Computational Learning Theory
Tight Bounds for Collaborative PAC Learning via Multiplicative Weights
Jiecao Chen, Qin Zhang, Yuan Zhou
Neural Information Processing Systems http://nips.cc/
Information Theoretic Lower Bounds for Information Theoretic Upper Bounds Roi Livni Department Electrical Engineering Tel Aviv University rlivni@tauex.tau.ac.il
But nonetheless, they tend to perform well on unseen test data.
Graph-based Discriminators: Sample Complexity and Expressiveness
Abasic question inlearning theory istoidentify iftwodistributions areidentical when we have access only to examples sampled from the distributions.
Active Classification with Few Queries under Misspecification
Our main algorithmic result is the first query-efficient algorithm for learning halfspaces under the popular Massart noise model.