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Generalization Bounds

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TitleStatusHype
Stability and Generalization of Graph Convolutional Neural Networks—0
ON BREIMAN’S DILEMMA IN NEURAL NETWORKS: SUCCESS AND FAILURE OF NORMALIZED MARGINS—0
On Tighter Generalization Bounds for Deep Neural Networks: CNNs, ResNets, and Beyond—0
Deep multi-Wasserstein unsupervised domain adaptation—0
Stability and Optimization Error of Stochastic Gradient Descent for Pairwise Learning—0
PLUME: Polyhedral Learning Using Mixture of Experts—0
A Generalization Bound for Online Variational Inference—0
An Exponential Efron-Stein Inequality for Lq Stable Learning Rules—0
Support and Invertibility in Domain-Invariant Representations—0
High probability generalization bounds for uniformly stable algorithms with nearly optimal rate—0
Provable Guarantees for Gradient-Based Meta-LearningCode0
A Distributionally Robust Optimization Method for Adversarial Multiple Kernel Learning—0
Uniform convergence may be unable to explain generalization in deep learningCode0
Generalization Error Bounds of Gradient Descent for Learning Over-parameterized Deep ReLU Networks—0
Generalization Bounds For Unsupervised and Semi-Supervised Learning With Autoencoders—0
Can SGD Learn Recurrent Neural Networks with Provable Generalization?—0
Adversarial Networks and Autoencoders: The Primal-Dual Relationship and Generalization Bounds—0
On Generalization Error Bounds of Noisy Gradient Methods for Non-Convex Learning—0
Supervised classification via minimax probabilistic transformations—0
Natural Analysts in Adaptive Data Analysis—0
A New Perspective on Machine Learning: How to do Perfect Supervised Learning—0
Random deep neural networks are biased towards simple functionsCode0
Generalization Bounds for Uniformly Stable Algorithms—0
Learning finite-dimensional coding schemes with nonlinear reconstruction maps—0
Stochastic Composite Mirror Descent: Optimal Bounds with High Probabilities—0
Support Recovery for Orthogonal Matching Pursuit: Upper and Lower bounds—0
Adversarial Multiple Source Domain Adaptation—0
The Structure of Optimal Private Tests for Simple Hypotheses—0
Sorting out Lipschitz function approximationCode0
Generalization Bounds for Vicinal Risk Minimization Principle—0
Pseudo-Bayesian Learning with Kernel Fourier Transform as PriorCode0
Learning Gaussian Processes by Minimizing PAC-Bayesian Generalization BoundsCode0
Rethinking Breiman's Dilemma in Neural Networks: Phase Transitions of Margin DynamicsCode0
Improved Generalization Bounds for Adversarially Robust Learning—0
Rademacher Generalization Bounds for Classifier Chains—0
A Surprising Linear Relationship Predicts Test Performance in Deep NetworksCode0
Escaping the Curse of Dimensionality in Similarity Learning: Efficient Frank-Wolfe Algorithm and Generalization BoundsCode0
Multi-distance Support Matrix Machines—0
The Generalization Error of Dictionary Learning with Moreau Envelopes—0
Dropout Training, Data-dependent Regularization, and Generalization Bounds—0
On Tighter Generalization Bound for Deep Neural Networks: CNNs, ResNets, and Beyond—0
Chaining Mutual Information and Tightening Generalization Bounds—0
Minnorm training: an algorithm for training over-parameterized deep neural networks—0
Learning latent variable structured prediction models with Gaussian perturbations—0
VC-Dimension Based Generalization Bounds for Relational Learning—0
Non-Vacuous Generalization Bounds at the ImageNet Scale: A PAC-Bayesian Compression ApproachCode0
Data-Dependent Coresets for Compressing Neural Networks with Applications to Generalization Bounds—0
Data-dependent PAC-Bayes priors via differential privacy—0
Stronger generalization bounds for deep nets via a compression approach—0
To understand deep learning we need to understand kernel learning—0
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