Lawrence Cayton
lcayton circle-a cs-ucsd-edu
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