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Generalization Bounds

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TitleStatusHype
Computing Nonvacuous Generalization Bounds for Deep (Stochastic) Neural Networks with Many More Parameters than Training DataCode0
On the benefits of output sparsity for multi-label classification—0
A Theory of Output-Side Unsupervised Domain Adaptation—0
Data-Dependent Stability of Stochastic Gradient Descent—0
Learning Kernels with Random Features—0
Generalization Bounds for Weighted Automata—0
Kernel Ridge Regression via Partitioning—0
Distribution-dependent concentration inequalities for tighter generalization bounds—0
Estimating individual treatment effect: generalization bounds and algorithmsCode1
Multi-task and Lifelong Learning of Kernels—0
The Structured Weighted Violations Perceptron Algorithm—0
Loss factorization, weakly supervised learning and label noise robustness—0
Dimensionality-Dependent Generalization Bounds for k-Dimensional Coding Schemes—0
Foundations of Coupled Nonlinear Dimensionality Reduction—0
A study of the classification of low-dimensional data with supervised manifold learning—0
Multi-class SVMs: From Tighter Data-Dependent Generalization Bounds to Novel Algorithms—0
On the accuracy of self-normalized log-linear models—0
Random Maxout Features—0
When and why are log-linear models self-normalizing?—0
Deep Learning and the Information Bottleneck PrincipleCode1
Towards a Learning Theory of Cause-Effect InferenceCode0
(Almost) No Label No Cry—0
Task-group Relatedness and Generalization Bounds for Regularized Multi-task Learning—0
Scalable Matrix-valued Kernel Learning for High-dimensional Nonlinear Multivariate Regression and Granger Causality—0
Generalization Bounds for Learning with Linear, Polygonal, Quadratic and Conic Side KnowledgeCode0
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