Communications of the ACM
On the theory of average case complexity
Journal of Computer and System Sciences
Threshold circuits of bounded depth
Journal of Computer and System Sciences
Learning linear threshold functions in the presence of classification noise
COLT '94 Proceedings of the seventh annual conference on Computational learning theory
Weakly learning DNF and characterizing statistical query learning using Fourier analysis
STOC '94 Proceedings of the twenty-sixth annual ACM symposium on Theory of computing
An introduction to computational learning theory
An introduction to computational learning theory
Boosting a weak learning algorithm by majority
Information and Computation
Communication complexity
Specification and simulation of statistical query algorithms for efficiency and noise tolerance
Journal of Computer and System Sciences - Special issue on the eighth annual workshop on computational learning theory, July 5–8, 1995
Efficient noise-tolerant learning from statistical queries
Journal of the ACM (JACM)
Machine Learning
Noise-tolerant learning, the parity problem, and the statistical query model
Journal of the ACM (JACM)
Learning with Queries Corrupted by Classification Noise
ISTCS '97 Proceedings of the Fifth Israel Symposium on the Theory of Computing Systems (ISTCS '97)
On using extended statistical queries to avoid membership queries
The Journal of Machine Learning Research
A simple polynomial-time rescaling algorithm for solving linear programs
STOC '04 Proceedings of the thirty-sixth annual ACM symposium on Theory of computing
New lower bounds for statistical query learning
Journal of Computer and System Sciences - Special issue on COLT 2002
On Computation and Communication with Small Bias
CCC '07 Proceedings of the Twenty-Second Annual IEEE Conference on Computational Complexity
CCC '07 Proceedings of the Twenty-Second Annual IEEE Conference on Computational Complexity
Attribute-Efficient and Non-adaptive Learning of Parities and DNF Expressions
The Journal of Machine Learning Research
Improved lower bounds for learning intersections of halfspaces
COLT'06 Proceedings of the 19th annual conference on Learning Theory
MFCS'07 Proceedings of the 32nd international conference on Mathematical Foundations of Computer Science
Journal of the ACM (JACM)
Fixed-parameter evolutionary algorithms and the vertex cover problem
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
On evolvability: the swapping algorithm, product distributions, and covariance
SAGA'09 Proceedings of the 5th international conference on Stochastic algorithms: foundations and applications
PAC learning and genetic programming
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Distribution free evolvability of polynomial functions over all convex loss functions
Proceedings of the 3rd Innovations in Theoretical Computer Science Conference
A complete characterization of statistical query learning with applications to evolvability
Journal of Computer and System Sciences
The max problem revisited: the importance of mutation in genetic programming
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Attribute-efficient evolvability of linear functions
Proceedings of the 5th conference on Innovations in theoretical computer science
| Hi-index | 0.00 |
Valiant has recently introduced a framework for analyzing the capabilities and the limitations of the evolutionary process of random change guided by selection. In his framework the process of acquiring a complex functionality is viewed as a substantially restricted form of PAC learning of an unknown function from a certain set of functions. Valiant showed that classes of functions evolvable in his model are also learnable in the statistical query (SQ) model of Kearns and asked whether the converse is true. We show that evolvability is equivalent to learnability by a restricted form of statistical queries. Based on this equivalence we prove that for any fixed distribution D over the instance space, every class of functions learnable by SQs over D is evolvable over D. Previously, only the evolvability of monotone conjunctions of Boolean variables over the uniform distribution was known. On the other hand, we prove that the answer to Valiant's question is negative when distribution-independent evolvability is considered. To demonstrate this, we develop a technique for proving lower bounds on evolvability and use it to show that decision lists and linear threshold functions are not evolvable in a distribution-independent way. This is in contrast to distribution-independent learnability of decision lists and linear threshold functions in the statistical query model.