Papers Online
The following papers are available in pdf format:
- B. Thiesson, D.M. Chickering, D. Heckerman, and C. 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]
- D.M. Chickering, C. Meek, and D. 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]
- C. Meek and D.M. Chickering (2003).
Practically Perfect. In Proceedings of
Nineteenth Conference on Uncertainty in
Artificial Intelligence, Acapulco, Mexico, pages 411-416. Morgan
Kaufmann. [Bib
entry]
- G. Hulten, D.M. Chickering, D. 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]
- D.M. Chickering and C. Meek (2003).
Monotone DAG Faithfulness: A Bad Assumption. Technical Report
MSR-TR-2003-16, Microsoft, Redmond, WA.
[Bib
entry]
- C. Meek, D.M. Chickering and D. 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]
- D.M. Chickering and C. 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]
- D.M. Chickering (2002).
Optimal Structure Identification with Greedy Search.
Journal of Machine Learning Research, 3:507-554.[Bib
entry]
- D.M. Chickering (2002).
Learning Equivalence Classes of Bayesian-Network
Structures. Journal of Machine Learning Research, 2:445-498.
[Bib
entry]
- D.M. Chickering, C. Meek and R. 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]
- A. Zimdars, D.M. Chickering and C. 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]
- D.M. Chickering and D. Heckerman (2000).
Targeted
Advertising with Inventory Management. In ACM Special Interest
Group on E-Commerce (EC00), Minneapolis, MN, pages 145-149.
[Bib
entry]
- D.M. Chickering, D. Heckerman, C. Meek, J.C. Platt, and B. Thiesson
(2000).
Goal-oriented
clustering. Technical Report MSR-TR-2000-82, Microsoft, Redmond, WA.
[Bib
entry]
- D.M. Chickering and D. 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]
- D.M. Chickering and D. Heckerman (2000).
A
Comparison of Scientific and Engineering Criteria for Bayesian Model
Selection. Statistics and Computing, 10(1):55-62. [Bib
entry]
- D. Heckerman, D.M. Chickering, C. Meek, R. Rounthwaite, C. Kadie (2000).
Dependency Networks for Inference, Collaborative Filtering, and Data
Visualization. Journal of Machine Learning Research, 1:49-75. [Bib
entry]
- B. Thiesson, C. Meek, D.M. Chickering, and D. Heckerman (1999).
Computationally efficient methods for selecting
among mixtures of graphical models. In Bernardo, J., Berger, J., Dawid, A., and Smith, A., editors,
Bayesian Statistics 6, pages 631-656. Oxford University Press. [Bib
entry]
- D.M. Chickering and D. 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]
- Chickering, D.M. and Heckerman, D. (1997).
Efficient approximations for the marginal likelihood of
Bayesian networks with hidden variables. Machine Learning,
29:181-212. [Bib
entry]
- Chickering, D.M., Heckerman, D., and Meek, C. (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]
- D.M. 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]
- D.M. 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]
- D.M. Chickering and J. 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]
- D.M.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]
- D. Heckerman, D. Geiger and D.M. Chickering (1995).
Learning
Bayesian networks: The Combination of Knowledge and Statistical Data.
Machine
Learning, 20:197-243. [Bib
entry]
- D.M. Chickering, D. Geiger and D. 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]
- D.M. Chickering, D. Geiger, and D. Heckerman, (1995).
On finding a cycle basis with a shortest maximal
cycle.
Information Processing Letters, 54:55-58. [Bib
entry]
- R.E. Korf and D.M. 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.