generalization bound

Implicit Bias of Gradient Descent for Wide Two-layer Neural Networks Trained with the Logistic Loss COLT 2020 Improved PAC-Bayesian Bounds for Linear Regression AAAI 2020 Improved Sample Complexities for Deep Neural Networks and Robust Classification via an All-Layer Margin ICLR 2020 Improving generalization by controlling label-noise information in neural network weights ICML 2020 Information-Theoretic Local Minima Characterization and Regularization ICML 2020 In search of robust measures of generalization NeurIPS 2020 Instance-based Generalization in Reinforcement Learning NeurIPS 2020 Learnability with Indirect Supervision Signals NeurIPS 2020 Learning Bounds for Risk-sensitive Learning NeurIPS 2020 Learning from Label Proportions: A Mutual Contamination Framework NeurIPS 2020 Learning Optimal Representations with the Decodable Information Bottleneck NeurIPS 2020 Learning Sparse Neural Networks for Better Generalization IJCAI 2020 Learning under Model Misspecification: Applications to Variational and Ensemble methods NeurIPS 2020 Learning with Feature and Distribution Evolvable Streams ICML 2020 Linear predictor on linearly-generated data with missing values: non consistency and solutions AISTATS 2020 Margins are Insufficient for Explaining Gradient Boosting NeurIPS 2020 Maximum-and-Concatenation Networks ICML 2020 Minimax Classification with 0-1 Loss and Performance Guarantees NeurIPS 2020 Near-Tight Margin-Based Generalization Bounds for Support Vector Machines ICML 2020 Neural Complexity Measures NeurIPS 2020 Normalized Flat Minima: Exploring Scale Invariant Definition of Flat Minima for Neural Networks Using PAC-Bayesian Analysis ICML 2020 On Convergence and Generalization of Dropout Training NeurIPS 2020 On Generalization Bounds of a Family of Recurrent Neural Networks AISTATS 2020 On the Multiple Descent of Minimum-Norm Interpolants and Restricted Lower Isometry of Kernels COLT 2020 On the Sample Complexity of Adversarial Multi-Source PAC Learning ICML 2020