Generalization

Neuron with Steady Response Leads to Better Generalization NeurIPS 2022 Offline Reinforcement Learning: Fundamental Barriers for Value Function Approximation COLT 2022 On Predicting Generalization using GANs ICLR 2022 On the Benefits of Large Learning Rates for Kernel Methods COLT 2022 On the Double Descent of Random Features Models Trained with SGD NeurIPS 2022 On the Effective Number of Linear Regions in Shallow Univariate ReLU Networks: Convergence Guarantees and Implicit Bias NeurIPS 2022 On the generalization of learning algorithms that do not converge NeurIPS 2022 On the Importance of Gradient Norm in PAC-Bayesian Bounds NeurIPS 2022 On the Strong Correlation Between Model Invariance and Generalization NeurIPS 2022 Open Problem: Better Differentially Private Learning Algorithms with Margin Guarantees COLT 2022 Optimal Binary Classification Beyond Accuracy NeurIPS 2022 Orthogonal Statistical Learning with Self-Concordant Loss COLT 2022 Overcoming a Theoretical Limitation of Self-Attention ACL 2022 PAC-Bayes Compression Bounds So Tight That They Can Explain Generalization NeurIPS 2022 Parameters or Privacy: A Provable Tradeoff Between Overparameterization and Membership Inference NeurIPS 2022 Rate-Distortion Theoretic Generalization Bounds for Stochastic Learning Algorithms COLT 2022 Realizable Learning is All You Need COLT 2022 Reducing Flipping Errors in Deep Neural Networks AAAI 2022 Reflash Dropout in Image Super-Resolution CVPR 2022 Rethinking Value Function Learning for Generalization in Reinforcement Learning NeurIPS 2022 Revisit Systematic Generalization via Meaningful Learning EMNLP 2022 Risk bounds for aggregated shallow neural networks using Gaussian priors COLT 2022 Risk Bounds of Multi-Pass SGD for Least Squares in the Interpolation Regime NeurIPS 2022 Robustness in deep learning: The good (width), the bad (depth), and the ugly (initialization) NeurIPS 2022 SizeShiftReg: a Regularization Method for Improving Size-Generalization in Graph Neural Networks NeurIPS 2022