Papers

Preprints

M. Sun, G. Lebanon, and K. Collins-Thompson. Visualizing Differences in Web Search Algorithms using the Expected Weighted Hoeffding Distance. Technical Report FODAVA-09-22, Georgia Institute of Technology, 2009.
P. Donmez, G. Lebanon, and K. Balasubramanian. Unsupervised Estimation of Classification and Regression Error Rates. Technical Report CMU-LTI-09-015, Carnegie Mellon University.
Y. Zhao, G. Lebanon, and Y. Zhao. Local Likelihood Modeling of the Concept Drift Phenomenon. Technical Report FODAVA-09-21, Georgia Institute of Technology 2009.

2009

Y. Mao and G. Lebanon. Generalized Isotonic Conditional Random Fields. Machine Learning (to appear).
G. Lebanon, M. Scannapieco, M. R. Fouad, and E. Bertino. Beyond k-Anonymity: A Decision Theoretic Framework for Assessing Privacy Risk. Transactions on Data Privacy 2(3):153-183 2009.
E. Greenshtein, J. Park, and G. Lebanon. Regularization through Variable Selection and Conditional MLE with Application to Classification in High Dimensions. Journal of Statistical Planning and Inference 139(2):385-395, 2009.
D. J. Kasik, D. Ebert, G. Lebanon, H. Park, and W. M. Pottenger. Data Transformations and Representations for Computation and Visualization. Information Visualization 8(4):275-285, 2009.
G. Lebanon. Axiomatic Geometries for Text Documents. In P. Gibilisco, E. Riccomagno, M.-P. Rogantin, and H. P. Wynn (eds). Algebraic and Geometric Methods in Statistics. Cambridge University Press, 2009.
Y. Mao and G. Lebanon. Domain Knowledge Uncertainty and Probabilistic Parameter Constraints. Proc. of the 25^th Conference on Uncertainty in Artificial Intelligence (UAI), 2009.
P. Kidwell, G. Lebanon, and K. Collins-Thompson. Statistical Estimation of Word Acquisition with Application to Readability Prediction. Proc. of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2009.
J. V. Dillon and G. Lebanon. Statistical and Computational Tradeoffs in Stochastic Composite Likelihood. Proc. of the 12th International Conference on Artificial Intelligence and Statistics (AISTAT) JMLR W&CP 5:129-136, 2009.

2008

G. Lebanon and Y. Mao. Non-parametric Modeling of Partially Ranked Data. Journal of Machine Learning Research 9(Oct):2401-2429, 2008.
P. Kidwell, G. Lebanon, and W. S. Cleveland. Visualizing Incomplete and Partially Ranked Data. IEEE Transactions on Visualization and Computer Graphics 14(6):1356-1363, 2008.
G. Lebanon and Y. Mao. Non-Parametric Modeling of Partially Ranked Data. Advances in Neural Information Processing Systems 20, 2008.
G. Lebanon and Y. Zhao. Local Likelihood Modeling of the Concept Drift Phenomenon. Proc. of the 25th International Conference on Machine Learning, 2008.
M. R. Fouad, G. Lebanon, and E. Bertino. ARUBA: A Risk-Utility-Based Algorithm for Data Disclosure. 5th VLDB Workshop on Secure Data Management. Lecture Notes in Computer Science, volume 5159, pages 32-49. Springer, 2008.
G. M. Howard, S. Bagchi, and G. Lebanon. Determining placement of intrusion detectors for a distributed application through Bayesian network modeling. 11th International Symposium on Recent Advances in Intrusion Detection (RAID). Lecture Notes in Computer Science, volume 5230, pages 271-290. Springer, 2008.
Z. Zhang, M. Gupta, S. Yang, G. Lebanon, Y. C. Hu, and S. Midkiff. Extracting Source Level Program Similarities from Dynamic Behavior. Technical Report TR-ECE-08-08, Purdue University, 2008.

