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| Artificial Intelligence Group |
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The Artificial Intelligence Group at UCSD engages in a wide range of theoretical and experimental research. Areas of particular strength include machine learning, reasoning under uncertainty, and cognitive modeling. Within these areas, students and faculty also pursue real-world applications to problems in computer vision, speech and audio processing, data mining, bioinformatics, and computer security. The Artificial Intelligence Group is part of a larger campus-wide effort in Computational Statistics and Machine Learning (COSMAL). Interdisciplinary collaborations are strongly supported and encouraged.
| People | Publications | Affiliations | Seminar |
Core Faculty
Affiliated Faculty
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Ph.D. Students
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Recent Publications
E. Coviello, A. Mumtaz, A. Chan, and G. Lanckriet. That was fast! Speeding up NN search of high dimensional distributions. In Proceedings of the 30th International Conference on Machine Learning (ICML-13). Atlanta, GA. June 2013.
D.-K. Kim, G. M. Voelker, and L. K. Saul. A variational approximation for topic modeling of hierarchical corpora. In Proceedings of the 30th International Conference on Machine Learning (ICML-13). Atlanta, GA. June 2013.
D. Lim, G. Lanckriet, and B. McFee. Robust structural metric learning. In Proceedings of the 30th International Conference on Machine Learning (ICML-13). Atlanta, GA. June 2013.
A. Menon, O. Tamuz, S. Gulwani, B. Lampson, and A. Kalai. A machine learning framework for programming by example. In Proceedings of the 30th International Conference on Machine Learning (ICML-13). Atlanta, GA. June 2013.
M. Telgarsky. Margins, Shrinkage, and Boosting. In Proceedings of the 30th International Conference on Machine Learning (ICML-13). Atlanta, GA. June 2013.
S. Dasgupta and K. Sinha. Randomized partition trees for exact nearest neighbor search. In Proceedings of the 26th Annual Conference on Computational Learning Theory (COLT-13). Princeton, NJ. June 2013.
M. Telgarsky. Boosting with the logistic loss is consistent. In Proceedings of the 26th Annual Conference on Computational Learning Theory (COLT-13). Princeton, NJ. June 2013.
K. Chaudhuri, A. Sarwate and K. Sinha. Near-optimal algorithms for differentially private principal components. In P. Bartlett, F. C. N. Pereira, C. J. C. Burges, L. Bottou, K. Q. Weinberger (eds.), Advances in Neural Information Processing Systems 25, pages 998-1006. Lake Tahoe, CA. December 2012.
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M. F. Der and L. K. Saul. Latent coincidence analysis: a hidden variable model for distance metric learning. In P. Bartlett, F. C. N. Pereira, C. J. C. Burges, L. Bottou, K. Q. Weinberger (eds.), Advances in Neural Information Processing Systems 25, pages 3239-3247. Lake Tahoe, CA. December 2012.
R. Huerta, S. Vembu, J. M. Amigo, T. Nowotny, and C. Elkan. Inhibition in multiclass classification. Neural Computation 24(9):2473-2507. September 2012.
S. Kpotufe and S. Dasgupta. A tree-based regressor that adapts to intrinsic dimension. Journal of Computer and System Sciences, 78(5): 1496-1515. September 2012.
A. Kumar, S. Vembu, A. K. Menon, and C. Elkan. Learning and inference in probabilistic classifier chains with beam search. In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD), pages 665-680. Bristol, UK. September 2012.
V. Ramavajjala and C. Elkan. Policy iteration based on a learned transition model. In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD), pages 211-226. Bristol, UK. September 2012.
B. Cipollini, J. H-W. Hsiao, and G. W. Cottrell. Connectivity asymmetry can explain visual hemispheric asymmetries in local/global, face, and spatial frequency processing. In Proceedings of the 34th Annual Conference of the Cognitive Science Society, pages 1410-1415. Sapporo, Japan. August 2012.
R. Li and G. W. Cottrell. A new angle on the EMPATH model: Spatial frequency orientation in recognition of facial expressions. In Proceedings of the 34th Annual Conference of the Cognitive Science Society, pages 1894-1899. Sapporo, Japan. August 2012.
T. Tsuchida and G. W. Cottrell. (2012) Auditory saliency using natural statistics. In Proceedings of the 34th Annual Conference of the Cognitive Science Society, pages 1048-1053. Sapporo, Japan. August 2012.
R. Yang and G. W. Cottrell. (2012) The influence of risk aversion on visual decision making. In Proceedings of the 34th Annual Conference of the Cognitive Science Society, pages 2564-2569. Sapporo, Japan. August 2012.
K. Chaudhuri and D. Hsu. Convergence rates for differentially private statistical estimation. In Proceedings of the 29th International Conference on Machine Learning (ICML-12), pages 1327-1334. Edinburgh, Scotland. June 2012.
A. K. Menon, X. Jiang, S. Vembu, C. Elkan, and L. Ohno-Machado. Predicting accurate probabilities with a ranking loss. In Proceedings of the 29th International Conference on Machine Learning (ICML-12), pages 703-710. Edinburgh, Scotland. June 2012.
M. Telgarsky and S. Dasgupta. Agglomerative Bregman clustering. In Proceedings of the 29th International Conference on Machine Learning (ICML-12), pages 1527-1534. Edinburgh, Scotland. June 2012.
K. Chaudhuri, F. Chung, and A. Tsiatas. Spectral clustering of graphs with general degrees in the extended planted partition model. In Proceedings of the 25th Annual Conference on Learning Theory (COLT-12). June 2012.
S. Dasgupta. Consistency of nearest neighbor classification under selective sampling. In Proceedings of the 25th Annual Conference on Learning Theory (COLT-12). June 2012.
C. Elkan and Y. Koren. Guest editorial for special issue KDD'10. ACM Transactions on Knowledge Discovery from Data 5(4):18. February 2012.
