Fabio Cozman's List of Selected Publications

Fabio Cozman's Selected Publications (as of February 2013)

You can find a complete list elsewhere; here is a list of publications that contain either relatively finished material on relatively complete research, or results that have not appeared elsewhere. Basically it contains several of my journal papers plus a selection of conference papers and a few technical reports.

Book chapters

Fabio G. Cozman, Teddy Seidenfeld. Independence for full conditional measures and their graphoid properties. Foundations of the Formal Sciences VI, Reasoning about Probabilities and Probabilistic Reasoning pp. 1-29, College Publications, London, 2009. Preprint available.
F. G. Cozman, I. Cohen, Risks of semi-supervised learning, in Olivier Chapelle, Bernhard Scholkopf, Alexander Zien (editors), Semi-Supervised Learning, pp. 55-70, 2006. Preprint available.

Journal articles

Fabio G. Cozman. Sets of probability distributions, independence, and convexity. Synthese, 186(2):577-600, 2012. Preprint available.
Rafael A. M. Goncalves, Diego R. Cueva, Marcos R. Pereira-Barretto, Fabio G. Cozman. A model for inference of emotional state based on facial expressions Journal of the Brazilian Computer Society, 2012 (Online first). Preprint available.
Valquiria Fenelon Pereira, Paulo E. Santos, Hannah M. Dee and Fabio G. Cozman. Reasoning about shadows in a mobile robot environment. Applied Intelligence, 2012 (Online first). Preprint available.
Daniel Kikuti, Fabio G. Cozman, Ricardo Shirota Filho. Sequential decision making with partially ordered preferences. Artificial Intelligence, 175(7-8):1346-1365, 2011. Preprint available.
Karina Valdivia Delgado, Leliane Nunes de Barros, Fabio Gagliardi Cozman, Scott Sanner. Using mathematical programming to solve Factored Markov Decision Processes with Imprecise Probabilities. International Journal of Approximate Reasoning, p. 200, 2011. Preprint available.
Fabio Gagliardi Cozman. Concentration inequalities and laws of large numbers under epistemic and regular irrelevance. International Journal of Approximate Reasoning, 51:1069-1084, 2010. Preprint available.
Marko Ackermann, Fabio Gagliardi Cozman. Automatic knee flexion in lower limb orthoses. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 31(4):305-311, 2009. Preprint available.
Cassio Polpo de Campos, Fabio G. Cozman, Jose Eduardo Ochoa Luna. Assembling a consistent set of sentences in relational probabilistic logic with stochastic independence, Journal of Applied Logic, 7:137-154, 2009.
Jaime Shinsuke Ide, Fabio G. Cozman. Approximate algorithms for credal networks with binary variables, International Journal of Approximate Reasoning, v. 48, p. 275-296, 2008. Preprint available.
Fabio G. Cozman, Cassio Polpo de Campos, Jose Carlos Ferreira da Rocha. Probabilistic logic with independence, International Journal of Approximate Reasoning, v. 49, p. 3-17, 2008. Preprint available.
C. Polpo de Campos, F. G. Cozman. Computing lower and upper expectations under epistemic independence, International Journal of Approximate Reasoning, 44(3):244-260, 2007. Preprint available.
P. Vicig, M. Zaffalon, F. G.Cozman. Notes on "Notes on conditional previsions", International Journal of Approximate Reasoning, 44(3):358-365, 2007. Preprint available.
F. G. Cozman, P. Walley. Graphoid properties of epistemic irrelevance and independence, Annals of Mathematics and Artificial Intelligence, 45:173-195, 2005. Preprint available.
N. Sebe, I. Cohen, F. G. Cozman, T. Gevers, T. S. Huang. Learning probabilistic classifiers for human-computer interaction applications, Multimedia Systems, 10(6):484-498, 2005. Preprint available.
F. T. Ramos, F. G. Cozman. Anytime anyspace probabilistic inference, International Journal of Approximate Reasoning, 38:53-80, 2005. Preprint available.
F. G.Cozman. Graphical models for imprecise probabilities, Journal of International Journal of Approximate Reasoning, 39(2-3):167-184, 2005. Preprint available.
J. C. F. da Rocha, F. G. Cozman. Inference in credal networks: branch-and-bound methods and the A/R+ algorithm, International Journal of Approximate Reasoning, 39(2-3):279-296, 2005. Preprint available.
I. Cohen, F. G. Cozman, N. Sebe, M. C. Cirelo, T. S. Huang. Semisupervised learning of classifiers: Theory, algorithms, and their application to human-computer interaction, IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(12):1553-1568, 2004. Preprint available.
F. G. Cozman, E. Krotkov, C. E. Guestrin. Outdoor Visual Position Estimation for Planetary Rovers, Autonomous Robots, vol. 9, pp. 135-150, 2000. Preprint available.
F. G. Cozman. Credal networks, Artificial Intelligence Journal, vol. 120, pp. 199-233, 2000. Preprint available.
F. G. Cozman. Computing posterior upper expectations, International Journal of Approximate Reasoning, vol. 24, pp. 191-205, 2000. Preprint available.
F. G. Cozman. Calculation of Posterior Bounds Given Convex Sets of Prior Probability Measures and Likelihood Functions, Journal of Computational and Graphical Statistics, vol. 8(4), pp. 824-838, 1999. Preprint available; note the errata for this paper!

