Papers
- A. Bogdanov, P. Papakonstantinou, and A. Wan
Pseudorandmoness for Linear Length Branching Programs and Stack Machines
Proceedings for the of the 16th International Workshop on Randomness and Computation (RANDOM), 2012.
- A. Bogdanov, P. Papakonstantinou, and A. Wan
Pseudorandomness for Read-once Formulas
Proceedings of the 52nd Annual Symposium on Foundations of Computer Science (FOCS), 2011.
- A. Klivans, H.K. Lee, and A. Wan
Mansour's Conjecture is True for Random DNF
23rd International Conference on Learning Theory (COLT), pp. 368-380, 2010
- I. Diakonikolas, R.A. Servedio, L. Tan, and A. Wan
A Regularity Lemma, and Low-Weight Approximators, for Low-Degree Polynomial Threshold Functions
25th Conference on Computational Complexity (CCC), pp. 211-220, 2010.
- A. Bogdanov, K. Talwar, and A. Wan
Hard Instances for Satisfiability and Quasi-one-way Functions
The First Symposium on Innovations in Computer Science (ICS), pp. 20-23, 2010.
- D. Dachman-Soled, H. Lee, T. Malkin, R. Servedio, A. Wan, H. Wee
Optimal Cryptographic Hardness of Learning Monotone Functions.
Theory of Computing, Vol. 5, pp. 257-282.
35th International Conference on Automata,
Languages and Programming (ICALP), 2008.
- I. Diakonikolas, H. Lee, K. Matulef, R. Servedio and A. Wan
Efficiently Testing Sparse GF(2) Polynomials.
35th International Conference on Automata,
Languages and Programming (ICALP), 2008.
- J. Jackson, H. Lee, R. Servedio and A. Wan
Learning Random Monotone DNF
Discrete Applied Mathematics, to appear.
RANDOM 2008, pp 283-497.
Also available as ECCC Tech Report TR07-129.
- I. Diakonikolas, H. Lee, K. Matulef, K. Onak,
R. Rubinfeld, R. Servedio and A. Wan.
Testing for Concise Representations
48th Annual Symposium on Foundations of Computer Science
(FOCS), 2007, pp 549-558.
- Homin Lee, Rocco Servedio and Andrew Wan.
DNF are Teachable in the Average Case.
Machine Learning, 69(2-3):79-96, 2007.
Preliminary version in Nineteenth Annual Conference on Computational Learning Theory (COLT), 2006, pp. 214--228.
Mark Fulk Best Student Paper award, COLT 2006.
- Ariel Elbaz, Homin Lee, Rocco Servedio and Andrew Wan.
Separating Models of Learning from Correlated and Uncorrelated Data.
Journal of Machine Learning Research, 8(Feb):277-290, 2007.
Preliminary version in
Eighteenth Annual Conference on Computational Learning Theory (COLT),
2005, pp. 637-651.
- Rocco Servedio and Andrew Wan.
Computing Sparse Permanents Faster.
Information Processing Letters 96(5), 2005, pp. 89-92.
- Learning, Cryptography and the Average Case
Ph.D. Thesis, Columbia University, May 2010.
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