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Generalization Bounds

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TitleStatusHype
Minnorm training: an algorithm for training over-parameterized deep neural networks—0
Misclassification excess risk bounds for PAC-Bayesian classification via convexified loss—0
Multiaccurate Proxies for Downstream Fairness—0
Multi-class SVMs: From Tighter Data-Dependent Generalization Bounds to Novel Algorithms—0
Multi-distance Support Matrix Machines—0
A Distributionally Robust Optimization Method for Adversarial Multiple Kernel Learning—0
Multi-task and Lifelong Learning of Kernels—0
Multi-Task Classification Hypothesis Space with Improved Generalization Bounds—0
Multi-View Majority Vote Learning Algorithms: Direct Minimization of PAC-Bayesian Bounds—0
Natural Analysts in Adaptive Data Analysis—0
Nearest-Neighbor Sample Compression: Efficiency, Consistency, Infinite Dimensions—0
Near-Tight Margin-Based Generalization Bounds for Support Vector Machines—0
Nested Barycentric Coordinate System as an Explicit Feature Map—0
Neural Abstract Reasoner—0
Neural Drift Estimation for Ergodic Diffusions: Non-parametric Analysis and Numerical Exploration—0
Neural Networks and Polynomial Regression. Demystifying the Overparametrization Phenomena—0
Neural Optimization Kernel: Towards Robust Deep Learning—0
NeurIPS 2020 Competition: Predicting Generalization in Deep Learning—0
Nonasymptotic analysis of Stochastic Gradient Hamiltonian Monte Carlo under local conditions for nonconvex optimization—0
Non-parametric Binary regression in metric spaces with KL loss—0
Non-parametric Group Orthogonal Matching Pursuit for Sparse Learning with Multiple Kernels—0
Nonparametric Hawkes Processes: Online Estimation and Generalization Bounds—0
Non-vacuous Generalization Bounds for Deep Neural Networks without any modification to the trained models—0
Norm-based Generalization Bounds for Compositionally Sparse Neural Networks—0
Nuclear Discrepancy for Active Learning—0
ON BREIMAN’S DILEMMA IN NEURAL NETWORKS: SUCCESS AND FAILURE OF NORMALIZED MARGINS—0
On Certified Generalization in Structured Prediction—0
On Combining Machine Learning with Decision Making—0
On Feature Diversity in Energy-based Models—0
On Generalization and Regularization via Wasserstein Distributionally Robust Optimization—0
On Generalization Bounds for Deep Compound Gaussian Neural Networks—0
On Generalization Bounds for Neural Networks with Low Rank Layers—0
On Generalization Bounds for Projective Clustering—0
On Generalization Bounds of a Family of Recurrent Neural Networks—0
On Generalization Error Bounds of Noisy Gradient Methods for Non-Convex Learning—0
On how to avoid exacerbating spurious correlations when models are overparameterized—0
On Leave-One-Out Conditional Mutual Information For Generalization—0
Online Optimization for Learning to Communicate over Time-Correlated Channels—0
Online-to-PAC Conversions: Generalization Bounds via Regret Analysis—0
Online-to-PAC generalization bounds under graph-mixing dependencies—0
On Localized Discrepancy for Domain Adaptation—0
On Predicting Generalization using GANs—0
On Rademacher Complexity-based Generalization Bounds for Deep Learning—0
On Rank-Dependent Generalisation Error Bounds for Transformers—0
On the accuracy of self-normalized log-linear models—0
On the benefits of output sparsity for multi-label classification—0
On the Discrimination-Generalization Tradeoff in GANs—0
On the Generalization Ability of Online Learning Algorithms for Pairwise Loss Functions—0
On the generalization of bayesian deep nets for multi-class classification—0
On the Geometry of Regularization in Adversarial Training: High-Dimensional Asymptotics and Generalization Bounds—0
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