Conferences and symposia

Jose Eduardo Ochoa Luna, Kate C. Revoredo, Fabio Gagliardi Cozman. An Experimental Evaluation of a Scalable Probabilistic Description Logic Approach for Semantic Link Prediction. 8th International Workshop on Uncertainty Reasoning for the Semantic Web (URSW 2012), pp. 63-74, 2012. Preprint available.
Kate C. Revoredo, Jose Eduardo Ochoa-Luna, Fabio Gagliardi Cozman. Semantic Link Prediction through Probabilistic Description Logic, 7th International Workshop on Uncertainty Reasoning for the Semantic Web, pp. 87–97, Bonn, Germany, 2011. Preprint available.
Tiago Matos, Yannick P. Bergamo, Valdinei F. da Silva, Fabio G. Cozman, Anna H. Reali Costa. Simultaneous Abstract and Concrete Reinforcement Learning, Symposium on Abstraction, Reformulation, and Approximation, pp. 82-89, Parador de Cardona, July 2011. Preprint available.
Jose Eduardo Ochoa Luna, Kate C. Revoredo, Fabio Gagliardi Cozman. Learning probabilistic description logics: A framework and algorithms, Advances in Artificial Intelligence - 10th Mexican International Conference on Artificial Intelligence (MICAI2011), Lecture Notes in Artificial Intelligence 7094 Part I, pp. 28-39, Springer, 2011. Preprint available.
Diego R. Cueva, Rafael A. M. Gonçalves, Fabio Gagliardi Cozman, Marcos R. Pereira-Barretto. Crawling to improve multimodal emotion detection, Advances in Artificial Intelligence - 10th Mexican International Conference on Artificial Intelligence (MICAI2011), Lecture Notes in Artificial Intelligence 7095, Part II, pp. 343-350, Springer, 2011. Preprint available.
Paulo E. Santos, Fabio G. Cozman, Valquiria F. Pereira, B. Hummel. Probabilistic logic encoding of spatial domains. International Workshop on Uncertainty in Description Logics, 2010. Preprint available.
Valquiria F. Pereira, B. Hummel, Paulo E. Santos, Fabio G. Cozman. Encoding spatial domains with relational Bayesian networks. Workshop on Spatio-Temporal Dynamics (STeDY), pp. 49-54, 2010. Preprint available.
Kate C. Revoredo, Jose Eduardo Ochoa-Luna, Fabio G. Cozman. Learning terminologies in probabilistic description logics. Lecture Notes in Artificial Intelligence, volume 6404, Advances in Artificial Intelligence - SBIA 2010, pp. 41-50, 2010. Preprint available.
Rodrigo B. Polastro, Fabiano E. Correa, Fabio G. Cozman, J. Okamoto Jr. Semantic mapping with a probabilistic description logic. Lecture Notes in Artificial Intelligence, volume 6404, Advances in Artificial Intelligence - SBIA 2010, pp. 62-71, 2010. Preprint available.
Fabiano Correa, Rodrigo Bellizia Polastro, Fabio Gagliardi Cozman, Jun Okamoto Junior. Dealing with semantic knowledge in robotics with a probabilistic description logic. ASAI 2010 - XI Argentine Symposium on Artificial Intelligence, p. 1-12, 2010. Preprint available.
Jose Eduardo Ochoa Luna, Kate Revoredo, Fabio Gagliardi Cozman. Semantic query extension through probabilistic description logics. Workshop on Uncertainty Reasoning for the Semantic Web at the International Semantic Web Conference, p. 49-60, 2010. Preprint available.
Jose Eduardo Ochoa Luna, Kate Revoredo, Fabio Gagliardi Cozman. Learning sentences and assessments in probabilistic description logics. Workshop on Uncertainty Reasoning for the Semantic Web at the International Semantic Web Conference, p. 85-96, 2010. Preprint available.
Karina Valdivia Delgado, Scott Sanner, Leliane Nunes de Barros, Fabio G. Cozman. Efficient solutions to factored MDPs with imprecise transition probabilities, 19th International Conference on Automated Planning and Scheduling, pp. 98-105, Thessaloniki, Greece, 2009. Preprint available.
Fabio G. Cozman, Rodrigo Polastro. Complexity analysis and variational inference for interpretation-based probabilistic description logics, Conference on Uncertainty in Artificial Intelligence, 2009. Preprint available.
