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.<\/p>\n
D.-K. Kim, M. F. Der and L. K. Saul. A Gaussian latent variable model for large margin classification of labeled and unlabeled data. In Proceedings of the 17th International Conference on Artificial Intelligence and Statistics (AISTATS 2014), Reykjavik, Iceland, April 2014 (to appear) <\/p>\n
M. Elkherj and Y. Freund. A system for sending the right hint at the right time. In ACM: Learning at Scale 2014 (L@S-14). Atlanta, Georgia. March 2014. <\/p>\n
C.M. Kanan, N. A. Ray, D. Bseiso, J. Hsiao, and G. Cottrell, Predicting an observer's task using multi-fixation pattern analysis. In Proceedings of The Annual Eye Tracking Research & Applications Symposium (ETRA 2014), Saftey Harbor, FL, March 2014 (to appear) <\/p>\n
K. Chaudhuri and S. Vinterbo. A stability-based validation procedure for differentially private machine learning . In Neural Information Processing Systems (NIPS), Lake Tahoe, NV. December 2013. <\/p>\n
M. Telgarsky and S. Dasgupta. Moment-based uniform deviation bounds for k-means and friends . In Neural Information Processing Systems (NIPS), Lake Tahoe, NV. December 2013. <\/p>\n
A. Balsubramani, S. Dasgupta, and Y. Freund. The fast convergence of incremental PCA . In Neural Information Processing Systems (NIPS), Lake Tahoe, NV. December 2013. <\/p>\n
R. A. Cowell, and G.W. Cottrell. What evidence supports special processing for faces? A cautionary tale for fMRI interpretation. In Journal of Cognitive Neuroscience 25(11):1777-1793. November 2013. <\/p>\n
Z. Ji and C. Elkan. Differential privacy based on importance weighting. Machine Learning 93(1): 163-183 October 2013. <\/p>\n
A. D. Sarwate and K. Chaudhuri. Signal processing and machine learning with differential privacy: algorithms and challenges for continuous data In IEEE Signal Processing Magazine , September 2013. <\/p>\n
A. Omigbodun, and G.W. Cottrell. Is facial expression processing holistic? In Proceedings of the 35th Annual Conference of the Cognitive Science Society. Austin, TX. July 2013. <\/p>\n
P. Wang, and G.W. Cottrell. A computational model of the development of hemispheric asymmetry of face processing. In Proceedings of the 35th Annual Conference of the Cognitive Science Society. Austin, TX. July 2013. <\/p>\n
B. Cipollini, and G.W. Cottrell. Uniquely human developmental timing may drive cerebral lateralization and interhemispheric coupling. In Proceedings of the 35th Annual Conference of the Cognitive Science Society. Austin, TX. July 2013. <\/p>\n
J. Hsiao, B. Cipollini, and G.W. Cottrell. Hemispheric asymmetry in perception: A differential encoding account. In Journal of Cognitive Neuroscience 25(7):998-1007. July 2013. <\/p>\n
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. <\/p>\n
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. <\/p>\n
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. <\/p>\n
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. <\/p>\n
M. Telgarsky. Margins, shrinkage, and boosting. In Proceedings of the 30th International Conference on Machine Learning (ICML-13). Atlanta, GA. June 2013. <\/p>\n
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. <\/p>\n
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. <\/p>\n
A. Kumar, S. Vembu, AK Menon and C. Elkan. Beam search algorithms for multilabel learning. Machine Learning 92(1): 65-89 June 2013. <\/p>\n
L. Yan, A. Elgamal, and G.W. Cottrell. A sub-structure vibration NARX neural network approach for statistical damage inference. In Journal of Engineering Mechanics (Special Issue on Dynamics and Analysis of Large-Scale Structures). 139(6):737-747. June 2013. <\/p>\n
R. Huerta, F. J. Corbacho, and C. Elkan. Nonlinear support vector machines can systematically identify stocks with high and low future returns. Algorithmic Finance 2(1): 45-58 March 2013. <\/p>\n
R. E. Schapire and Y. Fruend. Boosting: Foundations and Algorithms. R. E. Schapire and Y. Freund. MIT Press 2012. <\/p>\n
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. <\/p>\n
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. <\/p>\n
R. Huerta, S. Vembu, J. M. Amigo, T. Nowotny, and C. Elkan. Inhibition in multiclass classification. Neural Computation 24(9):2473-2507. September 2012. <\/p>\n
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. <\/p>\n
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. <\/p>\n
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. <\/p>\n
I. Valmianski, A.Y. Shih, J.D. Driscoll, D.W. Matthews, Y. Freund, D. Kleinfeld, Automatic identification of fluorescently labeled brain cells for rapid functional imaging. J Neurophysiol. September 2012. <\/p>\n
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. <\/p>\n
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. <\/p>\n
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. <\/p>\n
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. <\/p>\n
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.<\/p>\n
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. <\/p>\n
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. <\/p>\n
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. <\/p>\n
S. Dasgupta. Consistency of nearest neighbor classification under selective sampling. In Proceedings of the 25th Annual Conference on Learning Theory (COLT-12). June 2012. <\/p>\n
M. Jacobsen, Y. Freund and R. Kastner. RIFFA: A reusable integration framework for FPGA accelerators. In 20th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM), May 2012. <\/p>\n
C. Elkan and Y. Koren. Guest editorial for special issue KDD'10. ACM Transactions on Knowledge Discovery from Data 5(4):18. February 2012. <\/p>\n
M. Jacobson, Y. Freund and T. Nguyen. An online learning approach to occlusion boundary detection. IEEE Transaction on Image Processing, January 2012. <\/p>\n
<\/p>\n
Visit link: <\/p>\n
UCSD CSE - Artificial Intelligence<\/a><\/p>\n","protected":false},"excerpt":{"rendered":" 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 Continue reading