Inductive principles of the search for empirical dependences (methods based on weak convergence of probability measures)

Characterizations of learnability for classes of {O, …, n}-valued functions

COLT '92 Proceedings of the fifth annual workshop on Computational learning theory

Scale-sensitive dimensions, uniform convergence, and learnability

Journal of the ACM (JACM)

A PAC analysis of a Bayesian estimator

COLT '97 Proceedings of the tenth annual conference on Computational learning theory

Improved bounds on the sample complexity of learning

SODA '00 Proceedings of the eleventh annual ACM-SIAM symposium on Discrete algorithms

Using the Pseudo-Dimension to Analyze Approximation Algorithms for Integer Programming

WADS '01 Proceedings of the 7th International Workshop on Algorithms and Data Structures

Boosting and Microarray Data

Machine Learning

Small size quantum automata recognizing some regular languages

Theoretical Computer Science - The art of theory

Myths and legends in learning classification rules

AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 2

Universal ε-approximators for integrals

SODA '10 Proceedings of the twenty-first annual ACM-SIAM symposium on Discrete Algorithms

Comparing distributions and shapes using the kernel distance

Proceedings of the twenty-seventh annual symposium on Computational geometry

Information, Divergence and Risk for Binary Experiments

The Journal of Machine Learning Research

Data reduction for weighted and outlier-resistant clustering

Proceedings of the twenty-third annual ACM-SIAM symposium on Discrete Algorithms