Fabio G. Cozman, Rodrigo Bellizia Polastro. Loopy propagation in a probabilistic description logic, Second International Conference on Scalable Uncertainty Management, Lecture Notes in Artificial Intelligence, LNAI 5291, pp. 120-133, Springer, 2008. Preprint available.
Silvio do Lago Pereira, Leliane N. de Barros, Fabio G. Cozman. Strong Probabilistic Planning, Advances in Artificial Intelligence, 7th Mexican International Conference on Artificial Intelligence (MICAI), Lecture Notes in Computer Science 5317, Springer (ISBN 978-3-540-88635-8), pp. 636-652, 2008. Preprint available.
Felipe W. Trevizan, Fabio G. Cozman, Leliane N. de Barros. Mixed Probabilistic and Nondeterministic Factored Planning through Markov Decision Processes with Set-Valued Transitions, Workshop on A Reality Check for Planning and Scheduling Under Uncertainty at the Eighteenth International Conference on Automated Planning and Scheduling (ICAPS), 2008. Preprint available.
Felipe W. Trevizan, Fabio G. Cozman, Leliane N. de Barros. Planning under Risk and Knightian Uncertainty, International Joint Conference on Artificial Intelligence, pp. 2023-2028, 2007. Preprint available.
Cassio Polpo de Campos, Fabio Gagliardi Cozman. Inference in credal networks through integer programming, Fifth International Symposium on Imprecise Probability: Theories and Applications, pp. 145-154, Prague, Czech Republic, 2007. Preprint available.
Ricardo Shirota Filho, Fabio Gagliardi Cozman, Felipe Werndl Trevizan, Cassio Polpo de Campos, Leliane Nunes de Barros. Multilinear and integer programming for Markov decision processes with imprecise probabilities, Fifth International Symposium on Imprecise Probability: Theories and Applications, pp. 395-404, Prague, Czech Republic, 2007. Preprint available.
A. Antonucci, M. Zaffalon, J. Ide, F. G. Cozman. Binarization algorithms for approximate updating in credal nets. In L. Penserini, P. Peppas, A. Perini, eds., Proceedings of the Third European Starting AI Researcher Symposium, pp. 120-131, Amsterdam, The Netherlands, IOS Press, 2006.
C. P. de Campos, F. G. Cozman. Belief updating and learning in semi-qualitative probabilistic networks, Conference on Uncertainty in Artificial Intelligence (UAI), pp. 153-160, Edinburgh, United Kingdom, 2005. Preprint available.
D. Kikuti, F. G. Cozman, C. P. de Campos. Partially ordered preferences in decision trees: computing strategies with imprecision in probabilities, IJCAI Workshop on Advances in Preference Handling, Edinburgh, United Kingdom, 2005. Preprint available. PLEASE also note that an errata has been produced, correcting a few mistakes in the paper!
C. P. de Campos, F. G. Cozman. The inferential complexity of Bayesian and credal networks, International Joint Conference on Artificial Intelligence, pp. 1313-1318, Edinburgh, United Kingdom, 2005. Preprint available.
J. S. Ide, F. G. Cozman, F. T. Ramos. Generating random Bayesian networks with constraints on induced width, European Conference on Artificial Intelligence (ECAI), pp. 323-327, IOS Press, Amsterdan, 2004. Preprint available.
F. G. Cozman, C. P. de Campos, J. S. Ide, J. C. F. da Rocha. Propositional and relational Bayesian networks associated with imprecise and qualitative probabilistic assessments, Conference on Uncertainty in Artificial Intelligence, pp. 104-111, AUAI Press, 2004. Preprint available.
C. P. de Campos, F. G. Cozman. Inference in credal networs using multilinear programming, Second Starting AI Researcher Symposium (STAIRS), pp. 50-61, IOS Press, 2004. Preprint available.
J. C. F. da Rocha, F. G. Cozman, C. P. de Campos. Inference in polytrees with sets of probabilities, Conference on Uncertainty in Artificial Intelligence, pp. 217-224, Morgan Kaufmann, 2003. Preprint available.
