Dec 1, 2018 10 min read

Books

G. Lebanon and M. El-Geish. Computing with Data: An Introduction to the Data Industry, Springer 2018 (amazon).
G. Lebanon and S. V. N. Vishwanathan (Editors). Proceedings of the 18th International AI and Statistics Conference, JMLR Workshop and Conference Proceedings, Volume 38, 2015.
G. Lebanon. Riemannian Geometry and Statistical Machine Learning. Lambert Academic Publishing, 2015 (Reprint of 2005 PhD Dissertation).
G. Lebanon. Probability, Createspace Publishing, 2013 (amazon)
X.-W. Chen, G. Lebanon, M. Zaki, and H. Wang (Editors). Proceedings of the 21st ACM Conference on Information and Knowledge Management. ACM Press 2012.

Papers

2016

J. Lee, S. Kim, G. Lebanon, Y. Singer, and S. Bengio. LLORMA: Local Low-Rank Matrix Approximation. Journal of Machine Learning Research 17(15):1-24, 2016.
K. Balasubramanian, K. Yu, and G. Lebanon. Smooth Sparse Coding via Marginal Regression for Learning Sparse Representations. Artificial Intelligence 238(Sep):83-95, 2016.

2015

D. Agarwal, B.-C. Chen, Q. He, Z. Hua, G. Lebanon, Y. Ma, P. Shivaswamy, H.-P. Tseng, J. Yang and L. Zhang. Personalizing LinkedIn Feed. Proceedings of the 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2015.
S. Kim, J. Lee, G. Lebanon and H. Park. Estimating Temporal Dynamics of Human Emotions. Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI) 2015.
S. Kim, J. Lee, G. Lebanon and H. Park. Local Context Sparse Coding. Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI) 2015.

2014

J. Lee, S. Bengio, S. Kim, G. Lebanon, Y. Singer. Local Collaborative Ranking, Proceedings of the 23rd International World Wide Web Conference (WWW), 2014 (Best Student Paper Award).
S. Kim, H. Park and G. Lebanon. Fast Spammer Detection Using Structural Rank. ArXiv:1407.7072, 2014.

2013

K. Balasubramanian, K. Yu, and G. Lebanon. Smooth Sparse Coding via Marginal Regression for Learning Sparse Representations (supplement). Proceedings of the 30th International Conference on Machine Learning (ICML) 2013 (Best Paper Runner-Up Award).
J. Lee, S. Kim, G. Lebanon, and Y. Singer. Local Low-Rank Matrix Approximation. Proceedings of the 30th International Conference on Machine Learning (ICML) 2013.
S. Kim, F. Li, G. Lebanon, and I. Essa. Beyond Sentiment: The Manifold of Human Emotions. Proceedings of the 16th International Conference on Artificial Intelligence and Statistics (AISTATS), 2013.
K. Balasubramanian, B. Sriperumbudur, and G. Lebanon. Ultrahigh dimensional feature screening based on RKHS embeddings (supplement). Proceedings of the 16th International Conference on Artificial Intelligence and Statistics (AISTATS) 2013.
M. Sun, F. Li, J. Lee, K. Zhou, G. Lebanon, and H. Zha. Learning Multiple-Question Decision Trees for Cold-Start Recommendation. Proceedings of the 6th ACM International Conference on Web Search and Data Mining (WSDM) 2013.
F. Li, J. Carreira, G. Lebanon, and C. Sminchisescu. Composite Statistical Inference for Semantic Segmentation. IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) 2013.
L. Weiss, E. Briscoe, H. Hayes, O. Kemenova, S. Harbert, F. Li, G. Lebanon, C. Stewart, D. Miller Steiger, and D. Foy. A Comparative Study of Social Media and Traditional Polling in the Egyptian Uprising of 2011. Proceedings of the 6th International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction (SBP). Lecture Notes in Computer Science, volume 7812, pages 303-310, Springer 2013.
F. Li, J. Carreira, G. Lebanon, and C. Sminchisescu. CSI: Composite Statistical Inference for Semantic Segmentation. ICML 2013 Workshop on Inferning: Interactions between Inference and Learning, 2013.
J. Lee, S. Kim, G. Lebanon, Y. Singer. Matrix Approximation under Local Low-Rank Assumption, The Learning Workshop in International Conference on Learning Representations (ICLR), 2013.