2007

G. Lebanon, Y. Mao, and J. V. Dillon. The Locally Weighted Bag of Words Framework for Document Representation. Journal of Machine Learning Research 8(Oct):2405-2441, 2007.
Y. Mao, J. V. Dillon, and G. Lebanon. Sequential Document Visualization. IEEE Transactions on Visualization and Computer Graphics, 13(6):1208-1215, 2007.
J. V. Dillon, Y. Mao, G. Lebanon, and J. Zhang. Statistical Translation, Heat Kernels, and Expected Distances. Proc. of the 23^rd Conference on Uncertainty in Artificial Intelligence, pages 93-100, 2007.
Y. Mao and G. Lebanon. Isotonic Conditional Random Fields and Local Sentiment Flow. Advances in Neural Information Processing Systems 19, pages 961-968, 2007.

2006

G. Lebanon. Metric Learning for Text Documents. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(4):497-508, 2006.
G. Lebanon. Sequential Document Representations and Simplicial Curves. Proc. of the 22^nd Conference on Uncertainty in Artificial Intelligence, pages 273-280, 2006.
G. Lebanon, M. Scannapieco, M. R. Fouad, and E. Bertino. Beyond k-Anonymity: A Decision Theoretic Framework for Assessing Privacy Risk. Privacy in Statistical Databases. Lecture Notes in Computer Science, volume 4302, pages 217-232. Springer, 2006.
Y. Mao and G. Lebanon. Sequential Models for Sentiment Prediction. ICML workshop on Learning in Structured Output Spaces, 2006.
J. V. Dillon, Y. Mao, G. Lebanon, and J. Zhang. Statistical Translation, Heat Kernels, and Expected Distances. NIPS workshop on Learning to Compare Examples, 2006.

2005

G. Lebanon. Axiomatic Geometry of Conditional Models. IEEE Transactions on Information Theory 51(4):1283-1294, 2005.
J. Lafferty and G. Lebanon. Diffusion Kernels on Statistical Manifolds. Journal of Machine Learning Research 6(Jan):129-163, 2005.
G. Lebanon. Information Geometry, the Embedding Principle, and Document Classification. Proc. of the 2^nd International Symposium on Information Geometry and its Applications, pages 101-108, 2005.
G. Lebanon. Riemannian Geometry and Statistical Machine Learning. PhD Thesis. Technical Report CMU-LTI-05-189, 2005.

2004

G. Lebanon. An Extended Cencov-Campbell Characterization of Conditional Information Geometry. Proc. of the 20^th Conference on Uncertainty in Artificial Intelligence 2004.
G. Lebanon and J. Lafferty. Hyperplane Margin Classifiers on the Multinomial Manifold. Proc. of the 21^st International Conference on Machine Learning, 2004.

2003

G. Lebanon and J. Lafferty. Conditional Models on the Ranking Poset. Advances in Neural Information Processing Systems 15, pages 431-438, 2003.
J. Lafferty and G. Lebanon. Information Diffusion Kernels. Advances in Neural Information Processing Systems 15, pages 391-399, 2003.
G. Lebanon. Learning Riemannian Metrics. Proc. of the 19^th Conference on Uncertainty in Artificial Intelligence 2003.
G. Lebanon. Computing the Volume Element of a Family of Transformations on the Multinomial Simplex. Technical Report CMU-CS-03-145, 2003.

2002

G. Lebanon and J. Lafferty. Boosting and Maximum Likelihood for Exponential Models. Advances in Neural Information Processing Systems 14, pages 447-454, 2002.
G. Lebanon and J. Lafferty. Cranking: Combining Rankings using Conditional Models on Permutations. Proc. of the 19^th International Conference on Machine Learning, 2002.
G. Lebanon and A. Bruckstein. On Designing Moire Patterns. Visual Attention Mechanisms, pages 205-219. Springer, 2002.

2001

G. Lebanon and A. Bruckstein. Variational Approach to Moire Pattern Synthesis. Journal of the Optical Society of America A 18(6):1371-1382, 2001.
G. Lebanon and A. Bruckstein. Designing Moire Patterns. Energy Minimization Methods in Computer Vision and Pattern Recognition. Lecture Notes in Computer Science Vol. 2134, pages 185-201, Springer, 2001.
G. Lebanon and J. Lafferty. Boosting and Maximum Likelihood for Exponential Models. Technical Report CMU-CS-01-144, 2001.