Expanders, randomness, or time versus space
Proc. of the conference on Structure in complexity theory
Random number generation and quasi-Monte Carlo methods
Random number generation and quasi-Monte Carlo methods
Algorithms for random generation and counting: a Markov chain approach
Algorithms for random generation and counting: a Markov chain approach
A class of convex programs with applications to computational geometry
SCG '92 Proceedings of the eighth annual symposium on Computational geometry
Derandomization in computational geometry
Journal of Algorithms
On linear-time deterministic algorithms for optimization problems in fixed dimension
Journal of Algorithms
Communication complexity
A Spectral Approach to Lower Bounds with Applications to Geometric Searching
SIAM Journal on Computing
Efficient approximation of product distributions
Random Structures & Algorithms
Linear Programming in Linear Time When the Dimension Is Fixed
Journal of the ACM (JACM)
Pseudorandomness and Cryptographic Applications
Pseudorandomness and Cryptographic Applications
SFCS '89 Proceedings of the 30th Annual Symposium on Foundations of Computer Science
Approximate Halfspace Range Counting
SIAM Journal on Computing
ML-DS: a novel deterministic sampling algorithm for association rules mining
ICDM'12 Proceedings of the 12th Industrial conference on Advances in Data Mining: applications and theoretical aspects
| Hi-index | 0.03 |
Discrepancy theory is the study of irregularities of distributions. A typical question is: given a "complicated" distribution, find a "simple" one that approximates it well. As it turns out, many questions in complexity theory can be reduced to problems of that type. This raises the possibility that the deep mathematical techniques of discrepancy theory might be of utility to theoretical computer scientists. As will be discussed in this talk this is, indeed, the case. We will give several examples of breakthroughs derived through the application of the "discrepancy method."