2012

J. Lee, M. Sun, and G. Lebanon. PREA: Personalized Recommendation Algorithms Toolkit. Journal of Machine Learning Research, 13(Sep):2699-2703, 2012.
M. Sun, G. Lebanon, and P. Kidwell. Estimating Probabilities in Recommendation Systems. Journal of the Royal Statistical Society, Series C, 61(3):471-492, 2012.
J. Lee, M. Sun, S. Kim, and G. Lebanon. Automatic Feature Induction for Stagewise Collaborative Filtering. Advances in Neural Information Processing Systems 25, 2012.
K. Balasubramanian and G. Lebanon. The Landmark Selection Method for Multiple Output Prediction. Proc. of the 29th International Conference on Machine Learning (ICML) 2012.
L. Li, G. Lebanon, and H. Park. Fast Bregman Divergence NMF using Taylor Expansion and Coordinate Descent. Proc. of the 18th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2012.
F. Li, G. Lebanon, and C. Sminchisescu. Chebyshev Approximations to the Histogram Chi-Square Kernel. IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) 2012.
S. Kim, J. V. Dillon, and G. Lebanon. [Cumulative Revision Map[(http://arxiv.org/pdf/1205.3205.pdf). ArXiv 1205.3205, 2012.
J. Lee, M. Sun, and G. Lebanon. A Comparative Study of Collaborative Filtering Algorithms. ArXiv 1205.3193, 2012.

2011

K. Balasubramanian, P. Donmez, and G. Lebanon. Unsupervised Supervised Learning II: Training Margin Based Classifiers without Labels. Journal of Machine Learning Research 12(Nov):3119-3145, 2011.
P. Kidwell, G. Lebanon, and K. Collins-Thompson. Statistical Estimation of Word Acquisition with Application to Readability Prediction. Journal of the American Statistical Association 106(493):21-30, 2011.
K. Balasubramanian, P. Donmez, and G. Lebanon. Unsupervised Supervised Learning II: Margin Based Classification without Labels. Proc. of the 14th International Conference on Artificial Intelligence and Statistics (AISTATS) JMLR: W&CP 15 2011.
M. Sun, G. Lebanon, and P. Kidwell. Estimating Probabilities in Recommendation Systems. Proc. of the 14th International Conference on Artificial Intelligence and Statistics (AISTATS) JMLR: W&CP 15 2011.

2010

J. V. Dillon and G. Lebanon. Stochastic Composite Likelihood. Journal of Machine Learning Research 11(Oct):2597-2633, 2010.
P. Donmez, G. Lebanon, and K. Balasubramanian. Unsupervised Supervised Learning I: Estimating Classification and Regression Errors without Labels. Journal of Machine Learning Research 11(Apr):1323-1351, 2010.
G. Lebanon, Y. Zhao, and Y. Zhao. Modeling Temporal Text Streams using the Local Multinomial Model. Electronic Journal of Statistics 4:566-584, 2010.
P. Kidwell and G. Lebanon. Kernel smoothing for preference data using generating functions. In M. Viana and H. P. Wynn (eds), Algebraic Methods in Statistics and Probability II. Contemporary Mathematics Series, volume 516, American Mathematical Society, 2010.
J. V. Dillon, K. Balasubramanian, and G. Lebanon. Asymptotic Analysis of Generative Semi-Supervised Learning. Proc. of the 27th International Conference on Machine Learning (ICML) 2010.
M. Sun, G. Lebanon, and K. Collins-Thompson. Visualizing Differences in Web Search Algorithms using the Expected Weighted Hoeffding Distance. Proc. of the 19th International World Wide Web Conference (WWW) 2010.
S. Kim and G. Lebanon. Local Space-Time Smoothing for Version Controlled Documents. Proc. of The 23rd International Conference on Computational Linguistics (COLING) 2010.
Y. Mao, K. Balasubramanian, and G. Lebanon. Dimensionality Reduction for Text using Domain Knowledge. The 23rd International Conference on Computational Linguistics (COLING) 2010.

2009

Y. Mao and G. Lebanon. Generalized Isotonic Conditional Random Fields. Machine Learning 77(2-3):225-248, 2009.
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 25th 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 (AISTATS) JMLR W&CP 5:129-136, 2009.
M. Sun, G. Lebanon, and K. Collins-Thompson. Visualizing Spatial Proximity of Search Algorithms. NIPS Workhop on Learning with Ordering (poster abstract), 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.
A. Kamra, E. Bertino, and G. Lebanon. Mechanisms for Database Intrusion Detection and Response. Proceedings of the 2nd SIGMOD PhD workshop on Innovative database research, pages 31-36, ACM Press 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 23rd 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 22nd 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 2nd 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 20th Conference on Uncertainty in Artificial Intelligence 2004.
G. Lebanon and J. Lafferty. Hyperplane Margin Classifiers on the Multinomial Manifold. Proc. of the 21st 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 19th 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 19th 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.