We propose a formulation of the Decision Tree learning algorithm in the Compression settings and derive tight generalization error bounds. In particular, we propose Sample Compres...
We show a tight lower bound of Ω(N log log N) on the number of transmissions required to compute several functions (including the parity function and the majority function) in a...
In this work, we present a new bottom-up algorithmfor decision tree pruning that is very e cient requiring only a single pass through the given tree, and prove a strong performanc...
Decision tree construction is a well studied problem in data mining. Recently, there has been much interest in mining streaming data. Domingos and Hulten have presented a one-pass...
We describe and explore a new perspective on the sample complexity of active learning. In many situations where it was generally believed that active learning does not help, we sh...
Maria-Florina Balcan, Steve Hanneke, Jennifer Wort...