Research Interests
Computational learning theory, Machine learning, Algorithms, Complexity theory, Discrete mathematics
Publications
- A general framework for approximating min-sum ordering problems.
F. Happach, L. Hellerstein, and T. Lidbetter.
INFORMS Journal on Computing 34(3):1437-1452, 2022. - The Stochastic Score Classification Problem.
D. Gkenosis, N. Grammel, L. Hellerstein, and D. Kletenik.
Proceedings of the 26th Annual European Symposium on Algorithms (ESA), 2018. - Approximation Algorithms for Stochastic Boolean Function Evaluation and Stochastic Submodular Set Cover.
A. Deshpande, L. Hellerstein, and D. Kletenik.
Proceedings of the 25th ACM-SIAM Symposium on Discrete Algorithms (SODA), 2014. - Tight bounds on proper equivalence query learning of DNF.
L. Hellerstein, D. Kletenik, L. Sellie, and R.A. Servedio.
Proceedings of the 25th Conference on Learning Theory (COLT), 2012. - Algorithms for distributional and adversarial pipelined filter ordering problems.
A. Condon, A. Deshpande, L. Hellerstein, and N. Wu.
ACM Transactions on Algorithms, 5(2):1--34, 2009. - Minimizing disjunctive normal form formulas and AC0 circuits given a truth table.
E. Allender, L. Hellerstein, P. McCabe, T. Pitassi, and M.E. Saks.
SIAM Journal on Computing, 38(1):63--84, 2008. - On the power of finite automata with both nondeterministic and probabilistic states.
A. Condon, L. Hellerstein, S. Pottle, and A. Wigderson.
SIAM Journal on Computing 27(3): 739-762, 1998. - How many queries are needed to learn.
L. Hellerstein, K. Pillaipakkamnatt, V. Raghavan, and D. Wilkins.
JACM 43(5):840-862, 1996. - Coding techniques for handling failures in large disk arrays.
L. Hellerstein, G. Gibson, R.M. Karp, R.H. Katz, and D.A. Patterson.
Algorithmica, 12:182-208, 1994. - More complete list, with links: See journal papers and book articles and conference papers.