I work on machine learning and related areas. My advisor is Sanjoy Dasgupta. I used to serve as the senior teaching assistant for the Computer Science and Engineering department (fa06-sp08) and am still happy to answer any questions related to being a computer science TA. Some information for TAs is available on the CSE599- Teaching Methods in Computer Science webpage (and the one from last year).

Papers

• L. Cayton. Fast nearest neighbor retrieval for bregman divergences. 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)