I'm a research scientist in the Empirical Inference Department of the Max Planck Institute for Biological Cybernetics. Formerly, I was a graduate student at the University of California, San Diego. I work on machine learning and related areas, with a particular emphasis on efficient proximity search methods.

Papers

• L. Cayton. Efficient bregman range search. Advances in Neural Information Processing Systems 22 (NIPS), 2009.
• L. Cayton. Fast nearest neighbor retrieval for bregman divergences. (slides) Twenty-Fifth International Conference on Machine Learning (ICML), 2008.
  Bregman ball tree C implementation: [code, readme]
• L. Cayton and S. Dasgupta. A learning framework for nearest neighbor search. Advances in Neural Information Processing Systems 20 (NIPS), 2007.
• L. Cayton and S. Dasgupta. Robust Euclidean embedding. (slides) Twenty-Third International Conference on Machine Learning (ICML), 2006.
• L. Cayton, R. Herring, A. Holder, J. Holzer, C. Nightingale, T. Stohs. Asymptotic sign-solvability, multiple objective linear programming, and the nonsubstitution theorem. (older draft) Mathematical Methods of Operations Reseach, 64: 541-555, 2006.
• S. Agarwal, J. Wills, L. Cayton, G. Lanckriet, D. Kriegman, S. Belongie. Generalized non-metric multidimensional scaling. Eleventh International Conference on Artificial Intelligence and Statistics (AISTATS), 2007.
• Research Exam: Algorithms for manifold learning. Spring 2005. (UCSD tech report CS2008-0923)

Teaching

• cse20 - Discrete mathematics (summer 2007)