Instruct: Given a paper title, retrieve paper titles that are cited by the given paper
Query: On Bias, Variance, 0/1—Loss, and the Curse-of-Dimensionality
Neural Networks and the Bias/Variance Dilemma
Lessons from the Rademacher complexity for deep learning
Transductive rademacher complexity and its applications
Modeling the Loss Distribution
A General Lower Bound on the Number of Examples Needed for Learning
Reduced Support Vector Machines: A Statistical Theory
Bounds on Error Expectation for Support Vector Machines
Quadratic Upper Bound for Recursive Teaching Dimension of Finite VC Classes
Online regularized learning with pairwise loss functions
Bayesian Averaging of Classifiers and the Overfitting Problem
Bagging by design (on the suboptimality of bagging)
Learning Gradient Descent: Better Generalization and Longer Horizons
Statistical behavior and consistency of classification methods based on convex risk minimization
Computational limitations on learning from examples
Why and When Can Deep -- but Not Shallow -- Networks Avoid the Curse of Dimensionality: a Review
Latent Hinge-Minimax Risk Minimization for Inference from a Small Number of Training Samples
an overview of statistical learning theory .
Bias in error estimation when using cross-validation for model selection
Reconstruction of training samples from loss functions
On Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation
Software framework for a humanoid robotic arm
Complexity and diversity of eyes in Early Cambrian ecosystems
STOCHASTIC AGE-FREQUENCY ESTIMATION USING THE VON BERTALANFFY GROWTH EQUATION
Pattern classification and scene analysis
Werdy: Recognition and Disambiguation of Verbs and Verb Phrases with Syntactic and Semantic Pruning