Instruct: Given a paper title, retrieve paper titles that are cited by the given paper.
Query: Learning linear threshold functions in the presence of classification noise
Learning from noisy examples
Learnability and the Vapnik-Chervonenkis dimension
Multiclass learning, boosting, and error-correcting codes
Submodular Optimization under Noise
Online regularized learning with pairwise loss functions
Boosting and naive Bayesian learning
A robust minimax approach to classification
Computational learning theory
Robust and Proper Learning for Mixtures of Gaussians via Systems of Polynomial Inequalities
Toward Robustness against Label Noise in Training Deep Discriminative Neural Networks
Pathologies in information bottleneck for deterministic supervised learning.
Constructing Decision Trees in Noisy Domains.
Recent Advances of Large-Scale Linear Classification
Introduction to Statistical Learning Theory
Learning quickly when irrelevant attributes abound: A new linear-threshold algorithm
Learning from massive noisy labeled data for image classification
Learning with Annotation Noise
Local Rademacher Complexity for Multi-Label Learning
Measuring the VC-Dimension of a Learning Machine
On the Learnability and Design of Output Codes for Multiclass Problems
An Augmented Lagrangian Approach to the Constrained Optimization Formulation of Imaging Inverse Problems
Foam Rolling as a Recovery Tool after an Intense Bout of Physical Activity
Time and sample efficient discovery of Markov blankets and direct causal relations
classifying learnable geometric concepts with the vapnik - chervonenkis dimension ( extended abstract ) .
CondenseNet: An Efficient DenseNet Using Learned Group Convolutions