F. G. Cozman, I. Cohen, M. C. Cirelo. Semi-supervised learning of mixture models, International Conference on Machine Learning, pp. 99-106, 2003. Preprint available.
I. Cohen, N. Sebe, F. G. Cozman, M. C. Cirelo, T. S. Huang. Learning Bayesian network classifiers for facial expression recognition using both labeled and unlabeled data, IEEE Conference on Computer Vision and Pattern Recognition, 2003. Preprint available.
F. G. Cozman. Algorithms for Conditioning on Events of Zero Probability, Fifteenth International Florida Artificial Intelligence Society Conference, pags. 248-252, Pensacola, Florida, United States, 2002. Preprint available.
F. G. Cozman, I. Cohen. Unlabeled Data can Degrade Classification Performance of Generative Classifiers, Fifteenth International Florida Artificial Intelligence Society Conference, pags. 327-331, Pensacola, Florida, United States, 2002. Preprint available.
F. G. Cozman. Constructing Sets of Probability Measures Through Kuznetsov's Independence Condition, Proceedings of the Second International Symposium on Imprecise Probabilities and Their Applications, pags. 104-111, Ithaca, New York, United States, 2001. Preprint available.
F. G. Cozman. Generalizing Variable Elimination in Bayesian Networks, Proceedings of the IBERAMIA/SBIA 2000 Workshops (Workshop on Probabilistic Reasoning in Artificial Intelligence), pp. 27-32, Editora Tec Art, São Paulo, Brazil, 2000. Preprint available.
C. Guestrin; F. G. Cozman; E. Krotkov. Fast Software Image Stabilization with Color Registration, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 19-24, Victoria, Canada, October, 1998.
F. Cozman; E. Krotkov. Depth from Scattering, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Puerto Rico, June, 1997. Preprint available.
F. Cozman; E. Krotkov. Quasi-Bayesian Strategies for Efficient Plan Generation: Application to the Planning to Observe Problem, Proc. Twelfth Conference Uncertainty in Artificial Intelligence, pp. 186-193, 1996. Preprint available.
R. Simmons; E. Krotkov; L. Chrisman; F. Cozman; R. Goodwin; M. Hebert; L. Katragadda; S. Koenig; G. Krishnaswamy; Y. Shinoda; W. Whittaker; and P. Klarer. Experience with Rover Navigation for Lunar-Like Terrains, Proceedings of the Conference on Intelligent Robots and Systems (IROS), pages 441-446, 1995.
F. Cozman; E. Krotkov. Robot Localization using a Computer Vision Sextant, International Conference on Robotics and Automation, pages 106-111, Nagoya, Japan, May 1995. Preprint available.
F. Cozman; E. Krotkov. Truncated Gaussians as Tolerance Sets, Fifth Workshop on Artificial Intelligence and Statistics, Fort Lauderdale Florida, 1995. Preprint available.
F. G. Cozman; P. E. Miyagi. Trajectory Controller for a Mobile Robot using Optimal Control, XI Congresso Brasileiro de Engenharia Mecânica, 3:537-540, São Paulo, SP Brazil, 1991.

Technical reports

F. G. Cozman, T. Seidenfeld. Independence for full conditional measures, graphoids and Bayesian networks, Technical Report from Escola Politécnica da USP, BT/PMR/0711, 2007 (most material here appeared in a book chapter in 2009, but some interesting results are only discussed in this technical report).
F. G. Cozman. Axiomatizing Noisy-OR, Technical Report from Escola PolitÃ©cnica da USP, BT/PMR/0409, 2004. This report is an extended version of paper presented at the European Conference on Artificial Intelligence 2004.
F. Cozman and L. Chrisman. Learning Convex Sets of Probability from Data, Technical Report CMU-RI-TR-97-25, Robotics Institute, Carnegie Mellon University, Pittsburgh, 1997.
F. Cozman. An Informal Introduction to Quasi-Bayesian Theory (and Lower Probability, Lower Expectations, Choquet Capacities, Robust Bayesian Methods, etc...) for AI, Technical Report CMU-RI-TR-97-24, Robotics Institute, Carnegie Mellon University, Pittsburgh, 1997.

fgcozman@usp.br