamic Split Points in a Decision Tree.

David Maxwell Chickering, Christopher Meek, Robert Rounthwaite

Proceedings of the 2001 IEEE International Conference on Data Mining, 29 November, 2001

2000

A comparison of scientific and engineering criteria for Bayesian model selection.

David Maxwell Chickering, David Heckerman

Stat. Comput., 2000

Dependency Networks for Inference, Collaborative Filtering, and Data Visualization.

David Heckerman, David Maxwell Chickering, Christopher Meek, Robert Rounthwaite, Carl Myers Kadie

J. Mach. Learn. Res., 2000

Dependency Networks for Collaborative Filtering and Data Visualization.

David Heckerman, David Maxwell Chickering, Christopher Meek, Robert Rounthwaite, Carl Myers Kadie

Proceedings of the UAI '00: Proceedings of the 16th Conference in Uncertainty in Artificial Intelligence, Stanford University, Stanford, California, USA, June 30, 2000

A Decision Theoretic Approach to Targeted Advertising.

David Maxwell Chickering, David Heckerman

Proceedings of the UAI '00: Proceedings of the 16th Conference in Uncertainty in Artificial Intelligence, Stanford University, Stanford, California, USA, June 30, 2000

Targeted advertising with inventory management.

David Maxwell Chickering, David Heckerman

Proceedings of the 2nd ACM Conference on Electronic Commerce (EC-00), 2000

1999

Fast Learning from Sparse Data.

David Maxwell Chickering, David Heckerman

Proceedings of the UAI '99: Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence, Stockholm, Sweden, July 30, 1999

1998

Learning Mixtures of DAG Models.

Bo Thiesson, Christopher Meek, David Maxwell Chickering, David Heckerman

Proceedings of the UAI '98: Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, 1998

1997

Efficient Approximations for the Marginal Likelihood of Bayesian Networks with Hidden Variables.

David Maxwell Chickering, David Heckerman

Mach. Learn., 1997

A Bayesian Approach to Learning Bayesian Networks with Local Structure.

David Maxwell Chickering, David Heckerman, Christopher Meek

Proceedings of the UAI '97: Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence, 1997

1996

Best-First Minimax Search.

Richard E. Korf, David Maxwell Chickering

J. Int. Comput. Games Assoc., 1996

Efficient Approximations for the Marginal Likelihood of Incomplete Data Given a Bayesian Network.

David Maxwell Chickering, David Heckerman

Proceedings of the UAI '96: Proceedings of the Twelfth Annual Conference on Uncertainty in Artificial Intelligence, 1996

A Clinician's Tool for Analyzing Non-Compliance.

David Maxwell Chickering, Judea Pearl

Proceedings of the Thirteenth National Conference on Artificial Intelligence and Eighth Innovative Applications of Artificial Intelligence Conference, 1996

1995

Learning Bayesian Networks: The Combination of Knowledge and Statistical Data.

David Heckerman, Dan Geiger, David Maxwell Chickering

Mach. Learn., 1995

On Finding a Cycle Basis with a Shortest Maximal Cycle.

David Maxwell Chickering, Dan Geiger, David Heckerman

Inf. Process. Lett., 1995

A Transformational Characterization of Equivalent Bayesian Network Structures.

David Maxwell Chickering

Proceedings of the UAI '95: Proceedings of the Eleventh Annual Conference on Uncertainty in Artificial Intelligence, 1995

Learning Bayesian Networks is NP-Complete.

David Maxwell Chickering

Proceedings of the Learning from Data, 1995

1994

Best-First Minimax Search: Othello Results.

Richard E. Korf, David Maxwell Chickering

Proceedings of the 12th National Conference on Artificial Intelligence, Seattle, WA, USA, July 31, 1994